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Synthesized CLI agent-friendly principlesPublic

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GPT-5.5

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Aug 21, 12:32 PM

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nilenso/ai-playground

Summary

In Progress

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Summarized experimental results in insights document.
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Synthesized core principles from multiple experiment sources.
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You are PI ARM A for the autoresearch experiment. You replace the old Claude Arm A 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-a/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/decisions-are-the-users.md
/Users/priyangapkini/.claude/projects/-Users-priyangapkini-nilenso-ai-playground-autoresearch/memory/botmap-cache-is-shared-and-unpinned.md

ARM A old workspace:

/Users/priyangapkini/workspace/ar-a/botmap
skills: /Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-loop/SKILL.md, autoresearch-propose/SKILL.md, autoresearch-verify/SKILL.md

Important current facts:

Global Claude botmap skill is intentionally disabled at ~/.claude/botmap-skill-DISABLED-BY-ARM-A because it shadowed project-scoped botmap/data/skill.md and made prompt-lever experiments void. Read WHY-MOVED.txt. Do not restore it unless orchestrator/user tells you.
The new plan says no one starts component work until contract.py is merged. Parent/orchestrator is handling common blocking changes. Your role now is context reconstruction and readiness.
The final deliverable is docs/agent-friendly-cli.md entries backed by measurements.

Your immediate task:

Read the files above.
Inspect old Arm A skills and handover.
Prepare a concise readiness report: what Arm A owns under the new plan (sabotage.py, explain.py, class F detection, docs evidence entries), what context from old Arm A matters, hazards, exact files/paths, and questions that need Priyanga/orchestrator decision.
Do NOT edit project/source files yet. Do NOT spend model/API quota by launching evals. Do NOT restore the global botmap skill.

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

READY

Arm A readiness report

Current operating constraints

I have read:
- docs/plan.md
- experiments/arm-a/HANDOVER.md
- memory files:
  - autoresearch-three-arm-experiment.md
  - decisions-are-the-users.md
  - botmap-cache-is-shared-and-unpinned.md
- old Arm A skills:
  - /Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-loop/SKILL.md
  - /Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-propose/SKILL.md
  - /Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-verify/SKILL.md
- global skill disable breadcrumb:
  - /Users/priyangapkini/.claude/botmap-skill-DISABLED-BY-ARM-A/WHY-MOVED.txt
I will not edit project/source files until orchestrator says contract work has landed and Arm A is released.
I will not run evals or spend model/API quota.
I will not restore ~/.claude/skills/botmap.

What Arm A owns under the new plan

Blocked until autoresearch/agenteval/contract.py is merged by Arm B.

Once unblocked, Arm A owns:

autoresearch/agenteval/sabotage.py
- Fixtures per class A–F.
- Fixtures per class-C subtype:
  - c-vocabulary
  - c-wrong-column
  - c-wrong-type
  - c-dropped-input
  - c-truncated
  - c-wrong-entity
  - c-unknown
- Fixtures should be drawn from real recorded traces, especially:
  - experiments/failure_dataset.yaml
  - /Users/priyangapkini/workspace/ar-a/botmap/notes/findings.md
  - existing retained attempts from baseline-noise-run1-3009509 once orchestrator identifies path.
autoresearch/agenteval/explain.py
- Convert verdicts/probe evidence into actionable feedback text for proposer.
- Target shape from plan:
  - class/subtype
  - command run
  - result
  - probe evidence
  - missing CLI behaviour / fix instruction
Class F / agent-side detection
- Detect “hint offered at call N, not used by N+1”.
- Record as agent-side, not tool charge.
- Old C1 trace is important evidence: near-match hint fired, agent ignored once, acted late, final answer still wrong.
docs/agent-friendly-cli.md evidence entries
- Final deliverable entries only when backed by measurements.
- Each entry must include PROPERTY, VIOLATION, STATUS, BEFORE, CHANGE, AFTER, VERDICT.
- For now, Arm A can prepare candidate mappings, but should not claim confirmation without paired before/after runs.

Old Arm A context that matters

Candidate branches / commits in old workspace

Old repo: /Users/priyangapkini/workspace/ar-a/botmap

Important branches/commits:

cand/count-zero-hint
- measured commit: 99d993f
- branch head: 9a2496d, post-review fix, not re-measured
- Relevant to new plan:
  - fixture for class B / recovery hints
  - paired Tier-2 experiment for “Name the fix, not just the problem”
  - also class F evidence when hint is ignored
cand/skill-bus-station
- commit 6c04003
- prompt/instruction lever changing botmap/data/skill.md
- Relevant to new plan:
  - paired Tier-2 experiment for instruction-side fix
  - beware global skill shadowing made prompt-lever experiments void unless isolated
cand/count-flag-parity
- commit 05ef72c
- tool lever: --category / --class shortcuts on count
- Relevant mainly as “mechanism verified but agent used it zero times”.
arm-a-base
- contains notes/sync-candidates.sh guard and verify-skill rewrite.
- Guard matters if old candidate branches are reused.

Important evidence files

/Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/failure_dataset.yaml
- Maps 26/30 questions to known defects.
- Best starting point for sabotage fixture coverage.
/Users/priyangapkini/workspace/ar-a/botmap/notes/findings.md
- Contains Arm A findings F1–F15.
- Fragile: old handover says notes/ is excluded and untracked in that working tree.
/Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/artifacts/findings.md
- Mentioned as source for failure dataset evidence.
/Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/proposals.json
- Proposal IDs referenced by failure_dataset.yaml.

Hazards

Decision authority
- Priyanga withdrew autonomous decision-making.
- Arm A should report questions/options, not decide changes, weights, runs, or experimental interpretation.
Global botmap skill
- Disabled at:
  - ~/.claude/botmap-skill-DISABLED-BY-ARM-A
- Reason:
  - It shadowed project-scoped botmap/data/skill.md, making prompt-lever experiments void.
- Do not restore unless orchestrator/user explicitly says so.
No component work before contract
- Plan explicitly says nobody starts until contract.py is merged.
- Arm A should wait for the contract API before writing sabotage.py, explain.py, or class-F code.
Do not touch botmap/evals/
- New evaluator lives under autoresearch/agenteval/.
- Existing runs/records are history; do not rewrite.
Cache is shared and unpinned
- ~/.cache/botmap/divisions-index-<release>.parquet is user-level.
- Release rollover invalidates baselines silently.
- Concurrent rebuilds can corrupt cache.
- One orchestrated cache builder only.
Quota/network failures contaminate measurements
- Old Arm A found curlCode 28 / quota exhaustion can look like broken candidates.
- No evals until orchestrator says measurement conditions are valid.
Old C1 branch ambiguity
- Round 1 measured 99d993f.
- Branch head 9a2496d has an unverified post-review fix.
- Any future paired run must state exactly which commit is being tested.

Questions needing Priyanga/orchestrator decision

For Arm A sabotage fixtures, should I use only already-committed autoresearch artifacts, or may I reference old untracked Arm A notes under /Users/priyangapkini/workspace/ar-a/botmap/notes/?
For C1 count-zero-hint, which commit should future paired experiments treat as the candidate?
- measured 99d993f
- unmeasured branch head 9a2496d
- rebuild fresh from the new shared contract
For class F detection, should “hint ignored by N+1” be strict one-call-only, or should we record richer windows like “ignored for next K calls, eventually used”?
Should Arm A prepare docs/agent-friendly-cli.md hypothesis stubs before paired runs, or wait until AFTER traces exist?
Where are the 60 retained attempts from baseline-noise-run1-3009509 located, and should Arm A use them as fixture sources once unblocked?

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\nContinue to wait for contract.py before component edits. You may refine your readiness notes if this changes them; do not edit source files yet.

READY — updated with decisions.

Updated Arm A readiness notes

Confirmed decisions incorporated

Phase 0a validation
- Approved for Arm B/common path: two cheap Claude probe calls after code change.
- Arm A still should not run evals or probes unless assigned.
Clean call representation
- In record-v2, clean calls use JSON:
  - "class": null
Old Arm A notes
- Before using old untracked Arm A notes as fixture evidence, preserve/copy them into autoresearch.
- Important source to preserve:
  - /Users/priyangapkini/workspace/ar-a/botmap/notes/findings.md
- I will not perform that copy yet because current instruction is still no source/project edits.
C1 candidate identity
- Not decided.
- I will not act on whether C1 means 99d993f, 9a2496d, or a fresh rebuild until discussed further.
Ignored-hint / class F detection
- Use strict boolean plus richer details/window.
- Shape to implement after contract lands:
  - boolean: hint offered at call N and not used at N+1
  - detail: what hint, next command(s), whether eventually used, how many calls later, final outcome
Quota failures
- Contract should include attempt-level verdict.
- Quota/session-limit failures can be class E at attempt level, outside per-call CallVerdict.
Exit-0 did you mean / recovery hints
- Preserve recovery semantics.
- A guided hint is useful tool behaviour.
- If the agent does not recover:
  - per-call should still show guided signal
  - agent-side failure detail records non-use / misuse
- Do not collapse this into a generic failure that erases the guidance path.

Arm A remains blocked

No component edits until autoresearch/agenteval/contract.py is merged.

Once unblocked, Arm A owns:

autoresearch/agenteval/sabotage.py
autoresearch/agenteval/explain.py
class-F / ignored-hint detection
evidence-backed docs/agent-friendly-cli.md entries after measurements exist.

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.
Old Arm A findings are already preserved in autoresearch at experiments/arm-a/notes/findings.md and are identical to /Users/priyangapkini/workspace/ar-a/botmap/notes/findings.md.

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

autoresearch/agenteval/sabotage.py
autoresearch/agenteval/explain.py
autoresearch/agenteval/agent_side.py if you need a separate helper for class-F/ignored-hint detection
tests/test_sabotage.py, tests/test_explain.py, tests/test_agent_side.py

Do not touch botmap/evals. Do not launch evals or spend model/API quota. Use recorded/static fixtures only. Follow docs/plan.md exactly. Use contract types/functions; clean class is null; quota is attempt-level verdict; ignored-hint detection should provide strict boolean plus rich detail/window; exit-0 did-you-mean should preserve guided recovery path and agent-side non-use detail.

Goal for this pass: implement minimal but real Arm A components and tests from recorded traces/failure_dataset. If blocked by missing taxonomy/probe APIs, create small pure functions and fixtures that can be integrated later, and state the integration contract. Report READY or BLOCKED with tests run and decisions needed.

READY

Implemented minimal Arm A Phase 2 components using only allowed files:

autoresearch/agenteval/agent_side.py
- detect_ignored_hints(calls, window=3)
- Records strict ignored_by_next boolean plus richer window/details:
  - suggestions
  - next argv
  - window argv
  - eventually used / used_at_call
- Preserves guided recovery semantics; non-use is separate agent-side detail.
autoresearch/agenteval/explain.py
- explain(record)
- Produces fix-instruction style feedback for classed calls, probes, attempt verdicts, and agent-side records.
- Explicitly preserves Class B guided recovery path.
autoresearch/agenteval/sabotage.py
- Static FIXTURES covering classes A–F and every class-C subtype.
- Sources cite recorded/static evidence:
  - experiments/failure_dataset.yaml
  - experiments/arm-a/notes/findings.md
  - retained run traces under experiments/runs/baseline-noise-run1-3009509
  - docs/plan.md
- Provides:
  - run_sabotage(classifier)
  - assert_sabotage_passes(classifier)
  - expected_verdict(call) as temporary reference classifier until taxonomy/probe integration lands.

Tests added:

tests/test_agent_side.py
tests/test_explain.py
tests/test_sabotage.py

Tests run:

Integration contract for later Arms B/C:

Taxonomy classifier can be plugged into assert_sabotage_passes(classifier).
Classifier input expected: fixture call: dict.
Classifier output accepted as either:
- CallVerdict, using .cls / .subtype
- dict, using "class" or "cls" plus "subtype"
Probe output should populate call["probes"] as record-v2-compatible dicts.

You are PI ARM A. The evaluator-first work is now in the shared autoresearch checkout. Your role now: adapt/run the loop-as-a-skill arm using the NEW evaluator, not the old botmap eval scorer.

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/experiments/paired/categories-truncation-hint/result.md
/Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/paired/count-wrong-column-hint/result.md
/Users/priyangapkini/nilenso/ai-playground/autoresearch/autoresearch/agenteval/*
old Arm A workspace: /Users/priyangapkini/workspace/ar-a/botmap and .claude/skills/autoresearch-*

Constraints:

Do not talk to other arms. Report only here.
Do not edit shared autoresearch code unless you ask and receive orchestrator approval.
Do not restore global ~/.claude/skills/botmap.
Use the new evaluator artifacts: record-v2, sabotage gate, explain feedback, class histograms.
No GEPA. Arm A remains the skill-driven loop thesis.

Task:

Update your Arm A plan for how the loop-as-a-skill should use the new evaluator.
Identify the minimal changes needed to old Arm A skills so candidates are judged by record-v2/agenteval summaries and docs/agent-friendly-cli entries.
If the changes are only under /Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-* or notes, you may make them. Do not run paid evals yet.
Produce a READY report with: changed files, tests/checks, exact command to launch first low-cost loop-as-skill experiment using new evaluator, estimated cost, and any blocker.

READY

Changed files

Old Arm A workspace only:

/Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-loop/SKILL.md
/Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-propose/SKILL.md
/Users/priyangapkini/workspace/ar-a/botmap/.claude/skills/autoresearch-verify/SKILL.md
/Users/priyangapkini/workspace/ar-a/botmap/notes/arm-a-new-evaluator-plan.md
- note: notes/ appears excluded in this old workspace, so preserve/copy later if needed.

No shared autoresearch source edited.

What changed conceptually

Arm A loop now uses:

record-v2.json
autoresearch.agenteval.explain
agenteval-summary*.json
sabotage/probe/taxonomy gate
matched class/subtype deltas
docs/agent-friendly-cli.md entries

It no longer uses:

botmap/evals
old record.json
evals.score
cli_error_count
GEPA

Checks run

Result:

Exact first low-cost loop-as-skill launch command

Proposal/mechanism-only; no paid evals:

bash

cd /Users/priyangapkini/workspace/ar-a/botmapclaude -p 'Use the autoresearch-loop skill for one Arm A round with the NEW shared autoresearch record-v2/agenteval evaluator. Run the free evaluator gate, read the shared Phase 4 summary and paired-result artifacts, propose 2-3 candidates, and run only free/static mechanism checks if safe. Do not run GEPA. Do not touch botmap/evals. Do not run paid evals or autoresearch runner.ask_repeatedly. Do not restore the global botmap skill. Stop with candidates, checks, proposed paired subset, exact paid command, and cost estimate.' \  --setting-sources project \  --permission-mode bypassPermissions

Estimated cost: one Claude/Pi planning session only; $0 eval cost. If routed through paid model API, expect roughly <$0.50. Mechanism checks are CLI/static only.

Blockers / decisions

Any paid paired run still needs orchestrator approval of candidate, subset, repeats, and cost.
Old C1/count-zero-hint identity remains undecided; skills now explicitly avoid using it until Priyanga decides.
If claude -p cannot run because the old subscription path is unavailable, run the same prompt through Pi/orchestrator instead; the skill files are ready.

RELEASED: start Arm A run now.

User explicitly said: “let arm A run it”. You are authorized to run a low-cost Arm A experiment using the NEW autoresearch evaluator.

Hard constraints:

Do NOT use botmap/evals or botmap/evals/runner.py or botmap/evals/score.py.
Use the shared autoresearch evaluator path: autoresearch.runner -> record-v2.json -> autoresearch.agenteval.enrich/analyze/score/explain.
Use your isolated botmap repo: /Users/priyangapkini/workspace/ar-a/botmap.
Do not restore ~/.claude/skills/botmap. If it exists, stop and report.
Arms cannot talk to each other. Report only here.
Keep spend low. Do not run GEPA. This is Arm A loop-as-a-skill/candidate screening with the new evaluator.
Preserve original artifacts; write a new run dir under /Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/runs/ with an arm-a prefix.

Suggested scope: Measure your existing Arm A candidates through new evaluator:

cand/count-zero-hint
cand/skill-bus-station
cand/count-flag-parity against a small targeted subset from the autoresearch question bank. Prefer questions related to your mechanisms and known failures, e.g.:
bus-stops-cambridge
bike-parking-coverage
basic-category-rollup
residential-share-cambridge
williamsburg-which-one or bus-stops-with-coffee Use 1 repeat unless you have a strong reason to do 2.

Implementation guidance:

Write a small script in your Arm A workspace or /tmp that imports autoresearch.config, runner, orproxy, questions, and runs runner.ask(question, repo, repeat, keep_dir=...).
For each candidate branch, checkout the branch in /Users/priyangapkini/workspace/ar-a/botmap, run the small subset, then enrich/analyze records using autoresearch.agenteval.enrich.enrich_run and autoresearch.agenteval.analyze.write_summary.
Record candidate commit sha, branch, question ids, repeat count, completed/ok/cost/calls.
Use OpenRouter pinned provider the same way previous orchestrator measurement scripts did if needed.
If a branch does not start, record that as an attempt-level/candidate-level failure, do not patch it unless it is only a measurement-script bug.

Dashboard:

If simple, write summary.json/progress.jsonl so the existing dashboard progress cards can read it. If not simple, report run dirs and I’ll wire dashboard.

Deliverable: When done or blocked, report:

Run directories.
Candidate commits measured.
Question ids and repeats.
Summary tables from agenteval-summary.json.
Whether any Arm A candidate improves under the new evaluator.
Cost and duration.
Any failures that should be retried.

Start now.

RELEASED: start Arm A full run now.

User correction: this does NOT need to be low-cost. Run a full Arm A candidate screening using the NEW autoresearch evaluator.

Hard constraints:

Do NOT use botmap/evals or botmap/evals/runner.py or botmap/evals/score.py.
Use autoresearch.runner -> record-v2.json -> autoresearch.agenteval.enrich/analyze/score/explain.
Use your isolated botmap repo: /Users/priyangapkini/workspace/ar-a/botmap.
Do not restore ~/.claude/skills/botmap. If it exists, stop and report.
Arms cannot talk to each other. Report only here.
Do not run GEPA. This is Arm A loop-as-a-skill / existing-candidate screening with the new evaluator.
Preserve artifacts; write new run dirs under /Users/priyangapkini/nilenso/ai-playground/autoresearch/experiments/runs/ with arm-a prefixes.
Stop and report if preflight finds quota exhaustion, severe network failure, candidate import failure, or if a branch cannot start.

Scope: Measure all existing Arm A candidates through the new evaluator:

cand/count-zero-hint
cand/skill-bus-station
cand/count-flag-parity

Use the full autoresearch 30-question bank, 2 repeats each, unless a hard preflight failure makes that invalid. Expected size: 3 candidates x 30 questions x 2 repeats = 180 attempts. This is authorized by the user; do not downscope merely to save cost.

Implementation guidance:

Write/run a script in your Arm A workspace or /tmp that imports autoresearch.config, runner, orproxy, questions.
For each candidate branch:
1. checkout branch in /Users/priyangapkini/workspace/ar-a/botmap
2. record branch + commit sha
3. run every question, repeats 1 and 2, using runner.ask(question, repo, repeat, keep_dir=...)
4. write summary.json and progress.jsonl continuously
5. enrich with autoresearch.agenteval.enrich.enrich_run
6. summarize with autoresearch.agenteval.analyze.write_summary
Use OpenRouter pinned provider as previous orchestrator measurement scripts did.
Record attempt-level environment/quota failures as verdicts, not CLI failures.
Clean record-v2 calls use class: null.
Retry incomplete/network/timeouts only after preserving original artifacts.

Recommended run dir naming:

experiments/runs/arm-a-new-evaluator-count-zero-hint-/
experiments/runs/arm-a-new-evaluator-skill-bus-station-/
experiments/runs/arm-a-new-evaluator-count-flag-parity-/

Dashboard:

Write progress.jsonl and summary.json compatible with existing dashboard run_progress_state.
If dashboard wiring is needed, report run dirs and orchestrator will wire it.

Deliverable: When done or blocked, report:

Run directories.
Candidate commits measured.
Question ids and repeats.
Summary tables from agenteval-summary.json.
Comparison against baseline measurement summary where meaningful.
Whether any Arm A candidate improves under the new evaluator.
Total cost and duration.
Any failures that should be retried.

Start now.

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