Evaluate Autoresearch Fit

Assess whether a skill is a viable candidate for the Karpathy 3-File Autoresearch autonomous optimization loop. Scores each skill on four dimensions, proposes what the 3-file architecture would look like, and updates the canonical summary-ranked-skills.json via the update script.

Background

The Karpathy autoresearch pattern requires three conditions simultaneously:

1. A Clear Metric — a single number with a clear optimization direction
2. Automated Evaluation — no human in the loop; scoring runs headlessly from a shell command
3. One Editable File — the agent mutates only a single predefined target per loop

Skills that lack these properties cannot run an effective autonomous loop.

Data File

The canonical ranked skills list lives at:

plugin-research/experiments/analyze-candidates-for-auto-reseaarch/skills/eval-autoresearch-fit/assets/resources/summary-ranked-skills.json

After every evaluation, update it with the update script (see Step 5).

Scoring Dimensions

Each dimension is scored 1-10. Max total = 40.

Viability thresholds:

• 32-40 HIGH — Excellent candidate, implement now
• 24-31 MEDIUM — Good candidate, address identified gaps first
• 16-23 LOW — Needs significant rework to be viable
• < 16 NOT_VIABLE — Skip or the metric is unfixable

Evaluation Steps

Step 1: Locate the Skill

If $ARGUMENTS is a path to a directory containing SKILL.md, read it directly.

Otherwise find it by name from the repo root:

PROJECT_ROOT=$(git rev-parse --show-toplevel)
find "$PROJECT_ROOT/plugins" -name "SKILL.md" | grep "$ARGUMENTS" | head -5

Read the SKILL.md fully before scoring.

Step 2: Score Each Dimension

Reason through each dimension explicitly before assigning a number.

Objectivity (1-10)

• Can the outcome be captured as a single number from a shell command?
• Is there a binary pass/fail condition requiring no LLM judgment?
• Deductions: -2 if LLM-as-judge is needed, -4 if no numeric proxy exists, -7 if purely aesthetic
• Flag: if the only evaluator is an LLM call, note non-determinism cost

Execution Speed (1-10)

• <10s = 10, 10-60s = 9, 1-5min = 7, 5-15min = 5, 15-30min = 3, >30min = 1
• Ask: does the skill have interactive phases (confirmations, interviews)? Those must be bypassed in eval mode.

Frequency of Use (1-10)

• Multiple times per session = 10, Daily = 8, Few/week = 6, Weekly = 4, Monthly = 2, Rare = 1
• Base this on the skill's description trigger phrases and use context

Potential Utility (1-10)

• If this skill's behavior were optimized 50% more reliably, what's the downstream impact?
• Does it gate other work? Is it in a critical path?
• Systemic/prevents failures = 10, Saves significant time = 7, Moderate = 5, Minor = 2

Step 3: Identify Loop Type and Split Loops if Needed

Determine the loop type:

• DETERMINISTIC: evaluator is pure shell (no LLM call). Preferred.
• LLM_IN_LOOP: evaluator must call Claude/API to score. Non-deterministic, needs N averaging.
• HYBRID: script produces partial score, LLM judges the rest.

Important: if a skill has both a script component and a prompt component, propose splitting into two separate loops. Label them Loop A (script) and Loop B (prompt). Score and barrier each separately.

Step 4: Propose the 3-File Architecture

The Spec (`program.md`): What is the optimization goal? What constraints apply? What is the NEVER STOP directive?

The Mutation Target: Which single file does the agent modify per iteration? If the skill inherently requires multi-file changes, flag this as a barrier and propose how to isolate it.

The Evaluator (`evaluate.py`):

Note: this evaluate.py is a script you would write when implementing the autoresearch loop for the target skill — it is NOT part of this skill. This skill only describes what it would look like. When ready to build the loop, create evaluate.py inside the target skill's autoresearch/ directory.

• What shell command produces the metric?
• Is it deterministic? (Same input always produces same output?)
• If LLM-in-loop: propose a cheaper deterministic proxy if one exists

Step 5: Output Assessment and Update JSON

Produce the assessment in this format:

## Autoresearch Fit Assessment: [Skill Name]

**Plugin:** [plugin-name]
**Skill path:** [relative path from repo root]

### Scores
| Dimension | Score | Rationale |
|---|---|---|
| Objectivity | X/10 | [one line] |
| Execution Speed | X/10 | [one line] |
| Frequency of Use | X/10 | [one line] |
| Potential Utility | X/10 | [one line] |
| **TOTAL** | **X/40** | |

**Verdict: [HIGH / MEDIUM / LOW / NOT_VIABLE]**
**Loop type: [DETERMINISTIC / LLM_IN_LOOP / HYBRID]**

### Proposed 3-File Architecture

**Spec (`program.md`):**
> [2-3 sentences: optimization goal + constraints + NEVER STOP directive]

**Mutation Target:** `[path/to/file]`

**Evaluator command:**

[shell command that outputs a single number]

> Deterministic: [YES / NO + explanation]

### Key Barriers
- [Barrier 1]
- [Barrier 2 if any]

### Recommendation
[1-2 sentences. If MEDIUM: what to address first.]

Then update the JSON using the update script:

DATA_JSON=$(git rev-parse --show-toplevel)/plugin-research/experiments/analyze-candidates-for-auto-reseaarch/skills/eval-autoresearch-fit/assets/resources/summary-ranked-skills.json
SKILL_DIR=.

python "$SKILL_DIR/scripts/update_ranked_skills.py" \
  --json-path "$DATA_JSON" \
  --plugin <plugin> \
  --skill <skill> \
  --objectivity X --speed X --frequency X --utility X \
  --verdict HIGH|MEDIUM|LOW|NOT_VIABLE \
  --loop-type DETERMINISTIC|LLM_IN_LOOP|HYBRID \
  --mutation-target "path/to/file" \
  --evaluator-command "python evaluate.py ..." \
  --barriers "Barrier 1" "Barrier 2" \
  --eval-notes "Key insight from this evaluation" \
  --status EVALUATED

Useful script commands

DATA_JSON=$(git rev-parse --show-toplevel)/plugin-research/experiments/analyze-candidates-for-auto-reseaarch/skills/eval-autoresearch-fit/assets/resources/summary-ranked-skills.json

# List all entries with current status
python ./scripts/update_ranked_skills.py --json-path "$DATA_JSON" --list

# Show a specific entry
python ./scripts/update_ranked_skills.py --json-path "$DATA_JSON" \
  --plugin agent-agentic-os --skill os-eval-runner --show

# List only PENDING entries (next batch to evaluate)
python ./scripts/update_ranked_skills.py --json-path "$DATA_JSON" \
  --list --filter-status PENDING

# Generate morning report (full ranked table + recommendation)
python ./scripts/update_ranked_skills.py --json-path "$DATA_JSON" --morning-report

Batch Mode

When the user says "evaluate next batch" or "continue the list":

1. Run --list --filter-status PENDING to see remaining skills
2. Take the top 3 by total_autoresearch_viability
3. Evaluate each using Steps 1-5
4. After every 3, show the updated status table and ask: "Continue with next 3?"

Phase 2: Scaffold the Loop (HIGH / MEDIUM skills)

When a skill scores HIGH or MEDIUM, scaffold the actual autoresearch loop inside the target skill using the autoresearch/ convention. This folder lives inside the target skill directory.

Directory convention (inside the target skill):

plugins/<plugin>/skills/<skill>/
  SKILL.md                     ← mutation target (agent edits this each iteration)
  autoresearch/                ← the loop lives here
    program.md                 ← the spec (goal + constraints + NEVER STOP)
    evaluate.py                ← LOCKED evaluator (agent must never modify this)
    results.tsv                ← experiment ledger (one row per iteration)
    tasks/                     ← golden task fixtures (LLM_IN_LOOP skills only)
    test-fixtures/             ← deterministic inputs (DETERMINISTIC skills only)

Why evaluate.py runs every iteration: The loop is: agent mutates SKILL.md → run autoresearch/evaluate.py → record metric in results.tsv → KEEP (commit) or DISCARD (git reset). evaluate.py is locked — the agent only touches the mutation target.

Cost by loop type:

• DETERMINISTIC — seconds, zero LLM cost. 100 iterations overnight is realistic.
• LLM_IN_LOOP — ~3 min/iteration, ~$0.01 model cost. Use N=5 trials and average.
• HYBRID — intermediate runtime and cost.

Implement DETERMINISTIC candidates first where possible (fast, free, many trials).

Scaffold steps for HIGH/MEDIUM verdicts:

1. Create autoresearch/ inside the target skill directory
2. Write program.md from the template (goal, metric, mutation target, NEVER STOP)
3. Write evaluate.py implementing the evaluator command from the assessment
4. Create empty results.tsv with header: commit\tmetric\tstatus\tdescription
5. For DETERMINISTIC: add test-fixtures/ with at least one deterministic input
6. For LLM_IN_LOOP: add tasks/ with at least one human-validated golden task

Edge Cases

• Interactive skill phases: propose bypassing them in eval mode; note as a barrier
• Score 10 objectivity + 1 speed: flag as "needs wrapper script" and sketch what it would look like
• LLM-only evaluator: note cost, propose cheaper proxy if one exists; mark as LLM_IN_LOOP
• Skill not found: search plugins/ from repo root, report path before proceeding
• NOT_VIABLE threshold: score < 16 = NOT_VIABLE. A high-scoring skill with barriers is still HIGH or MEDIUM — do not conflate barriers with viability verdict.