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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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Repo

nilenso/ai-playground

Summary

Closed, Completed

Understand
Reviewed prior experiment runs and documentation.
Understand
Build
Synthesized general principles from experiment data.
Build
Edit
Documented top principles for CLI agent-friendly behavior.
Edit

You are PI ARM C for the autoresearch experiment. You replace the old Claude Arm C 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-c/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

Old Arm C worktree:

/Users/priyangapkini/nilenso/ar-c-autoresearch on branch feat/agent-struggle-scorer

Important current facts:

The new plan assigns Arm C: taxonomy.py (three-axis classifier and class derivation via contract), score.py (class-weighted scoring with TODO weights), class E network/quota handling, and proving class B fix with count-zero-hint.
No one starts component work until contract.py lands. Parent/orchestrator is handling common blocking changes.
Previous Arm C has useful commits but they were superseded by docs/plan.md; mine concepts carefully, do not blindly merge.

Your immediate task:

Read the files above.
Inspect old Arm C worktree status/log and relevant files, without modifying them.
Prepare a concise readiness report: which old Arm C ideas/commits map to the new evaluator plan, what Arm C should implement after contract.py, validation strategy, hazards, 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

Arm C readiness report

Files read / status checked

Read docs/plan.md, Arm C handover, and both memory notes.
Inspected old Arm C worktree only; no edits made.
Old worktree:
- Path: /Users/priyangapkini/nilenso/ar-c-autoresearch
- Branch: feat/agent-struggle-scorer
- Status: clean
- Head: 60d5de8 Add. the exam cannot detect what it was built to detect
Current shared checkout has no autoresearch/agenteval/contract.py yet, so Arm C remains blocked per plan.

Useful old Arm C work that maps to new plan

Directly mineable

Class B / hint-not-punished fix
- Commit: 7fc43f4 Fix. the score pointed at deleting the tool's own diagnostics
- Old idea: distinguish “Did you mean” diagnostics from silent bad values.
- New mapping:
  - error + guided → class B
  - should be near-free
  - should satisfy recovery
  - proves count-zero-hint must score better than baseline.
Class E network handling
- Commit: 0118512 Fix. network timeouts scored as if the tool had crashed
- Old idea: specific network markers before traceback classification.
- New mapping:
  - blame=environment → class E
  - dropped from scoring.
- Need adapt from string label network_failure into axis verdict.
Quota exhaustion handling
- Commit: da831d7 Fix. quota exhaustion scored as a candidate that broke the tool
- Old idea: detect Claude subscription quota at transcript/result-event layer and abort instead of scoring zero.
- New mapping:
  - class E, but probably at attempt/transcript level, not Call, because quota may produce no botmap call.
- Existing markers: confirmed "session limit"; "usage limit" / "rate limit" are unconfirmed guesses.
Silent-failure weighting concept
- Commit: ab5e899 Add. agent-struggle terms to the score
- Old idea: silent wrong answers should dominate visible failures.
- New mapping:
  - class C gets heaviest penalty.
  - old silent term becomes class-C penalty logic, now driven by probes/subtypes rather than brittle stderr matching.
Completion gate / effort terms
- Old tests around “giving up must not beat answering”, wasted commands, path length, recovery.
- New mapping:
  - useful test shape for score.py: effort must not reward no-answer attempts.
  - class D degenerate can reuse path/waste proportional scoring ideas.

Useful but no longer central

Noise-floor work (f6a7bd0, 0df3068, e039e09, 60d5de8) is evidence for why the old scorer/question bank saturated. New plan explicitly expects class C detection may restore headroom, so this becomes background context, not implementation direction.
Whole-repo GEPA background (1ee5bd2) is superseded by the evaluator-first plan; do not merge into new work unless orchestrator decides.

What Arm C should implement after `contract.py` lands

autoresearch/agenteval/taxonomy.py
- classify(call, probes) -> CallVerdict
- Use contract.derive_class(...); do not compute/store class independently.
- Implement axes:
  - outcome: ok | empty | error | degenerate
  - blame: tool | agent | environment
  - recovery: guided | unguided | n/a
- Class C subtypes from probe evidence:
  - c-vocabulary
  - c-wrong-column
  - c-wrong-type
  - c-dropped-input
  - c-truncated
  - c-wrong-entity
  - c-unknown
- Move old network/quota concepts into class E handling.
autoresearch/agenteval/score.py
- Class-weighted scoring.
- Drop E from scoring.
- Record/report F but do not charge it to tool.
- B near-free and counts as recovery.
- C dominates.
- Leave weights as named constants with TODO; no tuning before phase 5.
Prove class-B fix using Arm A’s count-zero-hint
- Under new scorer, hint candidate must score better than baseline.
- Specifically guard against the old inversion where diagnostics made the candidate worse.

Validation strategy

No model/API quota; all local/unit/recorded-data only until orchestrator authorizes otherwise.

Tests to build/adapt:

Contract compatibility once available:
- classifier returns CallVerdict
- class is derived through contract.derive_class
- disagreeing stored class is rejected by contract validator, not trusted.
Class derivation table:
- A: error + unguided
- B: error + guided
- C: empty + unguided
- D: degenerate
- E: blame=environment
- F: blame=agent
B regression:
- “Did you mean” / near-match hint is B, not C/A.
- hinted candidate scores better than silent zero baseline.
- helpful hint is not double-charged as both failure and wasted path.
E regression:
- network traceback markers classify as environment, not tool traceback.
- quota transcript produces environment/unmeasurable handling and no candidate zero.
C subtype tests:
- use Arm B probe fixtures once probe.py exists.
- if no conclusive probe: c-unknown, never clean.
Scoring tests:
- E dropped.
- F recorded not charged.
- C worse than several B/A-style visible recoverable failures.
- no-answer/give-up cannot beat completed answer.

Hazards

Do not blindly merge old branch. Old scorer uses labels and old score.py; new plan requires agenteval/ contract and axes.
Quota is probably transcript-level, not call-level. Need contract/orchestrator guidance on where attempt-level class E lives.
Network marker false positives. Old implementation used deliberately specific strings; keep that caution.
Class C must be probe-driven. Old 0 rows + categories.primary heuristic is too narrow and risks recreating invisible failures.
Weights must not be tuned now. Plan says named constants with TODO until class distribution is observed.
No component work before contract.py. Current checkout confirms contract is not present yet.
No eval/model spend. Validation must stay local/recorded-data-only until gate passes.

Questions for Priyanga / orchestrator

For quota exhaustion: should Arm C model it as an attempt-level class E outside CallVerdict, or should contract.py expose an attempt verdict as well?
Should taxonomy own only per-call classification, with agent-side class F supplied by Arm A later, or should taxonomy.py also expose helpers for F once Arm A detects ignored hints?
For class B, should outcome remain error even when old botmap exits 0 but emits “Did you mean” on stderr, or should taxonomy.py normalize that as an effective refused/guided outcome?
Which old Arm C tests should be ported first: B inversion, network E, quota E, or scoring gates?
Should old network_failure markers be accepted as-is initially, or should Priyanga review the marker list before they enter the shared evaluator?

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 readiness notes from Priyanga/orchestrator decisions:

Still blocked on contract.py; no source edits until it lands.
Clean record-v2 calls should serialize with JSON "class": null, not "clean" / "A-F".
Quota handling:
- contract.py should include an attempt-level verdict.
- Arm C should classify quota exhaustion as attempt-level class E, outside per-call CallVerdict.
Exit-0 did you mean / recovery hints:
- Preserve the recovery path.
- Treat the guided signal as usable by the agent’s next step.
- If the agent ignores it, record agent-side failure detail rather than flattening it into tool failure.
Ignored-hint detection:
- Arm A owns detection, but Arm C scoring should expect option C shape: strict boolean plus richer detail/window.
- Class F remains recorded, not charged.
Fixture evidence:
- Do not rely on old untracked Arm A notes until they are first copied/preserved into autoresearch.
- Once preserved, they may support fixtures.
C1 identity:
- Not decided; do not act on or assume C1 identity.
- For Arm C, keep the acceptance concept generic: “a count-zero-hint fixture/candidate, once identified by orchestrator, must score better than silent baseline.”
Phase 0a:
- Approved after code change, but owned by Arm B. Arm C should not spend those calls.

No edits made; awaiting contract.py.

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 C Phase 2 work. You may edit ONLY these files unless you ask first:

autoresearch/agenteval/taxonomy.py
autoresearch/agenteval/score.py
tests/test_agenteval_taxonomy.py, tests/test_agenteval_score.py

Do not modify old autoresearch/taxonomy.py or autoresearch/score.py in this pass unless you ask first; the new evaluator lives under autoresearch/agenteval/. Do not launch evals or spend model/API quota. Mine old Arm C branch for ideas but do not merge blindly. Follow docs/plan.md exactly: axes, class via contract.derive_class only, E dropped, F recorded-not-charged, B near-free and satisfies recovery, C dominates, weights named constants with TODO and no tuning.

Important decisions: clean class null; quota is attempt-level verdict; exit-0 did-you-mean preserves guided recovery path and agent-side non-use detail; C1 candidate identity is unresolved, so use generic fixtures rather than assuming a commit.

Goal for this pass: implement taxonomy.py and score.py minimal real versions with tests, including B inversion, E network/quota handling shape, C unknown fallback, and scoring ordering. Report READY or BLOCKED with tests run and decisions needed.

READY

Implemented minimal Arm C Phase 2 files only:

autoresearch/agenteval/taxonomy.py
autoresearch/agenteval/score.py
tests/test_agenteval_taxonomy.py
tests/test_agenteval_score.py

What landed:

Three-axis call classifier using contract.derive_class only.
Clean calls produce cls=None.
Exit-0 did you mean becomes guided class B recovery signal.
Network failures classify as class E.
Quota exhaustion handled as attempt-level class E via classify_attempt.
Empty unguided calls fall back to class C / c-unknown.
Probe evidence maps to C subtypes.
Scorer:
- E excluded/dropped.
- F recorded not charged.
- B near-free.
- C dominates.
- weights are named constants with TODO, not tuned.

Validation:

uv run pytest tests/test_agenteval_taxonomy.py tests/test_agenteval_score.py tests/test_contract.py
- 19 passed
uv run pytest
- 103 passed

No evals launched. No model/API quota spent.

Decision still needed later:

Integration shape: when/how the new agenteval.score.score_attempt is wired into record-v2 writing/evaluator flow.

You are PI ARM C. The evaluator-first work is now in the shared autoresearch checkout. Your role now: wire the NEW evaluator into the optimizer/evaluator flow so all future arms actually use it.

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, score.py, runner.py, baseline.py, optimize.py

Your clean botmap repo for future tool/repo-context runs:

/Users/priyangapkini/workspace/ar-c-new/botmap @ 3009509

Constraints:

You may edit shared autoresearch code for evaluator wiring and tests, but be the only writer for those files. Do not touch botmap/evals.
Do not launch paid runs yet. Local tests only.
Preserve legacy record/history where plan requires it.

Task:

Wire future evaluation feedback/objective to the new agenteval modules enough that Arm B/A can use the new evaluator. Prefer small, testable changes.
Ensure sabotage gate can be run before optimizer runs.
Ensure GEPA feedback can use agenteval.explain or equivalent record-v2 explanations.
Add tests and run uv run pytest.
Report READY/BLOCKED with changed files, commands run, and exact behavior now changed.

READY

Changed files:

autoresearch/agenteval/score.py
autoresearch/evaluator.py
autoresearch/baseline.py
autoresearch/config.py
autoresearch/optimize.py
tests/test_agenteval_score.py
tests/test_evaluator_agenteval.py
tests/test_optimizer_gate.py

Commands run:

uv run pytest
- 138 passed in 0.19s

Behavior now changed:

Future GEPA candidate evaluation now scores via autoresearch.agenteval.score, from record-v2 verdicts.
Evaluator feedback now logs agenteval.explain(record-v2) output, so GEPA sees class/subtype/evidence-based feedback.
Attempt-level quota/environment failures are excluded instead of charged to candidates.
Incomplete non-environment attempts score 0.0, avoiding fast/no-answer credit.
Baseline summaries now use agenteval correctness/recoverability.
config.CORRECTNESS_IMPL is now agenteval-v2, preventing silent comparison with old proxy-v1 histories.
Optimizer runs the sabotage gate before config.preflight and before GEPA, so failure detection is checked before paid work.

No evals launched. No model/API quota spent. No botmap/evals touched.

Decisions needed: none for this wiring pass.

Orchestrator update: user explicitly requested full repo context and full edit permissions for the optimizer / Arm C.

I have committed this support in shared autoresearch:

686cce1 Allow full repo optimizer context and edits

New behavior:

--all-files now means every tracked UTF-8 text file in the botmap repo is editable by GEPA.
--full-repo-context adds a bounded read-only snapshot of the tracked botmap repo to GEPA background.
Verified against /Users/priyangapkini/workspace/ar-c-new/botmap: 97 editable tracked text files.
Tests pass: uv run pytest -> 140 passed before your uncommitted wiring edits are included.

Use this target command shape for future full-repo optimizer runs, but do NOT launch paid runs yet:

cd /Users/priyangapkini/nilenso/ai-playground/autoresearch BOTMAP_REPO=/Users/priyangapkini/workspace/ar-c-new/botmap
uv run python -m autoresearch.optimize
--lever tool
--all-files
--full-repo-context
--budget
--keep-runs

I see your uncommitted shared changes:

autoresearch/agenteval/score.py
autoresearch/baseline.py
autoresearch/evaluator.py
tests/test_agenteval_score.py
tests/test_evaluator_agenteval.py
tests/test_optimizer_gate.py

Please continue wiring new evaluator usage on top of 686cce1. Do not revert the full-repo optimizer commit. Run uv run pytest when ready and report READY/BLOCKED with changed files and behavior.

READY

Continued on top of 686cce1; did not revert full-repo optimizer support.

Changed files:

autoresearch/agenteval/score.py
autoresearch/baseline.py
autoresearch/evaluator.py
tests/test_agenteval_score.py
tests/test_evaluator_agenteval.py
tests/test_optimizer_gate.py

Commands run:

uv run pytest tests/test_evaluator_agenteval.py tests/test_agenteval_score.py tests/test_optimizer_gate.py
- 15 passed
uv run pytest
- 141 passed

Behavior now changed/confirmed:

GEPA evaluator scoring uses record-v2/agenteval scoring via score_record.
GEPA feedback logs agenteval.explain(record) so proposer sees class/subtype/evidence feedback.
Environment/quota attempts are excluded, not charged to candidates.
Baseline summaries now use agenteval correctness/recoverability and drop environment attempts from baseline readings.
Incomplete non-environment attempts score 0.0.
Sabotage gate remains wired before optimizer preflight/paid GEPA path; tested.
Full repo optimizer flags from 686cce1 remain intact.

No paid runs launched.

RELEASED: launch Arm C.

User explicitly said: “launch arm c”. You are authorized to finish your evaluator wiring and launch the Arm C optimizer run.

Arm C purpose:

Full repo context + full edit permissions optimizer arm.
Use the NEW autoresearch evaluator, not legacy scoring.

Repo/state:

Shared autoresearch checkout: /Users/priyangapkini/nilenso/ai-playground/autoresearch
Clean botmap repo for Arm C optimizer: /Users/priyangapkini/workspace/ar-c-new/botmap @ 3009509
Full repo optimizer support is committed in shared autoresearch: 686cce1 Allow full repo optimizer context and edits

Hard constraints:

Do NOT touch botmap/evals manually.
Do NOT restore ~/.claude/skills/botmap.
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, or baseline/release comparability is invalid.
Preserve all artifacts; use new run dirs.

Before launch:

Finish your pending evaluator wiring in shared autoresearch if needed.
Run: cd /Users/priyangapkini/nilenso/ai-playground/autoresearch uv run pytest
Confirm sabotage gate passes.
Confirm optimizer help shows --all-files and --full-repo-context.

Launch command shape:

cd /Users/priyangapkini/nilenso/ai-playground/autoresearch set -a; source .env; set +a BOTMAP_REPO=/Users/priyangapkini/workspace/ar-c-new/botmap
uv run python -m autoresearch.optimize
--lever tool
--all-files
--full-repo-context
--budget 60
--keep-runs

Notes:

This is not low-cost; user explicitly removed that constraint.
If you believe budget 60 is invalid for 97 editable files, do not silently downscope. Report the concern and either proceed as requested or ask for budget increase only if technically necessary.
Do not use botmap/evals.
Write progress/run artifacts under experiments/runs as optimizer normally does.
If the run starts successfully, immediately report the 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.

READY — Arm C launched.

Tests/checks passed:

uv run pytest → 141 passed
Sabotage gate passed.
Optimizer help confirmed --all-files and --full-repo-context.

Exact command launched:

bash

cd /Users/priyangapkini/nilenso/ai-playground/autoresearchset -a; source .env; set +aBOTMAP_REPO=/Users/priyangapkini/workspace/ar-c-new/botmap \  uv run python -m autoresearch.optimize \    --lever tool \    --all-files \    --full-repo-context \    --budget 60 \    --keep-runs

Run status:

Run dir: experiments/runs/tool-3009509-1787544884
Launch log: experiments/runs/arm-c-full-repo-launch-20260824-094443.log
Python PID: 54264
Status: running, GEPA at 0/60 when checked.
Scope: 97 editable tracked UTF-8 files, full repo context enabled (587,194 chars).

Comparability/preflight notes:

Botmap target: /Users/priyangapkini/workspace/ar-c-new/botmap @ 3009509
Baseline: reused cached yardstick for 3009509; no new baseline launched.
Map-data release check: preflight could not ask tool release, so snapshot went unchecked; not reported as mismatch.
Network check: preflight could not test link; not reported as unhealthy.
OpenRouter balance reported: $61.34.
Warning emitted: budget 60 across 97 files gives ~0 evaluations/file; proceeded as explicitly requested.

Blockers:

None at launch time.

Loading more messages…

Summary

Arm C readiness report

Files read / status checked

Useful old Arm C work that maps to new plan

Directly mineable

Useful but no longer central

What Arm C should implement after contract.py lands

Validation strategy

Hazards

Questions for Priyanga / orchestrator

Summary

Arm C readiness report

Files read / status checked

Useful old Arm C work that maps to new plan

Directly mineable

Useful but no longer central

What Arm C should implement after contract.py lands

Validation strategy

Hazards

Questions for Priyanga / orchestrator

What Arm C should implement after `contract.py` lands

What Arm C should implement after `contract.py` lands