aeonfun/Autoresearch

> ${var} — Name of the skill to evolve (e.g. token-movers). Required.

If ${var} is empty, abort with: "autoresearch requires var= set to a skill name" and exit.

Read memory/MEMORY.md for context.

Goal

Improve an existing skill by researching better approaches, generating 4 distinct variations, scoring them against a rubric, and committing the winning version as a PR.

Steps

1. Load the target skill

Read skills/${var}/SKILL.md. If the file doesn't exist, abort and notify: "Skill '${var}' not found."

Parse the skill's:

• Purpose: what it does
• Data sources: APIs, URLs, commands it calls
• Output format: what it produces (article, notification, file)
• Dependencies: env vars, tools, other files it reads

Save the original content — you'll need it for the PR diff later.

2. Research improvements

Search the web for better approaches to what this skill does:

• Alternative or complementary APIs/data sources
• Best practices for the skill's domain (e.g., crypto analysis, RSS aggregation, security scanning)
• Common pitfalls or failure modes for the techniques the skill uses
• Output formats that are more actionable or readable

Also review:

• Recent memory/logs/ entries where this skill ran — did it produce useful output? Were there failures?
• memory/cron-state.json — has this skill been failing?

3. Generate 4 variations

Create 4 distinct improved versions of the SKILL.md, each with a different thesis:

Variation A — Better inputs: Improve data sources. Add alternative/complementary APIs, better search queries, more reliable endpoints. Fix any broken or deprecated sources found in step 2.

Variation B — Sharper output: Improve the output format and content quality. Make notifications more actionable, articles more substantive, analysis more insightful. Reduce noise, improve signal.

Variation C — More robust: Improve reliability and edge-case handling. Add fallback logic for when APIs fail, better deduplication, graceful handling of empty data, clearer error messages.

Variation D — Rethink: Take a fundamentally different approach to achieving the same goal. Different methodology, different angle, or a creative combination of techniques the original didn't consider.

Each variation must:

• Preserve the original frontmatter format (name, description, var, tags)
• Follow Aeon skill conventions (read memory, log to memory/logs/${today}.md, notify via ./notify)
• Be a complete, ready-to-run SKILL.md — no placeholders
• Include a one-line comment at the top of the body: <!-- autoresearch: variation X — thesis description -->

4. Evaluate and score

Score each variation on a 1-5 scale across these criteria:

| Criterion | What to evaluate | |-----------|-----------------| | Clarity | Will Claude execute this correctly? Are instructions unambiguous? | | Data quality | Are sources reliable, diverse, and likely to return useful data? | | Output value | Is the output actionable and worth reading? Low noise? | | Robustness | Does it handle failures, empty data, and edge cases? | | Conventions | Does it follow Aeon patterns? (memory, logging, notify, var usage) | | Improvement | How much better is this than the original? |

Write out your scoring with brief justification for each score. Calculate a weighted total:

• Improvement: 3x weight (the whole point)
• Output value: 2x weight
• Clarity, Data quality, Robustness: 1.5x weight each
• Conventions: 1x weight

5. Select and apply the winner

Pick the highest-scoring variation. If scores are very close (within 2 points total), prefer the variation that makes the biggest single improvement rather than small incremental changes.

Write the winning variation to skills/${var}/SKILL.md, replacing the original.

6. Create a PR

Create a branch named autoresearch/${var} and commit the change:

bash
git checkout -b autoresearch/${var}
git add skills/${var}/SKILL.md
git commit -m "improve(${var}): autoresearch evolution

Variation chosen: [A/B/C/D] — [thesis]
Key changes: [1-2 sentence summary]"
git push -u origin autoresearch/${var}

Open a PR with:

• Title: improve(${var}): autoresearch evolution
• Body: Include the full scoring table, the winning variation's thesis, and a diff summary of what changed. Include all 4 variation summaries so the reviewer can see what was considered.

bash
gh pr create --title "improve(${var}): autoresearch evolution" --body "..."

7. Notify and log

Send via ./notify:

*Autoresearch — ${var}*
Winner: Variation [X] — [thesis]
Score: [total]/50
Key changes: [summary]
PR: [url]

Log to memory/logs/${today}.md:

### autoresearch
- Target: ${var}
- Winner: Variation [X] ([score]/50)
- Thesis: [description]
- PR: [url]
- Runners-up: [brief scores]

Network note

There is no network sandbox — curl works, with WebFetch as the fallback for a flaky public GET. For an auth'd API, call ./secretcurl with a {ENV_NAME} placeholder (the key is injected via requires:), never a bare $SECRET.

Constraints

• Never downgrade a working skill. If all variations score lower than or equal to the original on "Improvement", skip the update and notify: "No improvement found for ${var} — all variations scored at baseline."
• Preserve the skill's core purpose — evolution, not replacement.
• Do not change the skill's tags or var semantics without strong justification.
• Do not add env vars that aren't already available in the workflow (check aeon.yml secrets).