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Synthesized CLI agent-friendly principlesPublic

Agent

Model

GPT-5.5

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Aug 21, 12:32 PM

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Repo

nilenso/ai-playground

Summary

Closed, Completed

Understand
Reviewed experimental documentation and prior results.
Understand
Build
Synthesized general principles from experiment data.
Build
Edit
Documented key CLI agent-friendly guidelines.
Edit

You are PI ARM B for the autoresearch experiment. You replace the old Claude Arm B because the Claude subscription ran out. Work as a child arm under an orchestrator; do not make project decisions on your own.

READ FIRST:

/Users/priyangapkini/nilenso/ai-playground/autoresearch/docs/plan.md (superseding agreed plan; follow it exactly)
/Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/arm-b/HANDOVER.md
/Users/priyangapkini/.claude/projects/-Users-priyangapkini-nilenso-ai-playground-autoresearch/memory/autoresearch-three-arm-experiment.md
/Users/priyangapkini/.claude/projects/-Users-priyangapkini-nilenso-ai-playground-autoresearch/memory/parallel-runs-need-separate-botmap-repo.md
/Users/priyangapkini/.claude/projects/-Users-priyangapkini-nilenso-ai-playground-autoresearch/memory/botmap-cache-is-shared-and-unpinned.md
/Users/priyangapkini/.claude/projects/-Users-priyangapkini-nilenso-ai-playground-autoresearch/memory/decisions-are-the-users.md

Important current facts:

The new plan assigns Arm B: Phase 0a skill isolation check, Phase 1 contract.py (blocking), then probe.py and record-v2 wiring.
Parent/orchestrator may implement common blocking edits, but you should be ready to own/review Arm B tasks.
Global Claude botmap skill is disabled because it shadowed the project skill. Phase 0a is to add --setting-sources project to claude -p invocation in runner.py and verify a probe run sees only project skills.
OpenRouter API key exists in /Users/priyangapkini/nilenso/ai-playground/autoresearch/.env. The experiment under test still uses claude interactive mode; do not launch paid runs without orchestrator approval.

Your immediate task:

Read the files above and inspect runner.py, contract-adjacent code, and current repo structure.
Prepare a concise readiness report: exact implementation plan for Phase 0a and contract.py, validation commands, hazards, dirty worktree concerns, and questions for Priyanga/orchestrator.
Do NOT edit project/source files yet. Do NOT spend model/API quota by launching evals.

When done, say READY and provide the report in this session.

READY

Readiness report — Arm B

Current state inspected

Read:

docs/plan.md
experiments/arm-b/HANDOVER.md
all four memory files
autoresearch/runner.py
adjacent evaluator/scoring/trace/config/worktree code
current repo/worktree status

Important current code points:

Claude invocation is in autoresearch/runner.py inside ask().
Current command lacks --setting-sources project.
Existing attempt shape is score.Attempt + trace.Call + trace.Transcript.
Existing classifier is old single-label autoresearch/taxonomy.py.
No autoresearch/agenteval/ package exists yet.
Existing tests are in tests/test_scoring.py.

Phase 0a implementation plan — skill isolation

Exact source edit when approved:

In autoresearch/runner.py, change:

to include:

Likely placement:

python

cmd = [	"claude", "-p", question.question,	"--setting-sources", "project",	"--output-format", "stream-json", "--verbose",	"--model", model,	"--permission-mode", "bypassPermissions",	"--allowedTools", "Bash",]

Rationale: lowest-risk behavioral change, scoped exactly to agreed Phase 0.

Validation after orchestrator approval:

Static/unit:
bash
```
uv run pytest
```
Cheap skill-isolation probe, only if Priyanga/orchestrator approves Claude quota use:
- create temp workdir with only project .claude/skills/botmap/SKILL.md
- invoke claude -p with --setting-sources project
- ask it to report visible skill names
- expected: botmap + Claude Code built-ins only; no herdr, context-lens, lavish, find-skills, mattpocock-skills:*
One normal low-cost question completion, also only with approval:
- via runner.ask() or equivalent retained attempt
- expected: transcript result present, botmap command logging still works

Hazard:

Need confirm Claude Code treats temp cwd .claude/skills as “project” source under --setting-sources project. If not, botmap skill may disappear too.

Phase 1 implementation plan — `agenteval/contract.py`

Create package:

Implement:

python

@dataclass(frozen=True)class Probe:	kind: str	ran: str	result: str	conclusive: bool
@dataclass(frozen=True)class CallVerdict:	outcome: str	blame: str	recovery: str	cls: str	subtype: str | None	evidence: str	probes: tuple[Probe, ...]
@dataclass(frozen=True)class Record2:	schema: str	question_id: str	repeat: int	calls: tuple[dict, ...]	agent_side: tuple[dict, ...]	tools_used: dict[str, int]	botmap_calls: int	answer: dict

Key choices to confirm:

Python field should be cls, JSON key should be "class" because class is reserved.
derive_class(outcome, blame, recovery) is the only class derivation function.
validate(raw) returns list[str], never raises.
load(path) can raise only for file/JSON I/O, but schema problems should be discoverable through validate.
write(path, record) writes JSON with "schema": "agenteval/2".

Expected derivation rules:

blame == "environment" → E
blame == "agent" → F
outcome == "degenerate" → D
outcome == "error" and recovery == "unguided" → A
outcome == "error" and recovery == "guided" → B
outcome == "empty" and recovery == "unguided" → C
otherwise clean/OK mapping needs orchestrator confirmation: probably no class or "clean"? Plan table only names A-F failures.

Validation tests to add:

hand-written valid record round-trips
validate() accepts minimal valid agenteval/2
validate() rejects stored "class" inconsistent with axes
invalid outcome/blame/recovery values are reported
probe shape errors are reported
derive_class() covers A-F table

Commands:

No model/API spend needed for contract validation.

Dirty worktree concerns

From repo root /Users/priyangapkini/nilenso/ai-playground:

Large dirty state exists outside autoresearch: many deleted files under editing-in-progress/, plus untracked IDE/worktree files.

Scoped autoresearch paths appear clean: returned no output.

Concern:

Any commit/status from monorepo root will be noisy and dangerous unless scoped carefully.
I should only touch autoresearch/... and tests/... after approval.

Hazards

Do not launch evals or optimizer runs without approval.
Claude subscription quota is exhausted/limited; probe calls still spend quota.
OpenRouter key exists but experiment under test still uses Claude interactive path unless orchestrator changes that.
BOTMAP_REPO must remain isolated: /Users/priyangapkini/workspace/ar-b/botmap.
botmap cache is user-global and unpinned; do not infer candidate failure from release/cache anomalies.
experiments/runs/ and experiments/baselines/ are gitignored local evidence; do not overwrite/move without explicit direction.
Do not edit botmap/evals/.

Questions for Priyanga/orchestrator

For clean calls in record-v2, should JSON "class" be omitted/null, or should we introduce "clean" despite plan saying classes A-F?
Should load() validate and raise on bad records, or simply parse while callers use validate()?
May Phase 0a validation spend the two cheap Claude probe calls after the code edit?
Should invocation metadata (setting_sources, model, agent path/provider) be included in Record2 now, or deferred as plan says it is not Phase 0?
Should I keep contract tests in existing tests/test_scoring.py or create tests/test_contract.py?

Decisions from Priyanga/orchestrator:\n\n1. Phase 0a validation: approved to spend the two cheap Claude probe calls after the code change.\n2. record-v2 clean calls: use JSON class: null.\n3. Fixture sources: first copy/preserve old untracked Arm A notes into autoresearch, then old notes may be used as supporting fixture evidence.\n4. C1 candidate identity is NOT decided; Priyanga does not understand the question yet. Do not act on C1 identity until discussed further.\n5. Ignored-hint detection: use option C — strict boolean plus richer details/window.\n6. Quota failures: extend contract with attempt verdict; quota can be attempt-level class E outside per-call CallVerdict.\n7. Exit-0 did-you-mean/recovery hints: keep the recovery path. Treat guidance as something the agent should be able to use in the next iteration; if the agent does not recover, record that failure path (guided signal + agent-side failure detail), do not flatten it away.\n\nParent/orchestrator will start Phase 0a + contract.py unless told otherwise. Do not edit source files yet.

ACK.

I will not edit source files yet.

Updated Arm B understanding:

Phase 0a: approved for two cheap Claude probe calls after code change.
record-v2 clean call class: JSON "class": null.
Contract should include attempt-level verdict so quota/session-limit can be class E outside CallVerdict.
Exit-0 did you mean remains guided recovery signal; if ignored, record guided signal plus agent-side failure detail.
Ignored-hint detection later: strict boolean + richer detail/window.
Fixture evidence may use old Arm A notes only after they are copied/preserved into autoresearch.
No action on C1 candidate identity.

If parent/orchestrator is implementing Phase 0a + contract.py, I will stay ready to review/validate Arm B scope rather than duplicate edits.

Contract is now landed in the shared checkout (not committed yet):

autoresearch/agenteval/contract.py
autoresearch/agenteval/init.py
tests/test_contract.py
runner.py has Phase 0a --setting-sources project
uv run pytest passes: 89 passed
Phase 0a probes completed: project botmap skill sentinel loaded under --setting-sources project; normal hospitals-rhode-island attempt completed through OpenRouter/Claude interactive path.

You are unblocked for Arm B Phase 2 work. You may edit ONLY these files unless you ask first:

autoresearch/agenteval/probe.py
tests/test_probe.py
if absolutely needed for shared probe helpers, add small data-only helpers under autoresearch/agenteval/ but ask before touching runner/evaluator wiring.

Do not touch botmap/evals. Do not launch evals or spend model/API quota. CLI probes in tests must be mocked/static, not real botmap spend/network. Follow docs/plan.md exactly: six probes, budget object/logging shape, inconclusive recorded, class-C subtype evidence. Acceptance target: recorded --class recreation case should produce c-wrong-column with 0-vs-86 evidence; if you cannot find the exact recorded trace, build a static fixture that captures that evidence and report the missing source.

Goal for this pass: implement probe.py with a pure testable interface and tests. Defer record-v2 wiring until probe/taxonomy contracts settle unless it is trivial and isolated. Report READY or BLOCKED with tests run and decisions needed.

READY

Implemented Arm B Phase 2 probe pass in allowed files only:

autoresearch/agenteval/probe.py
tests/test_probe.py

What landed:

Pure, injected-runner probe interface; no real botmap/network/model calls.
ProbeBudget with per-question call budget and audit log.
ProbeResult carrying probes, c-* subtype, evidence, and budget.
Six probes:
- vocabulary → c-vocabulary
- column swap → c-wrong-column
- type sweep → c-wrong-type
- limit raise → c-truncated
- argv echo → c-dropped-input
- entity check → c-wrong-entity
Inconclusive probes recorded; unexplained empties return c-unknown.
Budget exhaustion records skipped inconclusive probe and stops further probing.
Acceptance fixture: land_use recreation as class → 0, subtype → 86 produces c-wrong-column.

Tests run:

Result:

Note:

I could not find the exact raw recorded --class recreation command trace under retained attempts. I used static fixture evidence from experiments/proposals.json and experiments/artifacts/findings.md, per instruction, and documented that in the test.

You are PI ARM B. The evaluator-first work is now in the shared autoresearch checkout. Your role now: own the prompt-lever arm using the NEW evaluator.

Context to read:

/Users/priyangapkini/nilenso/ai-playground/autoresearch/docs/plan.md
/Users/priyangapkini/nilenso/ai-playground/autoresearch/docs/agent-friendly-cli.md
/Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/orchestrator/HANDOVER.md
/Users/priyangapkini/nilenso/ai-playground/autoresearch/autoresearch/agenteval/*
/Users/priyangapkini/nilenso/ai-playground/autoresearch/autoresearch/evaluator.py and optimize.py

Your clean botmap repo:

/Users/priyangapkini/workspace/ar-b-new/botmap @ 3009509 Use BOTMAP_REPO=/Users/priyangapkini/workspace/ar-b-new/botmap for any future prompt-lever run.

Constraints:

Do not talk to other arms. Report only here.
Do not run paid GEPA/evals until orchestrator says go.
Do not touch botmap/evals.
Preserve old history; write new run dirs.

Task:

Inspect whether autoresearch.optimize/evaluator currently uses the new agenteval score/explain, or still the legacy score.py. (Likely legacy.)
Prepare the exact prompt-lever run plan that uses the new evaluator once wiring is ready.
If you can add non-invasive checks/tests for prompt-lever readiness without overlapping Arm C wiring, do so; otherwise do not edit.
Produce a READY/BLOCKED report with exact command, required prerequisites, estimated cost, and dashboard/run-dir names.

BLOCKED for the prompt-lever run; READY with plan.

1. Current wiring status

autoresearch/evaluator.py still uses legacy scoring:

imports from . import config, runner, score
calls score.correctness(usable)
logs score.feedback(example, attempts)
reports FailedCommands: len(usable[0].errors)

It does not yet use:

autoresearch.agenteval.score.score_attempt
autoresearch.agenteval.explain.explain
enriched record-v2.json as the scoring source

runner.py does write record-v2.json when keep_dir is provided, but evaluator scoring still happens from legacy score.py.

Tests:

No source edits made.

2. Prompt-lever run plan once wiring is ready

Prerequisites:

Evaluator wired to new agenteval score/explain.
Candidate attempts retained/enriched so GEPA feedback uses class/probe evidence.
Dashboard updated for new Arm B repo paths, or accept that current Arm B dashboard points at old ~/workspace/ar-b.
/Users/priyangapkini/workspace/ar-b-new/botmap has .venv installed; currently missing executable .venv/bin/python.
Orchestrator explicitly approves paid run.

Setup:

bash

cd /Users/priyangapkini/nilenso/ai-playground/autoresearch
export BOTMAP_REPO=/Users/priyangapkini/workspace/ar-b-new/botmapexport AUTORESEARCH_AGENT_PATH=openrouterexport AUTORESEARCH_AGENT_PROVIDER=anthropic

If needed before run:

Run command:

bash

cd /Users/priyangapkini/nilenso/ai-playground/autoresearchBOTMAP_REPO=/Users/priyangapkini/workspace/ar-b-new/botmap \AUTORESEARCH_AGENT_PATH=openrouter \AUTORESEARCH_AGENT_PROVIDER=anthropic \uv run python -m autoresearch.optimize \  --lever prompt \  --budget 60 \  --workers 1 \  --keep-runs

Expected run dir:

Expected Arm B pool worktree:

Dashboard caveat:

Current dashboard config still points Arm B at old:

For live monitoring of this run, dashboard needs updating to ar-b-new.

3. Estimated cost

Using latest Phase 4 actuals:

60 attempts cost $15.50
one GEPA metric call = 2 repeats ≈ $0.52
budget 60 ≈ $31 for agent attempts
plus OpenRouter proposer/reflection cost

Suggested approval envelope: at least $35–45, assuming cached baseline is reused and not remeasured.

4. Current state/hazards

Clean botmap repo verified at 3009509.
botmap/data/skill.md exists.
.venv/bin/python missing in ar-b-new; run would degrade/fail unless uv sync is done.
Existing experiments/baselines/3009509.json means current optimize should not remeasure baseline, but new evaluator must avoid trusting legacy correctness as comparable.
Do not run until evaluator is wired to agenteval.

RELEASED: launch Arm B.

User explicitly said: “launch arm b”. You are authorized to launch the Arm B prompt-lever optimizer run using the NEW autoresearch evaluator.

Arm B purpose:

Prompt-lever arm.
Editable surface: botmap/data/skill.md only, unless optimizer wiring says otherwise.
Compare against Arm C/full-repo and Arm A/loop-as-skill later.

Repo/state:

Shared autoresearch checkout: /Users/priyangapkini/nilenso/ai-playground/autoresearch
Clean botmap repo for Arm B: /Users/priyangapkini/workspace/ar-b-new/botmap @ 3009509
Full repo optimizer support exists, but Arm B should normally NOT use --all-files. It should use --lever prompt.

Hard constraints:

Do NOT touch botmap/evals manually.
Do NOT restore ~/.claude/skills/botmap. If it exists, stop and report because prompt-lever measurement can be shadowed.
Arms cannot talk to each other; report only here.
Use new evaluator path: record-v2/agenteval score/explain/sabotage.
Run sabotage gate before optimizer launch.
Stop and report if tests fail, preflight fails, quota is exhausted, network is unhealthy, baseline/release comparability is invalid, or if the global botmap skill exists.
Preserve all artifacts; use new run dirs.

Before launch:

Check that Arm C has not left the shared checkout in a failing state. If tests fail because of Arm C’s uncommitted evaluator wiring, report BLOCKED rather than editing over it.
Run: cd /Users/priyangapkini/nilenso/ai-playground/autoresearch uv run pytest
Confirm sabotage gate passes.
Confirm ~/.claude/skills/botmap does not exist.

Launch command shape:

cd /Users/priyangapkini/nilenso/ai-playground/autoresearch set -a; source .env; set +a BOTMAP_REPO=/Users/priyangapkini/workspace/ar-b-new/botmap
uv run python -m autoresearch.optimize
--lever prompt
--budget 60
--keep-runs

Notes:

This is not low-cost; user has authorized launching arms.
Do not use --all-files or --full-repo-context unless you have a specific prompt-arm reason and report it first.
Do not use botmap/evals.
If run starts successfully, immediately report run dir, PID/status, and estimated scale/cost.

Deliverable/report:

Whether tests passed.
Exact command launched.
Run dir and any PID/status info.
Candidate/baseline comparability notes.
Any blockers.

Launched Arm B.

1. Gates

Tests passed:

Sabotage gate passed using taxonomy classifier + attempt-level quota check:

Global skill shadow check passed:

Botmap repo:

I ran uv sync there first because .venv/bin/python was missing.

2. Exact command launched

bash

cd /Users/priyangapkini/nilenso/ai-playground/autoresearchset -asource .envset +aBOTMAP_REPO=/Users/priyangapkini/workspace/ar-b-new/botmap \  uv run python -m autoresearch.optimize \    --lever prompt \    --budget 60 \    --keep-runs

Launched in background with output redirected to:

3. Run dir / PID / status

PID:

Status:

Run dir:

Current files:

4. Comparability notes

Preflight passed and logged:

text

tool: /Users/priyangapkini/workspace/ar-b-new/botmap @ 3009509map data: NOT pinned — release 2026-08-19.0, matching the cached baselinelink to map data: 6/6 test calls ok, median 9s, spread 1.8xproposals from: openrouter/anthropic/claude-opus-5 (OpenRouter balance $61.34)lever 'prompt' covers 1 file(s): botmap/data/skill.md25 train questions, 5 held outreusing yardstick measured earlier for 3009509budget: 60 evaluations, 2 repeats each

Estimated scale/cost:

60 GEPA evaluations × 2 repeats = ~120 agent attempts.
Based on Phase 4 actual cost, expect roughly $30–35 for agent attempts, plus proposer/reflection cost.
OpenRouter balance at launch: $61.34.

No blockers at launch.

You are Arm B. STOP WORK NOW: experiments are halted by user request.

Do not launch or resume any paid run. Do not run GEPA. Do not run autoresearch.runner. Do not run botmap evals. Do not modify code.

The orchestrator has already terminated the active background experiment processes at 2026-08-25T16:42:21Z to stop spend. Treat any incomplete run as intentionally stopped, not failed.

Your task now is only to write an insights report from your arm's work so far.

Write exactly one file in your arm directory:

Arm A: experiments/arm-a/INSIGHTS.md
Arm B: experiments/arm-b/INSIGHTS.md
Arm C: experiments/arm-c/INSIGHTS.md
Arm D: experiments/arm-d/INSIGHTS.md

Report structure:

What your arm was testing.
What ran / did not run, with run dirs and commits where known.
Key quantitative results so far.
Qualitative insights about agent-friendly CLI design.
What claims are supported vs tentative.
Failure modes / confounders / invalid runs.
Recommendation for next experiments.
Anything the blog/derivation sessions should know.

Be candid. Do not overclaim. If results are incomplete or enrichment failed, say so clearly. After writing the file, reply here with the file path and a 5-bullet executive summary.

Loading more messages…

Summary

Readiness report — Arm B

Current state inspected

Phase 0a implementation plan — skill isolation

Phase 1 implementation plan — agenteval/contract.py

Dirty worktree concerns

Hazards

Questions for Priyanga/orchestrator

1. Current wiring status

2. Prompt-lever run plan once wiring is ready

3. Estimated cost

4. Current state/hazards

1. Gates

2. Exact command launched

3. Run dir / PID / status

4. Comparability notes

Summary

Readiness report — Arm B

Current state inspected

Phase 0a implementation plan — skill isolation

Phase 1 implementation plan — agenteval/contract.py

Dirty worktree concerns

Hazards

Questions for Priyanga/orchestrator

1. Current wiring status

2. Prompt-lever run plan once wiring is ready

3. Estimated cost

4. Current state/hazards

1. Gates

2. Exact command launched

3. Run dir / PID / status

4. Comparability notes

Phase 1 implementation plan — `agenteval/contract.py`

Phase 1 implementation plan — `agenteval/contract.py`