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Get Started Free →majiayu000/autoresearch
ALWAYS activate when user types /autoresearch, $autoresearch plan, $autoresearch debug, $autoresearch fix, $autoresearch security, $autoresearch ship, $autoresearch scenario, $autoresearch predict, $autoresearch learn, $autoresearch reason, or $autoresearch probe. MUST also activate when user mentions "autoresearch" with ANY goal, metric, or task, even when the invocation is embedded in prose. This is a BLOCKING skill invocation — invoke BEFORE generating any other response.
Passed 17 of 20 test cases with the skill, and 6 of 20 without it.
and 9 more in the full report
12 fixed7 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
What lock file path is used inside .lab to prevent concurrent execution of experiment runners?
| Checked | Without skill | With skill |
|---|---|---|
| References .lab/.runner.lock as the PID lock file | Missed | Passed |
The instructions your model reads when the skill loads.
USE WHEN the user runs /autoresearch, says "autoresearch", "optimize this prompt", "improve this overnight", "run an optimization loop", "iterate on this", "auto-improve", or wants to autonomously refine a file against measurable criteria.
This skill supports two mutually exclusive evaluation modes. Both maintain four-way separation.
| Role | Who | Knows Eval Code? | Knows Prompt History? |
|---|---|---|---|
| Main Agent | You (optimizer) | NO — reads metric number only | Yes — reads logs, plans changes |
| Eval Agent | autoresearch-eval-agent sub-agent | Yes — writes eval.py | No |
| Test Runner | autoresearch-test-runner sub-agent | NO — fresh context | NO |
| Judge Script | eval.py (deterministic Python) | IS the eval | No |
Metric: pass_rate. Best for tasks with clear, mechanical quality checks (word count, format, keywords, structure).
| Role | Who | Knows Rubric? | Knows Prompt History? |
|---|---|---|---|
| Main Agent | You (optimizer) | Criteria names only | Yes — reads logs, plans changes |
| Eval Agent | autoresearch-eval-agent sub-agent | Yes — writes rubric.md | No |
| Test Runner | autoresearch-test-runner sub-agent | NO — fresh context | NO |
| Judge Agent | autoresearch-judge sub-agent | Follows rubric | NO — fresh context |
Metric: quality_score. Best for creative/subjective tasks where mechanical checks miss the point (tone, authenticity, narrative, emotional resonance).
The modes are mutually exclusive. Deterministic mode uses eval.py only. AI judge mode uses the judge agent only. There is no combined score.
Key isolation (both modes):
test cases passed, vs 6 / 20 without
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