Individual skill
research-refine-pipeline
Run an end-to-end workflow that chains `research-refine` and `experiment-plan`. Use when the user wants a one-shot pipeline from vague research direction to focused final proposal plus detailed experiment roadmap, or asks to "串起来", build a pipeline, do it end-to-end, or generate both the method and experiment plan together.
skills/skills-codex/research-refine-pipeline/SKILL.md
Source commit 2901234ad2954cd2388ced8d3aed2ed9de42803a
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Nov 8, 2026
- License
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- ai-research Repository topic
- autonomous-agent Repository topic
- claude-code Repository topic
- codex Repository topic
- mcp Repository topic
- ml-research Repository topic
- paper-review Repository topic
- research-automation Repository topic
- ai-tools Repository topic
- claude Repository topic
- operations Derived category
- research Derived category
- testing Derived category
- testing-browser Derived category
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SKILL.mdcore · 6,930 bytes
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# Research Refine Pipeline: End-to-End Method and Experiment Planning
> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> resolve invocation → `.aris/reviewer.json` → Codex default (fresh native reviewer)
> with `reviewer_route.py` through the canonical helper resolver, preserving this
> slot's tier, fresh contexts, source/image inputs and deterministic gates.
> Codex examples/pins below apply only to `backend=codex`; other routes use their
> own first-call/reply contract. Save the route and actual returned identity;
> unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual
> families/completion/scope. Forward invocation reviewer/model/effort overrides
> to reviewer-bearing children. Generation workers are unchanged.
Refine and concretize: **$ARGUMENTS**