aeon-autoresearch · diff

git:20260512.2331923 to git:20260512.a4ba040

27 added, 87 removed. Audit A to A.

---

name: aeon-autoresearch

description: |

- Evolve any installed Bankr skill by generating four distinct improved variations along separate

- theses (better inputs / sharper output / more robust / rethink), scoring them on a weighted

- rubric, and applying the winning version. Genetic-algorithm-style self-improvement. Never

- downgrades a working skill. Use when an installed skill is producing low-signal output, hitting

- deprecated APIs, or just feels stale.

- Triggers: "improve this skill", "evolve $skill_name", "auto-research my $skill", "regenerate

- variations for X", "tune the output of skill Y".

+ Evolve any installed skill by generating four variations along separate theses (better inputs /

+ sharper output / more robust / rethink), scoring them on a weighted rubric, and applying the

+ winner. Never downgrades a working skill — aborts cleanly if no variation improves the original.

+ Use when an installed skill is producing low-signal output, hitting deprecated APIs, or feels

+ stale.

+ Triggers: "improve this skill", "evolve $skill", "auto-research my X", "regenerate variations".

---

# aeon-autoresearch

- Self-improvement loop for any installed skill. Given a target SKILL.md, generates four parallel improved variations along distinct theses, scores each on a weighted rubric, and applies the winner.

- Not a replacement for human authorship — a way to harden working skills between maintenance windows. Fix deprecated endpoints, sharpen output formats, add fallbacks.

+ Self-improvement loop. Given a target SKILL.md, generates four parallel improved variations, scores each, applies the winner.

## Inputs

| Param | Description |

|---|---|

- | `target` | Skill name (e.g. `token-movers`) or path to a SKILL.md. Required. |

- | `mode` | `evolve` (default) — score, apply winner, write diff. `dry-run` — score and print, write nothing. |

- ## The four theses

+ | `target` | Skill name or path to SKILL.md. Required. |

+ | `mode` | `evolve` (default) writes the diff. `dry-run` scores and prints, writes nothing. |

- The skill generates exactly four variations, each with a different focus:

+ ## The four variations

- | Variation | Focus |

- |---|---|

- | **A — Better inputs** | Improve data sources. Replace deprecated APIs, add fallbacks, fix broken endpoints. |

- | **B — Sharper output** | Tighter format, signal over noise, explicit verdicts, banned filler phrases. |

- | **C — More robust** | Empty-data handling, retries, dedup state, rate-limit awareness. |

- | **D — Rethink** | Fundamentally different methodology for the same goal. |

+ - **A — Better inputs**: replace deprecated APIs, add fallbacks, fix broken endpoints.

+ - **B — Sharper output**: tighter format, signal over noise, explicit verdicts, banned filler.

+ - **C — More robust**: empty-data handling, retries, dedup state, rate-limit awareness.

+ - **D — Rethink**: fundamentally different methodology for the same goal.

- Each must be a complete, runnable SKILL.md. Frontmatter shape preserved (name, description, tags, any custom fields).

+ Each is a complete runnable SKILL.md. Frontmatter shape preserved.

- ## Scoring rubric

+ ## Scoring

- Each variation scored 1–5 on:

+ 1-5 per axis, weighted total max 50:

| Axis | Weight |

|---|---|

| Improvement vs original | 3× |

| Output value | 2× |

- | Clarity (LLM-executable) | 1.5× |

- | Data quality | 1.5× |

- | Robustness | 1.5× |

- | Conventions (frontmatter, structure) | 1× |

+ | Clarity, data quality, robustness | 1.5× each |

+ | Conventions | 1× |

- Max weighted total: 50. Tie-break within 2 points: prefer the variation making the biggest single improvement over many small ones.

+ Tie-break (within 2 points): prefer the variation making the biggest single improvement over many small ones.

## Safety guarantee

- If every variation scores ≤ original on the **Improvement** axis, the skill aborts with `AUTORESEARCH_NO_IMPROVEMENT`. No file written, no change applied. Working skills are never downgraded.

- The skill also preserves the original's:

- - Core purpose (evolution, not replacement).

- - Frontmatter shape and required fields.

- - Any env vars the skill already declares (no new dependencies introduced silently).

- ## Output

- A diff against the target SKILL.md, plus a report:

- ```

- *Autoresearch — token-movers — 2026-05-12*

- Variation A (Better inputs): 43/50 — added GeckoTerminal fallback, dropped deprecated v1 endpoint

- Variation B (Sharper output): 47/50 — added pump-risk flags, banned filler phrases, structured top-5

- Variation C (Robustness): 41/50 — added retry + empty-data path

- Variation D (Rethink): 38/50 — narrative-clustered instead of raw ranking

- Winner: B (47/50)

- Reason: largest improvement axis (5) — output value bump is the biggest single gain.

- Diff:

- SKILL.md +84 -52

- References to deprecated /v1/markets removed (3 places)

- Added pump-risk flag table (8 lines)

- Banned filler phrase list added to constraints (4 lines)

- Runners-up summary:

- A: improvement was real but smaller — same output shape with better sources.

- C: tightened reliability without changing the core output. Strong second.

- D: interesting reframe but data-quality unproven on the new methodology.

- ```

- In `evolve` mode, the target SKILL.md is replaced with the winner. In `dry-run` mode, the report is emitted and no files change.

- ## Integration with version control

- If the target skill is inside a git repo, the skill creates a branch (`autoresearch/${target}`), writes the changes, and offers a `git diff` for review. It never pushes — the operator commits and pushes manually after reading the diff.

- If not inside a repo, the original SKILL.md is preserved at `${target}/SKILL.md.before-autoresearch` so the operator can roll back.

- ## When this fails

- | Condition | Exit |

- |---|---|

- | All variations ≤ original on improvement | `AUTORESEARCH_NO_IMPROVEMENT` — no change. |

- | Target skill not found | `AUTORESEARCH_TARGET_NOT_FOUND`. |

- | Repeated `AUTORESEARCH_NO_IMPROVEMENT` on the same target | Indicates the skill genuinely needs human authorship; flag for operator. |

+ If every variation scores ≤ original on **Improvement**, the skill aborts with `AUTORESEARCH_NO_IMPROVEMENT`. No file written. Working skills are never downgraded.

- ## Guidelines

+ Preserves the original's core purpose, frontmatter shape, and declared env vars.

- - Improvement is the headline. A variation that scores high on clarity but doesn't improve over the original is not the winner.

- - Tie-breaker rewards bold improvement over incremental polish.

- - Preserves the skill's core purpose. The skill's name and stated description are inviolate; evolution refines how it accomplishes that purpose.

+ ## Versioning

- ## Required keys

+ Inside a git repo, changes land in a branch (`autoresearch/${target}`) — operator reviews the diff before merging. Outside a repo, the original is preserved at `${target}/SKILL.md.before-autoresearch` for rollback.

- None directly. The skill may invoke WebFetch / WebSearch during the "Better inputs" variation to research current APIs.

+ ## Output

- ## Pairs with

+ The diff, plus a report with the scoring table for all four variations and a one-paragraph rationale for the winner.

- - `aeon-skill-evals` upstream (surfaces which skills need evolution — low scores on the eval manifest are the trigger).

- - `aeon-skill-repair` for deterministic bug fixes; autoresearch handles quality lifts.

+ Pairs with `aeon-skill-evals` (surfaces what's underperforming) and `aeon-skill-repair` (deterministic bugs; autoresearch handles quality lifts).