autoresearch

Included with Lifetime

$97 forever

Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior

Writing & Docs

What this skill does


<Purpose>
Autoresearch is a stateful skill for bounded, evaluator-driven iterative improvement. It owns one mission at a time, keeps iterating through non-passing results, records each evaluation and decision as durable artifacts, and stops only when an explicit max-runtime ceiling or another explicit terminal condition is reached.
</Purpose>

<Use_When>
- You already have a mission and evaluator from `/deep-interview --autoresearch`
- You want persistent single-mission improvement with strict evaluation
- You need durable experiment logs under `.omc/autoresearch/`
- You want a supported path for periodic reruns via Claude Code native cron
</Use_When>

<Do_Not_Use_When>
- You need evaluator generation at runtime — use `/deep-interview --autoresearch` first
- You need multiple missions orchestrated together — v1 forbids that
- You want the deprecated `omc autoresearch` CLI flow — it is no longer authoritative
</Do_Not_Use_When>

<Contract>
- Single-mission only in v1
- Mission setup/evaluator generation stays in `deep-interview --autoresearch`
- Evaluator output must be structured JSON with required boolean `pass` and optional numeric `score`
- Non-passing iterations do **not** stop the run
- Stop conditions are explicit and bounded, with max-runtime as the primary strict stop hook
</Contract>

<Required_Artifacts>
Canonical persistent storage lives under `.omc/autoresearch/<mission-slug>/` and/or `.omc/logs/autoresearch/<run-id>/`.

Minimum required artifacts:
- mission spec
- evaluator script or command reference
- per-iteration evaluation JSON
- markdown decision logs

Recommended canonical shape:
```text
.omc/autoresearch/<mission-slug>/
  mission.md
  evaluator.json
  runs/<run-id>/
    evaluations/
      iteration-0001.json
      iteration-0002.json
    decision-log.md
```
Reuse existing runtime artifacts when available rather than duplicating them unnecessarily.
</Required_Artifacts>

<Workflow>
1. Confirm a single mission exists and evaluator setup is already available.
2. Ensure mode/state is active for `autoresearch` and records:
   - mission slug/dir
   - evaluator reference
   - iteration count
   - started/updated timestamps
   - explicit max-runtime or deadline
3. On every iteration:
   - run exactly one experiment/change cycle
   - run the evaluator
   - persist machine-readable evaluation JSON
   - append a human-readable markdown decision log entry
   - continue even when evaluation does not pass
4. Stop when:
   - max-runtime ceiling is reached
   - user explicitly cancels
   - another explicit terminal condition is recorded by the runtime
</Workflow>

<Cron_Integration>
Claude Code native cron is a supported integration point for periodic mission enhancement. In v1, prefer documenting/configuring cron inputs over building a large scheduler UI.

If cron is used:
- keep one mission per scheduled job
- preserve the same mission/evaluator contract
- append new run artifacts rather than overwriting prior experiments
</Cron_Integration>

<Execution_Policy>
- Do not hand execution back to `omc autoresearch`
- Do not create multi-mission orchestration
- Prefer reusing `src/autoresearch/*` runtime/schema helpers where they already match the stricter contract
- Keep logs useful to humans, not only machines
</Execution_Policy>

Files: 1

Size: 3.5 KB

Complexity: 11/100

Category: Writing & Docs

Source: https://github.com/yeachan-heo/oh-my-claudecode/tree/main/skills/autoresearch

Related in Writing & Docs

jax-development

Included

Use this skill when the user is writing, debugging, profiling, refactoring, reviewing, benchmarking, parallelising, exporting, or explaining JAX code, or when they mention JAX, jax.numpy, jit, grad, value_and_grad, vmap, scan, lax, random keys, pytrees, jax.Array, sharding, Mesh, PartitionSpec, NamedSharding, pmap, shard_map, Pallas, XLA, StableHLO, checkify, profiler, or the JAX repo. It helps turn NumPy or PyTorch-style code into pure functional JAX, fix tracer/control-flow/shape/PRNG bugs, remove recompiles and host-device syncs, choose transforms and sharding strategies, inspect jaxpr/lowering/IR, and benchmark compiled code correctly.

Writing & Docsscripts

nature-article-writer

Included

Drafts, rewrites, diagnostically critiques, and style-calibrates primary research manuscripts for Nature and Nature Portfolio journals. Use when the user wants a Nature-style title, summary paragraph or abstract, introduction, results, discussion, methods, figure legends, presubmission enquiry, cover letter, reviewer response, or when a scientific draft sounds generic, jargon-heavy, structurally weak, or AI-ish and needs precise, broad-reader-friendly prose without inventing data, analyses, or references. Best for primary research articles and letters rather than reviews or press releases unless explicitly adapting one.

Writing & Docsscripts

deckrd

Included

Document-driven framework that derives requirements, specifications, implementation plans, and executable tasks from goals through structured AI dialogue. Use when user says "write requirements", "create spec", "plan implementation", "derive tasks", "structure this feature", "break down into tasks", or "document this module". Also use for reverse engineering existing code into docs (/deckrd rev). Do NOT use for direct code writing — use /deckrd-coder after tasks are generated. Do NOT use when the user only wants to run or fix existing code without planning.

Writing & Docsscripts

clinical-decision-support

Included

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis.

Writing & Docsscripts

handling-sf-data

Included

Salesforce data operations with 130-point scoring. Use this skill to create, update, delete, bulk import/export, generate test data, and clean up org records using sf CLI and anonymous Apex. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, needs data factory patterns for Apex tests, or needs to seed/clean records in a Salesforce org. DO NOT TRIGGER when: SOQL query writing only (use querying-soql), Apex test execution (use running-apex-tests), or metadata deployment (use deploying-metadata).

Writing & Docsscripts

accelint-ac-to-playwright

Included

Convert and validate acceptance criteria for Playwright test automation. Use when user asks to (1) review/evaluate/check if AC are ready for automation, (2) assess if AC can be converted as-is, (3) validate AC quality for Playwright, (4) turn AC into tests, (5) generate tests from acceptance criteria, (6) convert .md bullets or .feature Gherkin files to Playwright specs, (7) create test automation from requirements. Handles both bullet-style markdown and Gherkin syntax with JSON test plan generation and validation.

Writing & Docsscripts