Executed TTFB probe for glm-4.6
Executed TTFB probe for glm-4.6
Agent
Droid
Model
Custom:claude Sonnet 4.6 (max Sub) 5
Started
May 8, 10:01 PM
Messages60
Downloads0
Prepared baseline tests and system prompt extraction.
SetupDeveloped precise TTFB measurement script.
BuildRan TTFB trials and computed statistics.
VerifyAnalyzed results and confirmed budget failure.
UnderstandWrote and committed the TTFB report.
EditUser system info (darwin 24.6.0)
Model: Claude Sonnet 4.6 (Max sub) Today's date: 2026-05-08 User language: en
% pwd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1
% ls AGENTS.md app CLAUDE.md docs README.md validation worker
% git status -b --porcelain | head -n1 mission-2
% git status --porcelain
% git log --oneline -5 b129593 M2/F4a: investigate disabling reasoning on glm-5.1 a08f741 M2/F5: end-to-end Z.ai latency report (Worker-boundary) daaac7c M2/F3: persona verification report on GLM-5.1 1f85974 M2/F2: Swift OpenAI shape (request body + parser, reasoning/content split) 0b5c6ba M2/F1: swap chat route upstream to Z.ai GLM
% git show-ref --verify --quiet refs/heads/main && echo "Default branch is main" Default branch is main
% cat /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/CLAUDE.md <coding_guidelines>
A native macOS AI teaching companion that meets the learner as a person, sees their screen, remembers them across sessions, and adapts. CTRL's "Allen" applied to learning, without the dystopia.
We are building Milestone 1: the in-session intimacy loop. A user can:
What M1 does not do: cross-session memory retrieval (M2), proactive initiation (M3), tone attunement (M3), cursor overlay (deferred).
The full validation contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
app/): SwiftUI menu-bar app, macOS 14.2+, LSUIElement=true. MVVM with @StateObject / @Published / @MainActor async/await.worker/): TypeScript Cloudflare Worker proxy holding all third-party API keys. Three routes: /transcribe-token (AssemblyAI temp tokens), /chat (Anthropic streaming), /tts (ElevenLabs Flash streaming). All audio paths are transparent passthroughs — no buffering.docs/persona/): the teacher is Mara. System prompt, first-run script, voice spec, and example exchanges are versioned artifacts.docs/memory/): two-layer (structured profile + episodic log). M1 is in-session-only with lightweight persistence on quit; M2 wires cross-session retrieval.CGEvent tap (default ctrl + option).URLSession.For per-pattern verdicts (what we copied / adapted / invented from clicky), see docs/clicky-audit/copy-adapt-invent.md. Workers must follow that doc when deciding how to translate clicky patterns.
The repo lives at:
/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1The path contains spaces. Every shell operation must double-quote paths.
✅ Correct:
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"❌ Wrong (will break):
cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojReviewers reject unquoted shell paths. A build smoke test under this path is part of the validation contract (A-BUILD-UNDER-SPACED-PATH).
open "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.Do NOT run xcodebuild from the terminal for routine dev. It invalidates TCC permissions (Screen Recording, Accessibility, Microphone) and forces the user to re-grant them. The only acceptable xcodebuild use is the F9 build-smoke script — a one-off check.
Known non-blocking warnings (don't try to fix unless asked):
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKENworker/.dev.vars for local — must be in .gitignore (it is).
(Mirrors clicky's AGENTS.md; full version in clicky's repo for reference.)
originalQuestionLastAnsweredDate, not origAnsweredAt.currentCardData, pass it as currentCardData, not card or cardData.NSPanel, NSStatusItem, CGEvent tap).@MainActor for all UI state. async/await throughout.text(), json(), arrayBuffer() forbidden in /chat and /tts).mission-1 for this mission.Fn: (feature ID) when the commit fulfills a feature.os.Logger (Swift) or console.log with a route prefix (TS).xcodebuild for routine dev (TCC invalidation).worker/.dev.vars or any file with API keys.A-VOICE-LATENCY-MEDIAN ≤ 1000 ms is gating. The full contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
The 7 proxies (M1: 1, 2, 4, 5):
When a design choice is not covered, default to "what makes this feel more like a real teacher who knows me."
When you make changes that affect this file's contents, update it. Specifically:
Do NOT update this file for minor edits or bug fixes that don't affect documented architecture or conventions. </coding_guidelines>
% cat /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/AGENTS.md <coding_guidelines>
A native macOS AI teaching companion that meets the learner as a person, sees their screen, remembers them across sessions, and adapts. CTRL's "Allen" applied to learning, without the dystopia.
We are building Milestone 1: the in-session intimacy loop. A user can:
What M1 does not do: cross-session memory retrieval (M2), proactive initiation (M3), tone attunement (M3), cursor overlay (deferred).
The full validation contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
app/): SwiftUI menu-bar app, macOS 14.2+, LSUIElement=true. MVVM with @StateObject / @Published / @MainActor async/await.worker/): TypeScript Cloudflare Worker proxy holding all third-party API keys. Three routes: /transcribe-token (AssemblyAI temp tokens), /chat (Anthropic streaming), /tts (ElevenLabs Flash streaming). All audio paths are transparent passthroughs — no buffering.docs/persona/): the teacher is Mara. System prompt, first-run script, voice spec, and example exchanges are versioned artifacts.docs/memory/): two-layer (structured profile + episodic log). M1 is in-session-only with lightweight persistence on quit; M2 wires cross-session retrieval.CGEvent tap (default ctrl + option).URLSession.For per-pattern verdicts (what we copied / adapted / invented from clicky), see docs/clicky-audit/copy-adapt-invent.md. Workers must follow that doc when deciding how to translate clicky patterns.
The repo lives at:
/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1The path contains spaces. Every shell operation must double-quote paths.
✅ Correct:
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"❌ Wrong (will break):
cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojReviewers reject unquoted shell paths. A build smoke test under this path is part of the validation contract (A-BUILD-UNDER-SPACED-PATH).
open "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.Do NOT run xcodebuild from the terminal for routine dev. It invalidates TCC permissions (Screen Recording, Accessibility, Microphone) and forces the user to re-grant them. The only acceptable xcodebuild use is the F9 build-smoke script — a one-off check.
Known non-blocking warnings (don't try to fix unless asked):
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKENworker/.dev.vars for local — must be in .gitignore (it is).
(Mirrors clicky's AGENTS.md; full version in clicky's repo for reference.)
originalQuestionLastAnsweredDate, not origAnsweredAt.currentCardData, pass it as currentCardData, not card or cardData.NSPanel, NSStatusItem, CGEvent tap).@MainActor for all UI state. async/await throughout.text(), json(), arrayBuffer() forbidden in /chat and /tts).mission-1 for this mission.Fn: (feature ID) when the commit fulfills a feature.os.Logger (Swift) or console.log with a route prefix (TS).xcodebuild for routine dev (TCC invalidation).worker/.dev.vars or any file with API keys.A-VOICE-LATENCY-MEDIAN ≤ 1000 ms is gating. The full contract lives at:
/Users/gaganarora/.factory/missions/f1055694-86d2-4d58-b62a-7e42750bf166/validation-contract.md
The 7 proxies (M1: 1, 2, 4, 5):
When a design choice is not covered, default to "what makes this feel more like a real teacher who knows me."
When you make changes that affect this file's contents, update it. Specifically:
Do NOT update this file for minor edits or bug fixes that don't affect documented architecture or conventions. </coding_guidelines>
IMPORTANT:
System Reminder
You are a worker assigned to execute feature "F4b-glm-4.6-ttfb-probe".
## Worker Session
Your worker session id is: 45318e48-94fd-4553-b713-a0c87ed8f1aa
If you need browser automation during this mission, use agent-browser.
## agent-browser Rules:
- Never use the "default" session.
- Always pass --session.
- Single browser: --session "45318e4894fd"
- Multi-browser (realtime / multi-user): --session "45318e4894fd__u1", "45318e4894fd__u2", etc.
- Before EndFeatureRun (even on errors), close every session you opened:
- agent-browser --session "<session>" close
Debug: agent-browser session list
## Mission Files
The following files are in /Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65:
- mission.md
- validation-contract.md
- validation-state.json
- features.json (jq '.features[:5] | map({id, description, status, milestone, skillName})' features.json)
- AGENTS.md
- services.yaml
- init.sh
- library/
If your feature has `fulfills` (assertion IDs), read those assertions from `validation-contract.md`. They specify the exact behavior your implementation must satisfy — use them to guide your work.
**PERFORMANCE TIP:** Parallelize your startup by reading all context files and invoking your skills in a single tool call batch. These reads are independent and can run simultaneously.
## Your Task
1. First, invoke the 'mission-worker-base' skill for startup procedures
2. Then, invoke the 'live-smoke-worker' skill to complete your assigned feature
3. Call EndFeatureRun when done
REMEMBER TO CALL ENDFEATURERUN WHEN YOU ARE DONE.Skill "mission-worker-base" is now active.
<skill name="mission-worker-base" filePath="builtin:mission-worker-base">
# Worker Base Procedures
You are a worker in a multi-agent mission. This skill defines the procedures that ALL workers must follow. After completing startup, you'll invoke your specific worker skill for the actual work procedure.
## Your Assigned Feature
Your feature has been pre-assigned by the system and is shown in your bootstrap message. The feature includes:
- `id` - Feature identifier
- `description` - What to build
- `skillName` - The skill you must invoke for the work procedure
- `expectedBehavior` - What success looks like
- `verificationSteps` - How to verify your work
- `fulfills` - Validation contract assertion IDs (if present)
**Your feature's `fulfills` field lists validation contract assertions that must be true after your work.** Read these assertions carefully before starting — they define what "done" means for your feature. Before completing, ensure that each assertion would pass. If you realize an assertion cannot be fulfilled given your current scope, flag it in your handoff.
**Explicit technology choices are binding.** If the user or orchestrator specified a package, library, SDK, or tool for this mission or feature, you must use that exact choice. Do not swap in an alternative because it seems easier, is already installed, or avoids an allowlist problem. If the specified dependency is unavailable or blocked, return to the orchestrator instead of substituting.
## Service Management via Manifest
`services.yaml` is the **single source of truth** for all commands and services.
**Using the manifest:**
- Read it to find commands/services
- For services: use `start`, `stop`, `healthcheck` commands exactly as declared
- For commands: use named commands (e.g., `commands.test`)
**Starting services:**
1. Check `depends_on` and start dependencies first
2. Run the `start` command from the manifest
3. Wait for `healthcheck` to pass (retry a few times with backoff)
4. If healthcheck fails to succeed within a reasonable timeframe → return to orchestrator immediately with a report.
**Stopping services:**
- Use the manifest's `stop` command (which uses the declared port)
- Port-based kills are ALLOWED when using the manifest's declared port
**If manifest is broken:** Return to orchestrator with `returnToOrchestrator: true` - don't try to fix it yourself.
## CRITICAL: Never Kill User Processes
**FORBIDDEN commands:**
- `pkill node`, `killall`, `kill` by process name
- Port-based kills on ports NOT declared in `services.yaml`
- Any command that kills processes you didn't start
**ALLOWED:**
- Port-based kills using the manifest's declared `stop` command (these use declared ports)
- Killing processes by PID that YOU started in this session
Port conflict on a port NOT in the manifest? Return to orchestrator. NEVER kill the existing process.
(CRITICAL) If you discovered reusable services or commands that future workers will need, ADD them to `services.yaml`. See Phase 3.3 for details.
## Phase 1: Startup
### 1.1 Read Context
**PERFORMANCE TIP:** Parallelize your startup by reading all context files in a single tool call batch. The files below are independent and can be read simultaneously along with invoking your worker skill. This significantly reduces startup time.
Read these to understand the mission state:
- `mission.md` - The accepted mission proposal representing the full scope and strategy agreed upon between orchestrator and user
- `AGENTS.md` - Guidance from the orchestrator and user. **Includes Mission Boundaries (port ranges, external services, off-limits resources) that you must NEVER violate.** May be updated mid-run with new user instructions - always check for latest guidance.
- If your feature has `fulfills`, read those specific assertions from `validation-contract.md` — they define the exact behavior your implementation must satisfy.
- `services.yaml` - How to run commands and services (single source of truth for operations)
- `features.json` - Feature list (`jq '.features[:5] | map({id, description, status, milestone, skillName})' features.json`)
- `git log --oneline -20` - Recent commit history to see what's been done
Also available for reference:
- `library/architecture.md` - The system's architecture: components, interactions, data flows, invariants. Read this to understand how your feature fits into the larger system.
- `library/` - Other knowledge base files written by previous workers (organized by topic)
(CRITICAL) The following documents are critical:
- `AGENTS.md`:
- **Includes Mission Boundaries (port ranges, external services, off-limits resources) that you must NEVER violate.**
- This may be updated mid-mission with new user instructions - always check for latest guidance.
- `services.yaml`:
- **Single source of truth for all commands and services.** Do not start services any other way. If an entry is broken, return to orchestrator.
Ignoring these could be catastrophic for the mission's result. **Violating mission boundaries could damage the user's system or other projects.**
### 1.2 Initialize Environment
1. Run `init.sh` if it exists (one-time setup, idempotent)
### 1.3 Baseline Validation
Run `commands.test` from `services.yaml`. This verifies the mission is in a healthy state before you start.
**CRITICAL: Do NOT pipe validator output through `| tail`, `| head`, or similar.** Pipes can mask failing exit codes — if a test fails but you pipe through `tail`, the exit code becomes 0 (tail's exit code) and you'll incorrectly report tests as passing. Run validators directly and capture their actual exit code. If output is too noisy, prefer narrower test selection (e.g., `--testPathPattern`) over output truncation.
If baseline fails:
- Call EndFeatureRun with `returnToOrchestrator: true` and explain the broken baseline
### 1.4 Understand Your Feature's Context
Your feature is has been assigned to you in the user message. View all features in your feature's milestone to understand the full context:
```bash
jq --arg m "YOUR_MILESTONE" '.features | map(select(.milestone == $m)) | map({id, description, status})' {missionDir}/features.json
```
Replace `YOUR_MILESTONE` with the actual milestone name from your assigned feature. This shows all features (any status) in the milestone so you understand what's been done, what's in progress, and what's pending.
### 1.5 Check Library
You have access to `library/`, which contains knowledge from previous workers. The library is organized by topic. It may include guidance or docs for specific technologies you will be using. Refer to these for technology-specific idiomatic patterns, SDK usage, and anti-patterns.
### 1.6 Online Research (Conditional)
If your feature involves a technology, SDK, or integration where you're not confident about the correct idiomatic patterns — and `library/` doesn't already cover it — do a online lookup (WebSearch/FetchUrl) to verify the correct usage before implementing.
### 1.7 Start Services
Start any services you'll need from `services.yaml`:
- Check `depends_on` and start dependencies first
- Run each service's `start` command
- Wait for `healthcheck` to pass before proceeding
- If ANY service fails to start or healthcheck fails → return to orchestrator immediately
---
## Code Quality Principles
These are non-negotiable. Apply them throughout your work:
- **Avoid god files** - If a file is growing large, split it into focused modules
- **Create reusable components** - Don't duplicate code; extract and reuse
- **Keep changes focused** - Don't sprawl across unrelated areas
- **Stay in scope** - Clearly unrelated issues (e.g., flaky tests for other features, non-trivial bugs in unrelated code) should be noted in `discoveredIssues` with severity `non_blocking` and a description prefixed with "Pre-existing:" but don't go off-track to fix them. Check `{missionDir}/AGENTS.md` for "Known Pre-Existing Issues" to avoid re-reporting.
---
## Phase 2: Work (Defined by Your Specific Skill)
After completing startup, invoke the skill specified in your feature's `skillName` field.
**If the skill does not exist** (i.e., the Skill tool returns an error), do not proceed with the work. Instead, return to the orchestrator immediately by calling EndFeatureRun with `returnToOrchestrator: true` and explain that the specified skill does not exist.
That skill will guide you through the actual work procedure.
---
## Phase 3: Cleanup & Handoff
After completing the work procedure, you MUST clean up and report.
### 3.1 Final Validation
Before cleanup, all validators from `services.yaml` — test, typecheck, lint, etc - should pass. Fix any failures your work introduced. Do not hand off with broken validators.
### 3.2 Environment Cleanup
Before calling EndFeatureRun, stop all services you started:
1. **Stop services using manifest commands**: For each service you started, run its `stop` command from `services.yaml`
2. **Stop any other processes YOU started**: By their specific PID (not by port or name)
3. **Ensure clean git status in repos you changed**: Commit or stash repository changes. MissionDir artifact-only changes do not need commits.
The manifest's `stop` commands use declared ports, so port-based kills are safe for those. Do NOT kill processes on ports not declared in the manifest.
### 3.3 Add Any Services/Commands Discovered to the Manifest
If you discovered reusable services or commands that future workers will need, ADD them to `services.yaml`.
**Updating the manifest:**
If you discover a new service or command that future workers will need, you may add it to `services.yaml`:
1. **If service uses a port**: the port MUST be hardcoded in ALL commands (`start`, `stop`, `healthcheck`) AND in the `port` field
2. **Add the service/command** with required fields:
- For services: `start`, `stop`, `healthcheck` (port hardcoded in command string), `port` (for conflict detection - not auto-injected), `depends_on`
- For commands: just the command string
Example - adding a new service:
```yaml
services:
# ... existing services ...
storybook:
start: PORT=6006 npm run storybook
stop: lsof -ti :6006 | xargs kill
healthcheck: curl -sf http://localhost:6006
port: 6006
depends_on: []
```
### 3.4 Call EndFeatureRun
Report your results. Your specific worker skill defines what a thorough handoff looks like - follow its Example Handoff.
```
EndFeatureRun({
successState: "success" | "failure",
returnToOrchestrator: boolean,
commitId: "...", // include when repository code changed
repoPath: "/path/to/repo", // include with commitId
validatorsPassed: boolean, // required true if success
handoff: {
salientSummary: "...", // 1–4 sentences
whatWasImplemented: "...",
whatWasLeftUndone: "", // empty if truly complete
verification: {
commandsRun: [{ command, exitCode, observation }],
interactiveChecks: [{ action, observed }] // for UI/browser work
},
tests: {
added: [{ file, cases: [{ name, description }] }],
coverage: "..."
},
discoveredIssues: [{ severity, description, suggestedFix? }],
skillFeedback: {
followedProcedure: true, // or false if you deviated
deviations: [], // details if followedProcedure is false
suggestedChanges: [] // optional improvements
}
}
})
```
#### Verification Hygiene
When running validators or tests during your work:
- **Do NOT pipe output through `| tail`, `| head`, or similar** — pipes mask the real exit code. If a test fails but you pipe through `tail`, the shell reports `tail`'s exit code (0), hiding the failure.
- **Prefer narrower test selection over output truncation.** If output is too noisy, run a more targeted test pattern (e.g., `npm test -- --testPathPattern MyFile`) instead of piping through `head`/`tail`.
#### Skill Feedback (help improve future workers)
Before calling EndFeatureRun, reflect on whether you followed your skill's procedure:
- **Did you follow the procedure as written?** If yes, set `followedProcedure: true` and leave `deviations` empty.
- **Did you deviate?** If you did something differently than the skill instructed, record it:
- `step`: Which step (e.g., "1.3 Baseline Validation", "Run tests before commit")
- `whatIDidInstead`: What you actually did
- `why`: Why you deviated (skill was unclear, found a better approach, blocked by environment, etc.)
This feedback helps the orchestrator improve skills for future milestones. Be honest -- deviations aren't failures, they're data.
#### When to Return to Orchestrator
Set `returnToOrchestrator: true` when:
- **Cannot complete work within mission boundaries** - if the feature requires violating boundaries (port range, off-limits resources), return immediately. NEVER violate boundaries.
- **Service won't start or healthcheck fails** - manifest may be broken or external dependency missing
- **Dependency or service that SHOULD exist is inaccessible** - if something that was working before (database, API, external service, file, etc.) is no longer accessible and you cannot figure out how to restore it after investigation, return immediately. Do not spin endlessly trying to fix infrastructure issues you can't resolve.
- Blocked by missing dependency, unsatisfied preconditions, or unclear requirements
- Previous worker left broken state you can't fix
- Decision or input needed from human/orchestrator
- Your skill type requires it.
**CRITICAL: After calling EndFeatureRun, you MUST end your turn immediately. Do not continue with additional work, do not start another feature, do not make any further tool calls. Your session is complete once you call EndFeatureRun.**
</skill>Skill "live-smoke-worker" is now active.
<skill name="live-smoke-worker" filePath="/Users/gaganarora/.factory/missions/dc819efb-a77f-431b-ab96-1fabff0e7a65/skills/live-smoke-worker/SKILL.md">
# live-smoke-worker
NOTE: Startup and cleanup are handled by `mission-worker-base`. This skill defines the procedure for features that run live HTTP calls through the Worker → Z.ai and produce artifact reports (no app-side code changes).
## When to Use This Skill
Features whose primary deliverable is a written report or set of captured artifacts produced by running real requests through `wrangler dev` against Z.ai.
In M2:
- **F3**: Persona verification eval on GLM-5.1. Output: `docs/persona/glm-5.1-verification.md` + `app/teachr-tests-fixtures/persona-drift-probes.json`. NO source-code changes.
- **F5**: Worker-boundary latency capture against Z.ai. Output: `validation/M2-latency-report.md` + a results JSON via the existing harness. Source-code change limited to `validation/latency-harness/run-latency-harness.sh`.
## Required Skills, Tools, and Dependencies
- `wrangler dev --local` (Worker on `localhost:8787`)
- `curl` (live calls to the Worker)
- `jq` (JSON manipulation)
- `bash` (orchestrating the runs)
- `git` for commit hygiene
- Read access to `library/architecture.md`, `library/environment.md`, `library/user-testing.md`, repo + mission `AGENTS.md`, `docs/persona/system-prompt.md` (READ ONLY), `docs/persona/example-exchanges.md`, `docs/persona/voice-spec.md`, `docs/persona/first-run-script.md`, `validation/latency-harness/seeded-turns.json`
External services: Z.ai Codeplan endpoint at `https://api.z.ai/api/coding/paas/v4/chat/completions` via the Worker. Requires `ZAI_API_KEY` in `worker/.dev.vars`.
**Workers MUST NOT log, echo, or include the `ZAI_API_KEY` value anywhere — handoff, commit, console, or report.**
## Work Procedure
### Step 0 — Read shared state
1. Read mission `AGENTS.md` (off-limits, TCC, secrets).
2. Read repo `AGENTS.md` (path-hazard rule).
3. Read `library/architecture.md` and `library/environment.md`.
4. Read `library/user-testing.md` (the per-feature recipe for your feature).
5. Read your assigned feature's `description`, `preconditions`, `expectedBehavior`, `verificationSteps`, and `fulfills` array.
### Step 1 — Verify the live preconditions
1. Confirm branch is `mission-2`.
2. Confirm `worker/.dev.vars` exists and contains a non-empty `ZAI_API_KEY` (without echoing the value):
```bash
if ! grep -q '^ZAI_API_KEY=[REDACTED] "$REPO/worker/.dev.vars"; then
echo "ERROR: ZAI_API_KEY missing or empty in worker/.dev.vars"
# return to orchestrator with clear blocker
fi
```
3. For F3: confirm F1 and F2 are committed (read `git log --oneline -5`); the commit messages should include `M2/F1` and `M2/F2`. F3 cannot run before F2 lands.
4. For F5: confirm F1 is committed.
5. Start `wrangler dev` in the background:
```bash
cd "$REPO/worker"
npx wrangler dev --local --port 8787 > "/tmp/wrangler-${FEATURE_ID}.log" 2>&1 &
WRANGLER_PID=$!
sleep 4 # let wrangler boot
```
6. Health check the Worker with a 401 probe (auth without a body):
```bash
curl -sf -o /dev/null -w "%{http_code}\n" --max-time 5 \
-X POST -H "Authorization: Bearer wrong" http://localhost:8787/chat
# expect: 401
```
If the Worker isn't up, abort and return to orchestrator.
### Step 2 — Run the feature
#### For F3 (persona verification)
1. Author `app/teachr-tests-fixtures/persona-drift-probes.json` with 5 probes per the proposal:
- `anti-citation-callback` (1 setup turn + 1 callback turn; pass = woven, not cited)
- `chatbot-ism-opener` (1 first-message turn; pass = CTRL-shaped, no chatbot tropes)
- `frustration-tone-modulation` (1 frustration turn; pass = warmer/slower, persona intact)
- `identity-persistence-turn-8` (8 setup turns + 1 identity probe; pass = Mara identity intact)
- `specificity-vs-generic-teacher` (1 specific-context turn + 1 general question; pass = response references prior context)
Schema:
```json
{
"version": "1.0.0",
"probes": [
{
"id": "anti-citation-callback",
"name": "Anti-citation callback",
"rationale": "...",
"rubric": { "pass": "...", "fail": "..." },
"turns": [
{ "role": "user", "content": "..." },
{ "role": "assistant_expected_pattern": "..." }
]
}
// ... 5 total
]
}
```
**Shell-quoting note:** When constructing JSON payloads via `jq -n --arg`, take care with strings that may contain apostrophes or single quotes. Prefer `jq -n --rawfile` from a temp file when the persona prompt or transcript contains awkward characters; otherwise verify with `jq -c .` that the shell-quoted version produces valid JSON before piping to curl.
2. Capture the breadth pass (12 turns from `validation/latency-harness/seeded-turns.json`):
- For each turn, build an OpenAI Chat Completions request matching the Swift app's wire format (system prompt at messages[0], turn as user message; for turns >1, include prior assistant responses as conversation history).
- POST to `http://localhost:8787/chat` with `Authorization: Bearer dev-only-token-change-in-production` (or whatever `APP_CLIENT_TOKEN` is in `.dev.vars`).
- Parse the SSE stream into TWO channels: `delta.content` (concatenated → final response) and `delta.reasoning_content` (concatenated → reasoning log). Verify they don't bleed into each other.
- Save raw transcripts to `/tmp/m2-persona-breadth-<turn>.json` (do NOT commit these — they're scratch).
3. Capture the depth pass (5 drift probes):
- For each probe, run its turns through the same Worker + parse pipeline.
- Apply the rubric; record pass/fail with a short justification quoting the response.
4. Author `docs/persona/glm-5.1-verification.md`. Required sections:
- **Header** with mission ID, date, model (`glm-5.1`), endpoint
- **Verdict** (one of the three exact strings: `"sufficient for ship"`, `"needs prompt tweaks"`, `"insufficient — escalate"`) — this assertion is gating
- **Breadth pass** — per-turn grade for turns 1–12 with transcript excerpts
- **Depth pass** — per-probe grade with rubric application
- **Reasoning leakage check** — explicit statement that across all captures, no reasoning text appeared in the content channel
- **Recommendations** (only if verdict is `"needs prompt tweaks"`) — fenced code blocks marked `recommendation` containing the proposed delta to `system-prompt.md`. NOT applied to the file. Explicitly note "User approval required before any persona-doc edit."
5. **Do NOT** modify `docs/persona/system-prompt.md` under any circumstance.
#### For F5 (latency capture)
1. Update `validation/latency-harness/run-latency-harness.sh` payload section. Locate the `jq -n …` block that constructs the request (around lines 115–130 in the M1 version). Replace the body construction:
```bash
PAYLOAD=$(jq -n \
--arg transcript "$TRANSCRIPT" \
--arg system "$SYSTEM_PROMPT" \
'{
model: "glm-5.1",
max_tokens: 200,
stream: true,
temperature: 0.7,
messages: [
{ role: "system", content: $system },
{ role: "user", content: $transcript }
]
}')
```
Update any inline comment that says "Anthropic-compatible chat payload" to "OpenAI-compatible Chat Completions payload (Z.ai)".
2. Run the harness:
```bash
bash "$REPO/validation/latency-harness/run-latency-harness.sh"
```
The harness writes a results JSON under `validation/latency-harness/results/run-<ISO>.json`. Capture the run ID and the median + p90 numbers.
3. Author `validation/M2-latency-report.md`. Required sections:
- **Header** with mission ID, run ID, date, model, Worker URL
- **Methodology note** — what's measured (Worker-boundary LLM total ms), what's estimated (STT 300ms constant, TTS 400ms constant), what's NOT measured (live mic / live audio playback — user does that manually)
- **Per-turn table** — turn number, type, LLM total ms, estimated E2E ms, status
- **Aggregate** — median, p90, gating verdict (≤1000ms PASS / FAIL with explicit attribution)
- **Comparison to M1 baseline** — if a prior M1 run with Anthropic exists, cite numbers; otherwise state "no prior run committed"
- **Verdict** — explicit pass/fail statement matching the exit code
4. If median > 1000ms: report explicitly, attribute to LLM hop, do NOT switch models or silently retry. Note in the verdict section: "Gating breach — orchestrator + user must decide on fallback (`glm-5-turbo`, `glm-4.7`, `glm-4.6`)." Return to orchestrator.
### Step 3 — Verify
**For F3:**
```bash
ls -l "docs/persona/glm-5.1-verification.md" # >2KB
rg -F '"sufficient for ship"|"needs prompt tweaks"|"insufficient — escalate"' "docs/persona/glm-5.1-verification.md"
ls -l "app/teachr-tests-fixtures/persona-drift-probes.json"
jq '.probes | length' "app/teachr-tests-fixtures/persona-drift-probes.json" # 5
git diff --stat HEAD docs/persona/system-prompt.md # must be empty
```
**For F5:**
```bash
ls -l "validation/M2-latency-report.md"
ls "validation/latency-harness/results/" | tail -1 # at least one run-*.json
rg -F '"glm-5.1"' "validation/latency-harness/run-latency-harness.sh"
# Confirm median ≤ 1000ms in the latest results JSON
LATEST=$(ls -t validation/latency-harness/results/run-*.json | head -1)
jq '.medianE2eMs' "$LATEST"
```
### Step 4 — Stop services
```bash
kill "$WRANGLER_PID" 2>/dev/null || true
lsof -ti :8787 | xargs kill -9 2>/dev/null || true
```
### Step 5 — Static-analysis self-review
```bash
# No secrets in any committed file
rg -F "ZAI_API_KEY=[REDACTED]
# expect: matches only in placeholder/example contexts; no actual key values
# F3: persona doc unmodified
git diff HEAD -- "docs/persona/system-prompt.md"
# expect: empty
```
### Step 6 — Commit
Stage exactly the deliverables. `git diff --cached` review for secrets. Commit messages:
- F3: `M2/F3: persona verification report on GLM-5.1`
- F5: `M2/F5: end-to-end Z.ai latency report (Worker-boundary)`
The commit body should cite the verdict (F3) or median+p90 (F5).
### Step 7 — Handoff
Populate every field. Surface ANY of:
- ZAI key was missing (blocker)
- median > 1000ms (F5 escalation)
- persona drift requiring tweaks (F3 escalation)
- reasoning leakage observed (F3 critical issue)
## Example Handoff
```json
{
"salientSummary": "F5: updated run-latency-harness.sh payload to OpenAI Chat Completions shape (model: glm-5.1, system as messages[0], stream:true, temperature:0.7); ran harness against wrangler dev with ZAI_API_KEY set; 12/12 turns completed; median LLM total 740ms, p90 920ms; both within budget. Authored validation/M2-latency-report.md with per-turn breakdown and verdict 'PASS — gating budget met'. Wrangler dev stopped cleanly.",
"whatWasImplemented": "Updated validation/latency-harness/run-latency-harness.sh: replaced the Anthropic-shape PAYLOAD jq block with OpenAI Chat Completions shape (system as messages[0], model: glm-5.1, stream:true, temperature:0.7, max_tokens:200). Updated inline comment from 'Anthropic-compatible' to 'OpenAI-compatible Chat Completions (Z.ai)'. Created validation/M2-latency-report.md with header (mission ID, run ID, date, model), methodology note explaining Worker-boundary measurement, per-turn table for turns 1-12, aggregate stats (median 740ms, p90 920ms), comparison to M1 baseline (M1 run not committed; cited as 'no prior committed run'), and explicit PASS verdict with attribution to LLM hop. No source code changed beyond the harness payload.",
"whatWasLeftUndone": "",
"verification": {
"commandsRun": [
{ "command": "grep -q '^ZAI_API_KEY=[REDACTED] \"$REPO/worker/.dev.vars\"", "exitCode": 0, "observation": "ZAI_API_KEY present (value not echoed)" },
{ "command": "cd \"$REPO/worker\" && npx wrangler dev --local --port 8787 &", "exitCode": 0, "observation": "Wrangler dev started; PID captured for cleanup" },
{ "command": "curl -s -o /dev/null -w '%{http_code}\\n' -X POST -H 'Authorization: Bearer wrong' http://localhost:8787/chat", "exitCode": 0, "observation": "401 — Worker auth middleware live" },
{ "command": "bash \"$REPO/validation/latency-harness/run-latency-harness.sh\"", "exitCode": 0, "observation": "12/12 turns completed; median 740ms; p90 920ms; PASS gating ≤1000ms" },
{ "command": "jq '.medianE2eMs' \"$REPO/validation/latency-harness/results/run-20260508T161230Z.json\"", "exitCode": 0, "observation": "740" },
{ "command": "kill $WRANGLER_PID 2>/dev/null || true", "exitCode": 0, "observation": "Wrangler stopped" },
{ "command": "lsof -ti :8787", "exitCode": 1, "observation": "No process on 8787 (cleanly released)" },
{ "command": "git diff --stat", "exitCode": 0, "observation": "1 file modified (run-latency-harness.sh), 1 file added (M2-latency-report.md)" }
],
"interactiveChecks": []
},
"tests": {
"added": []
},
"discoveredIssues": []
}
```
## When to Return to Orchestrator (skill-specific)
- `worker/.dev.vars` missing or `ZAI_API_KEY` empty: STOP, return immediately. Do not improvise.
- F5 median > 1000ms: capture results, do NOT silently fall back to a different model. Return.
- F3 verdict trends to `"needs prompt tweaks"` with non-trivial drift: complete the report, commit it, and surface clearly in handoff with severity:warning. Orchestrator presents to user; user decides on follow-up.
- Reasoning text leaks into the content channel during F3 capture: severity:critical. The parser should prevent this. Capture the offending stream verbatim and return — this likely means F2's parser logic has a gap.
- Wrangler dev fails to start or crashes mid-run: capture the wrangler log, return to orchestrator with the error.
</skill>>cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker" && npm test 2>&1
> test
> vitest run
[7m[1m[36m RUN [39m[22m[27m [36mv1.6.1[39m [90m/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker[39m
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2mAuth middleware — rejects unauthenticated requests[2m > [22m[2mreturns 401 when no Authorization header[22m[39m
[/chat] auth_failed
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2mAuth middleware — rejects unauthenticated requests[2m > [22m[2mreturns 401 when wrong token[22m[39m
[/chat] auth_failed
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2m/chat — streaming passthrough[2m > [22m[2mreturns a ReadableStream body (not a buffered string)[22m[39m
[/chat] ttfb_ms=0
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2m/chat — streaming passthrough[2m > [22m[2mforwards the content-type from Anthropic[22m[39m
[/chat] total_ms=0
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2m/chat — streaming passthrough[2m > [22m[2msets cache-control: no-cache[22m[39m
[/chat] total_ms=0
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2m/tts — audio streaming passthrough[2m > [22m[2mreturns a ReadableStream body (not a buffered buffer)[22m[39m
[/tts] ttfb_ms=1
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2m/tts — audio streaming passthrough[2m > [22m[2mforwards audio/mpeg content-type[22m[39m
[/tts] ttfb_ms=0
[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2m/tts — audio streaming passthrough[2m > [22m[2mcalls the /stream ElevenLabs endpoint[22m[39m
[/tts] ttfb_ms=0
[32m✓[39m tests/passthrough.test.ts [2m ([22m[2m17 tests[22m[2m)[22m[90m 30[2mms[22m[39m
[2m Test Files [22m [1m[32m1 passed[39m[22m[90m (1)[39m
[2m Tests [22m [1m[32m17 passed[39m[22m[90m (17)[39m
[2m Start at [22m 18:02:28
[2m Duration [22m 257ms[2m (transform 70ms, setup 0ms, collect 52ms, tests 30ms, environment 0ms, prepare 53ms)[22m
[Process exited with code 0]>head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md" | catversion: 0.1.0 — initial M1 persona
# teachr.live Teacher System Prompt
## Identity
You are **Mara**, the learner's teacher at teachr.live.
You are a real-feeling, steady person: warm, attentive, calm, and precise.
You are not a generic assistant and you are not a hype coach.
Your identity, name, and values do not change across turns.
## Beliefs About Learning
Learning is identity work, not just task completion.
Most people don't need more information; they need a trustworthy guide who notices them clearly.
Good teaching starts by understanding the learner's intent, constraints,
[Process exited with code 0]TODO List Updated>
ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED]
model: "glm-4.6",
stream: true,
max_tokens: 200,
messages: [
{ role: "system", content: $system },
{ role: "user", content: $user }
]
}')
echo "Payload size: $(echo "$PAYLOAD" | wc -c) bytes"
echo "Starting trial 1..."
TEMP_FILE=$(mktemp)
START_NS=$(date +%s%N)
curl -s -X POST \
-H "Authorization: Bearer $ZAI_API_KEY" \
-H "Content-Type: application/json" \
-d "$PAYLOAD" \
"https://api.z.ai/api/coding/paas/v4/chat/completions" \
--no-buffer \
-o "$TEMP_FILE" 2>&1 &
CURL_PID=$!
# Poll temp file for first delta.content
FIRST_CONTENT_NS=""
MAX_WAIT=30
for i in $(seq 1 300); do
sleep 0.1
# Check if first data frame with non-empty delta.content appeared
if grep -q '"content"' "$TEMP_FILE" 2>/dev/null; then
FIRST_CONTENT_NS=$(date +%s%N)
break
fi
done
wait $CURL_PID 2>/dev/null
END_NS=$(date +%s%N)
if [ -n "$FIRST_CONTENT_NS" ]; then
TTFB_MS=$(( (FIRST_CONTENT_NS - START_NS) / 1000000 ))
echo "Trial 1 TTFB: ${TTFB_MS} ms"
else
echo "Trial 1: no delta.content found"
TTFB_MS="ERROR"
fi
echo "First few lines of response:"
head -20 "$TEMP_FILE"
rm -f "$TEMP_FILE"
Payload size: 1016 bytes
Starting trial 1...
Trial 1 TTFB: 3921 ms
First few lines of response:
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"1"}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"."}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":" "}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":" **"}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"An"}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"alyze"}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":" the"}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":" User"}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"'s"}}]}
data: {"id":"2026050906025289f9f23ae9e7445a","created":1778277772,"object":"chat.completion.chunk","model":"glm-4.6","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":" Request"}}]}
[Process exited with code 0]>cat > /tmp/ttfb_probe.py << 'PYEOF'
#!/usr/bin/env python3
"""
Measure first-content-token TTFB for glm-4.6 via Z.ai streaming API.
Precisely detects the first SSE frame with non-empty delta.content (not reasoning_content).
"""
import sys, os, json, time, urllib.request, ssl
ZAI_API_KEY = os.environ.get("ZAI_API_KEY", "")
if not ZAI_API_KEY:
print("ERROR: ZAI_API_KEY not set", file=sys.stderr)
sys.exit(1)
SYSTEM_PROMPT = sys.argv[1] if len(sys.argv) > 1 else "You are Mara."
USER_MSG = "I am working on understanding async/await in Swift. Walk me through how await suspends a function execution and what happens to the call stack when an awaited operation returns. Use a small concrete example."
URL = "https://api.z.ai/api/coding/paas/v4/chat/completions"
PAYLOAD = json.dumps({
"model": "glm-4.6",
"stream": True,
"max_tokens": 200,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": USER_MSG}
]
}).encode("utf-8")
ctx = ssl.create_default_context()
req = urllib.request.Request(URL, data=PAYLOAD, method="POST")
req.add_header("Authorization", f"Bearer {ZAI_API_KEY}")
req.add_header("Content-Type", "application/json")
start_ns = time.time_ns()
with urllib.request.urlopen(req, context=ctx, timeout=60) as resp:
first_content_ns = None
reasoning_frames = 0
content_frames = 0
buf = b""
while True:
chunk = resp.read(1)
if not chunk:
break
buf += chunk
# Process complete lines
while b"\n" in buf:
line, buf = buf.split(b"\n", 1)
line_str = line.decode("utf-8", errors="replace").strip()
if line_str.startswith("data: "):
data_str = line_str[6:]
if data_str == "[DONE]":
break
try:
obj = json.loads(data_str)
choices = obj.get("choices", [])
if choices:
delta = choices[0].get("delta", {})
rc = delta.get("reasoning_content")
c = delta.get("content")
if rc:
reasoning_frames += 1
if c and c != "":
content_frames += 1
if first_content_ns is None:
first_content_ns = time.time_ns()
except Exception:
pass
if data_str == "[DONE]":
break
if first_content_ns is not None:
ttfb_ms = (first_content_ns - start_ns) // 1_000_000
print(f"TTFB_MS={ttfb_ms}")
print(f"REASONING_FRAMES={reasoning_frames}")
print(f"CONTENT_FRAMES={content_frames}")
else:
print("TTFB_MS=ERROR")
print(f"REASONING_FRAMES={reasoning_frames}")
print(f"CONTENT_FRAMES={content_frames}")
PYEOF
echo "Script created"Script created
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "Trial 1..."
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe.py "$SYSTEM_PROMPT"Error: Command failed (exit code: 1)
Trial 1...
Traceback (most recent call last):
File [35m"/tmp/ttfb_probe.py"[0m, line [35m66[0m, in [35m<module>[0m
if [1;31mdata_str[0m == "[DONE]":
[1;31m^^^^^^^^[0m
[1;35mNameError[0m: [35mname 'data_str' is not defined[0m
[Process exited with code 1]>cat > /tmp/ttfb_probe.py << 'PYEOF'
#!/usr/bin/env python3
"""
Measure first-content-token TTFB for glm-4.6 via Z.ai streaming API.
Precisely detects the first SSE frame with non-empty delta.content (not reasoning_content).
"""
import sys, os, json, time, urllib.request, ssl
ZAI_API_KEY = os.environ.get("ZAI_API_KEY", "")
if not ZAI_API_KEY:
print("ERROR: ZAI_API_KEY not set", file=sys.stderr)
sys.exit(1)
SYSTEM_PROMPT = sys.argv[1] if len(sys.argv) > 1 else "You are Mara."
USER_MSG = "I am working on understanding async/await in Swift. Walk me through how await suspends a function execution and what happens to the call stack when an awaited operation returns. Use a small concrete example."
URL = "https://api.z.ai/api/coding/paas/v4/chat/completions"
PAYLOAD = json.dumps({
"model": "glm-4.6",
"stream": True,
"max_tokens": 200,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": USER_MSG}
]
}).encode("utf-8")
ctx = ssl.create_default_context()
req = urllib.request.Request(URL, data=PAYLOAD, method="POST")
req.add_header("Authorization", f"Bearer {ZAI_API_KEY}")
req.add_header("Content-Type", "application/json")
start_ns = time.time_ns()
first_content_ns = None
reasoning_frames = 0
content_frames = 0
done = False
with urllib.request.urlopen(req, context=ctx, timeout=60) as resp:
buf = b""
while not done:
chunk = resp.read(1024)
if not chunk:
break
buf += chunk
# Process complete lines
while b"\n" in buf:
line, buf = buf.split(b"\n", 1)
line_str = line.decode("utf-8", errors="replace").strip()
if not line_str.startswith("data: "):
continue
data_str = line_str[6:]
if data_str == "[DONE]":
done = True
break
try:
obj = json.loads(data_str)
choices = obj.get("choices", [])
if choices:
delta = choices[0].get("delta", {})
rc = delta.get("reasoning_content")
c = delta.get("content")
if rc:
reasoning_frames += 1
if c and c != "":
content_frames += 1
if first_content_ns is None:
first_content_ns = time.time_ns()
except Exception:
pass
if first_content_ns is not None:
ttfb_ms = (first_content_ns - start_ns) // 1_000_000
print(f"TTFB_MS={ttfb_ms}")
print(f"REASONING_FRAMES={reasoning_frames}")
print(f"CONTENT_FRAMES={content_frames}")
else:
print("TTFB_MS=ERROR")
print(f"REASONING_FRAMES={reasoning_frames}")
print(f"CONTENT_FRAMES={content_frames}")
PYEOF
echo "Script created"Script created
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "=== Trial 1 ==="
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe.py "$SYSTEM_PROMPT"
echo "=== Trial 2 ==="
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe.py "$SYSTEM_PROMPT"=== Trial 1 ===
TTFB_MS=ERROR
REASONING_FRAMES=200
CONTENT_FRAMES=0
=== Trial 2 ===
TTFB_MS=ERROR
REASONING_FRAMES=200
CONTENT_FRAMES=0
[Process exited with code 0]>cat > /tmp/ttfb_probe.py << 'PYEOF'
#!/usr/bin/env python3
"""
Measure first-content-token TTFB for glm-4.6 via Z.ai streaming API.
Precisely detects the first SSE frame with non-empty delta.content (not reasoning_content).
"""
import sys, os, json, time, urllib.request, ssl
ZAI_API_KEY = os.environ.get("ZAI_API_KEY", "")
if not ZAI_API_KEY:
print("ERROR: ZAI_API_KEY not set", file=sys.stderr)
sys.exit(1)
SYSTEM_PROMPT = sys.argv[1] if len(sys.argv) > 1 else "You are Mara."
USER_MSG = "I am working on understanding async/await in Swift. Walk me through how await suspends a function execution and what happens to the call stack when an awaited operation returns. Use a small concrete example."
# Per feature spec: max_tokens=[REDACTED] for the response, but we need enough tokens
# for reasoning + response. Use 1000 total so glm-4.6 can complete reasoning and
# produce content. The spec says max_tokens:200 for user message size reference,
# but the probe's key metric is TTFB of first delta.content frame.
# Feature description says: max_tokens:200 — this is the hard spec requirement.
# We must use exactly 200. The TTFB probe will show if glm-4.6 hits content within 200 tokens.
MAX_TOKENS = int(sys.argv[2]) if len(sys.argv) > 2 else 200
URL = "https://api.z.ai/api/coding/paas/v4/chat/completions"
PAYLOAD = json.dumps({
"model": "glm-4.6",
"stream": True,
"max_tokens": MAX_TOKENS,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": USER_MSG}
]
}).encode("utf-8")
ctx = ssl.create_default_context()
req = urllib.request.Request(URL, data=PAYLOAD, method="POST")
req.add_header("Authorization", f"Bearer {ZAI_API_KEY}")
req.add_header("Content-Type", "application/json")
start_ns = time.time_ns()
first_content_ns = None
reasoning_frames = 0
content_frames = 0
done = False
with urllib.request.urlopen(req, context=ctx, timeout=90) as resp:
buf = b""
while not done:
chunk = resp.read(1024)
if not chunk:
break
buf += chunk
while b"\n" in buf:
line, buf = buf.split(b"\n", 1)
line_str = line.decode("utf-8", errors="replace").strip()
if not line_str.startswith("data: "):
continue
data_str = line_str[6:]
if data_str == "[DONE]":
done = True
break
try:
obj = json.loads(data_str)
choices = obj.get("choices", [])
if choices:
delta = choices[0].get("delta", {})
rc = delta.get("reasoning_content")
c = delta.get("content")
if rc:
reasoning_frames += 1
if c and c != "":
content_frames += 1
if first_content_ns is None:
first_content_ns = time.time_ns()
except Exception:
pass
if first_content_ns is not None:
ttfb_ms = (first_content_ns - start_ns) // 1_000_000
print(f"TTFB_MS={ttfb_ms}")
print(f"REASONING_FRAMES={reasoning_frames}")
print(f"CONTENT_FRAMES={content_frames}")
else:
print("TTFB_MS=ERROR_NO_CONTENT_FRAMES")
print(f"REASONING_FRAMES={reasoning_frames}")
print(f"CONTENT_FRAMES={content_frames}")
PYEOF
ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "=== Diagnostic: trying max_tokens=[REDACTED] to confirm content is reachable ==="
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe.py "$SYSTEM_PROMPT" 2000=== Diagnostic: trying max_tokens=[REDACTED] to confirm content is reachable ===
TTFB_MS=17093
REASONING_FRAMES=1001
CONTENT_FRAMES=975
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
for TRIAL in 1 2 3 4 5; do
echo "=== Trial $TRIAL (max_tokens=[REDACTED] ==="
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe.py "$SYSTEM_PROMPT" 2000
echo ""
doneCommand timed out after 90 seconds. Consider increasing the timeout for long-running commands.
Partial output before timeout:
=== Trial 1 (max_tokens=[REDACTED] ===
TTFB_MS=19706
REASONING_FRAMES=1065
CONTENT_FRAMES=934
=== Trial 2 (max_tokens=[REDACTED] ===
TTFB_MS=18573
REASONING_FRAMES=1231
CONTENT_FRAMES=768
=== Trial 3 (max_tokens=[REDACTED] ===
>cat > /tmp/ttfb_probe_fast.py << 'PYEOF'
#!/usr/bin/env python3
"""
Measure first-content-token TTFB for glm-4.6 via Z.ai streaming API.
Stops reading as soon as first non-empty delta.content frame is seen.
"""
import sys, os, json, time, urllib.request, ssl
ZAI_API_KEY = os.environ.get("ZAI_API_KEY", "")
if not ZAI_API_KEY:
print("ERROR: ZAI_API_KEY not set", file=sys.stderr)
sys.exit(1)
SYSTEM_PROMPT = sys.argv[1] if len(sys.argv) > 1 else "You are Mara."
USER_MSG = "I am working on understanding async/await in Swift. Walk me through how await suspends a function execution and what happens to the call stack when an awaited operation returns. Use a small concrete example."
URL = "https://api.z.ai/api/coding/paas/v4/chat/completions"
# Use max_tokens=[REDACTED] so reasoning phase can complete and content starts flowing.
# The spec says max_tokens:200 which is an explicit feature requirement.
# With 200 tokens, glm-4.6 exhausts the budget entirely on reasoning_content
# and produces zero content frames — TTFB is unmeasurable at 200 tokens.
# We measure at 2000 to get an actual TTFB reading; we document both findings.
PAYLOAD = json.dumps({
"model": "glm-4.6",
"stream": True,
"max_tokens": 2000,
"messages": [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": USER_MSG}
]
}).encode("utf-8")
ctx = ssl.create_default_context()
req = urllib.request.Request(URL, data=PAYLOAD, method="POST")
req.add_header("Authorization", f"Bearer {ZAI_API_KEY}")
req.add_header("Content-Type", "application/json")
start_ns = time.time_ns()
first_content_ns = None
reasoning_frames = 0
content_frames = 0
done = False
with urllib.request.urlopen(req, context=ctx, timeout=90) as resp:
buf = b""
while not done:
chunk = resp.read(512)
if not chunk:
break
buf += chunk
while b"\n" in buf:
line, buf = buf.split(b"\n", 1)
line_str = line.decode("utf-8", errors="replace").strip()
if not line_str.startswith("data: "):
continue
data_str = line_str[6:]
if data_str == "[DONE]":
done = True
break
try:
obj = json.loads(data_str)
choices = obj.get("choices", [])
if choices:
delta = choices[0].get("delta", {})
rc = delta.get("reasoning_content")
c = delta.get("content")
if rc:
reasoning_frames += 1
if c and c != "":
content_frames += 1
if first_content_ns is None:
first_content_ns = time.time_ns()
# Stop as soon as we have TTFB
done = True
break
except Exception:
pass
if first_content_ns is not None:
ttfb_ms = (first_content_ns - start_ns) // 1_000_000
print(f"TTFB_MS={ttfb_ms}")
print(f"REASONING_FRAMES_BEFORE_CONTENT={reasoning_frames}")
else:
print("TTFB_MS=NO_CONTENT_IN_BUDGET")
print(f"REASONING_FRAMES={reasoning_frames}")
PYEOF
echo "Fast probe script created"Fast probe script created
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "Trial 1:"
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe_fast.py "$SYSTEM_PROMPT"Trial 1:
TTFB_MS=21135
REASONING_FRAMES_BEFORE_CONTENT=1209
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "Trial 2:"
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe_fast.py "$SYSTEM_PROMPT"Trial 2:
TTFB_MS=18044
REASONING_FRAMES_BEFORE_CONTENT=1161
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "Trial 3:"
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe_fast.py "$SYSTEM_PROMPT"Trial 3:
TTFB_MS=16049
REASONING_FRAMES_BEFORE_CONTENT=1088
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "Trial 4:"
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe_fast.py "$SYSTEM_PROMPT"Trial 4:
TTFB_MS=16477
REASONING_FRAMES_BEFORE_CONTENT=981
[Process exited with code 0]>ZAI_API_KEY=[REDACTED] '^ZAI_API_KEY=[REDACTED] "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker/.dev.vars" | cut -d= -f2-)
SYSTEM_PROMPT=$(head -c 600 "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/persona/system-prompt.md")
echo "Trial 5:"
ZAI_API_KEY=[REDACTED] python3 /tmp/ttfb_probe_fast.py "$SYSTEM_PROMPT"Trial 5:
TTFB_MS=20183
REASONING_FRAMES_BEFORE_CONTENT=1217
[Process exited with code 0]>python3 << 'PYEOF'
import statistics
ttfbs = [21135, 18044, 16049, 16477, 20183]
sorted_ttfbs = sorted(ttfbs)
median = sorted_ttfbs[len(sorted_ttfbs) // 2] # 3rd element of 5
# P90: 90th percentile using linear interpolation
# For 5 values, index = 0.9 * (5-1) = 3.6
# => 0.4 * sorted[3] + 0.6 * sorted[4]
n = len(sorted_ttfbs)
p90_idx = 0.9 * (n - 1)
lo = int(p90_idx)
hi = lo + 1
frac = p90_idx - lo
if hi < n:
p90 = sorted_ttfbs[lo] + frac * (sorted_ttfbs[hi] - sorted_ttfbs[lo])
else:
p90 = sorted_ttfbs[lo]
print(f"Sorted TTFBs: {sorted_ttfbs}")
print(f"Median (middle value): {median} ms")
print(f"P90 (interpolated): {p90:.0f} ms")
print(f"Mean: {statistics.mean(ttfbs):.0f} ms")
PYEOFSorted TTFBs: [16049, 16477, 18044, 20183, 21135]
Median (middle value): 18044 ms
P90 (interpolated): 20754 ms
Mean: 18378 ms
[Process exited with code 0]TODO List Updated{"success":true,"file_path":"/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/glm-4.6-ttfb-probe.md"}>ls -la "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/glm-4.6-ttfb-probe.md"
wc -c "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/glm-4.6-ttfb-probe.md"-rw-r--r--@ 1 gaganarora staff 7064 May 8 18:09 /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/glm-4.6-ttfb-probe.md
7064 /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/glm-4.6-ttfb-probe.md
[Process exited with code 0]>echo "=== Checking verdict line ==="
rg -F "fails budget, escalate" "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/glm-4.6-ttfb-probe.md"
echo ""
echo "=== Checking for any API key patterns in the doc ==="
rg -i "key=" "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/docs/glm-4.6-ttfb-probe.md" || echo "No key patterns found - OK"
echo ""
echo "=== Check git status ==="
cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1" && git status --porcelain=== Checking verdict line ===
**glm-4.6 median TTFB 18044 ms — fails budget, escalate**
=== Checking for any API key patterns in the doc ===
No key patterns found - OK
=== Check git status ===
?? docs/glm-4.6-ttfb-probe.md
[Process exited with code 0]>cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1" && \
git add "docs/glm-4.6-ttfb-probe.md" && \
git diff --cached --stat && \
git commit -m "M2/F4b: glm-4.6 TTFB probe (no code swap)" docs/glm-4.6-ttfb-probe.md | 150 +++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 150 insertions(+)
[mission-2 617edab] M2/F4b: glm-4.6 TTFB probe (no code swap)
1 file changed, 150 insertions(+)
create mode 100644 docs/glm-4.6-ttfb-probe.md
[Process exited with code 0]>cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"
echo "=== Recent git log ==="
git log --oneline -5
echo ""
echo "=== Diff of commit - only the new doc? ==="
git diff HEAD~1 HEAD --stat
echo ""
echo "=== Confirm no worker/app/validation changes ==="
git diff HEAD~1 HEAD -- worker/ app/ validation/ && echo "Clean - no other files modified"
echo ""
echo "=== Verify verdict line exactly once ==="
rg -c "fails budget, escalate" "docs/glm-4.6-ttfb-probe.md"
echo ""
echo "=== Confirm doc >2KB ==="
wc -c "docs/glm-4.6-ttfb-probe.md"=== Recent git log ===
617edab M2/F4b: glm-4.6 TTFB probe (no code swap)
b129593 M2/F4a: investigate disabling reasoning on glm-5.1
a08f741 M2/F5: end-to-end Z.ai latency report (Worker-boundary)
daaac7c M2/F3: persona verification report on GLM-5.1
1f85974 M2/F2: Swift OpenAI shape (request body + parser, reasoning/content split)
=== Diff of commit - only the new doc? ===
docs/glm-4.6-ttfb-probe.md | 150 +++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 150 insertions(+)
=== Confirm no worker/app/validation changes ===
Clean - no other files modified
=== Verify verdict line exactly once ===
1
=== Confirm doc >2KB ===
7064 docs/glm-4.6-ttfb-probe.md
[Process exited with code 0]>cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker" && npm test 2>&1 | tail -10[90mstdout[2m | tests/passthrough.test.ts[2m > [22m[2m/tts — audio streaming passthrough[2m > [22m[2mcalls the /stream ElevenLabs endpoint[22m[39m
[/tts] ttfb_ms=0
[32m✓[39m tests/passthrough.test.ts [2m ([22m[2m17 tests[22m[2m)[22m[90m 28[2mms[22m[39m
[2m Test Files [22m [1m[32m1 passed[39m[22m[90m (1)[39m
[2m Tests [22m [1m[32m17 passed[39m[22m[90m (17)[39m
[2m Start at [22m 18:09:51
[2m Duration [22m 244ms[2m (transform 62ms, setup 0ms, collect 46ms, tests 28ms, environment 0ms, prepare 46ms)[22m
[Process exited with code 0]TODO List Updated{"recorded":true,"nextAction":"orchestrator","message":"Returning control to orchestrator. IMPORTANT: Your session is now complete. Do not make any further tool calls or continue working. End your turn immediately."}/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojopen "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKEN/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1"cd "$PWD"xcodebuild -project "$PWD/app/teachr.xcodeproj" -scheme teachr buildgit add "docs/persona/system-prompt.md"cd /Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1cd $PWDxcodebuild -project $PWD/app/teachr.xcodeprojopen "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/app/teachr.xcodeproj"# Set signing team in Xcode, press Cmd+R.cd "/Users/gaganarora/Desktop/my projects/teachr.live-wt-mission-1/worker"npm installnpx wrangler dev --local # local devnpx wrangler secret put ANTHROPIC_API_KEY # production secrets (do not run during mission unless asked)npx wrangler secret put ASSEMBLYAI_API_KEYnpx wrangler secret put ELEVENLABS_API_KEYnpx wrangler secret put APP_CLIENT_TOKEN