/idea-discovery

name: idea-discovery
description: "Workflow 1: Full idea discovery pipeline to go from a broad research direction to validated, pilot-tested ideas. Use when user says \"找idea全流程\", \"idea discovery pipeline\", \"从零开始找方向\", or wants the complete idea exploration workflow."
argument-hint: "[research-direction]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, WebSearch, WebFetch, Skill, mcp__codex__codex, mcp__codex__codex-reply

Workflow 1: Idea Discovery Pipeline

Orchestrate a complete idea discovery workflow for: **$ARGUMENTS**

Overview

This skill chains sub-skills into a single automated pipeline:

/research-lit → /idea-creator → /novelty-check → /research-review → /research-refine-pipeline
  (survey)      (brainstorm)    (verify novel)    (critical feedback)  (refine method + plan experiments)

Each phase builds on the previous one's output. The final deliverables are a validated `idea-stage/IDEA_REPORT.md` with ranked ideas, plus a refined proposal (`refine-logs/FINAL_PROPOSAL.md`) and experiment plan (`refine-logs/EXPERIMENT_PLAN.md`) for the top idea.

Constants

• **PILOT_MAX_HOURS = 2** — Skip any pilot experiment estimated to take > 2 hours per GPU. Flag as "needs manual pilot" in the report.
• **PILOT_TIMEOUT_HOURS = 3** — Hard timeout: kill any running pilot that exceeds 3 hours. Collect partial results if available.
• **MAX_PILOT_IDEAS = 3** — Run pilots for at most 3 top ideas in parallel. Additional ideas are validated on paper only.
• **MAX_TOTAL_GPU_HOURS = 8** — Total GPU budget across all pilots. If exceeded, skip remaining pilots and note in report.
• **AUTO_PROCEED = true** — If user doesn't respond at a checkpoint, automatically proceed with the best option after presenting results. Set to `false` to always wait for explicit user confirmation.
• **REVIEWER_MODEL = `gpt-5.6-sol`** — Model used via Codex MCP. Must be an OpenAI model (e.g., `gpt-5.6-sol`, `o3`, `gpt-4o`). Passed to sub-skills.
• **OUTPUT_DIR = `idea-stage/`** — All idea-stage outputs go here. Create the directory if it doesn't exist.
• **ARXIV_DOWNLOAD = false** — When `true`, `/research-lit` downloads the top relevant arXiv PDFs during Phase 1. When `false` (default), only fetches metadata. Passed through to `/research-lit`.
• **COMPACT = false** — When `true`, generate compact summary files for short-context models and session recovery. Writes `idea-stage/IDEA_CANDIDATES.md` (top 3-5 ideas only) at the end of this workflow. Downstream skills read this instead of the full `idea-stage/IDEA_REPORT.md`.
• **RENDER_HTML = true** — When `true` (default), auto-render `idea-stage/IDEA_REPORT.md` to HTML at workflow end via `/render-html`. Uses `--no-review` (the source MD already went through novelty + cross-model review during Phase 3). Set `false` to skip, or pass `— render html: false`.
• **REF_PAPER = false** — Reference paper to base ideas on. Accepts: local PDF path, arXiv URL, or any paper URL. When set, the paper is summarized first (`idea-stage/REF_PAPER_SUMMARY.md`), then idea generation uses it as context. Combine with `base repo` for "improve this paper with this codebase" workflows.
• **RESUMABLE = true** — Record stage evidence under `.aris/runs/<run_id>.json` and require a deterministic evidence gate before declaring the final report complete.

> 💡 These are defaults. Override by telling the skill, e.g., `/idea-discovery "topic" — ref paper: https://arxiv.org/abs/2406.04329` or `/idea-discovery "topic" — compact: true`.

Per-stage evidence gate (`RESUMABLE = true`)

Resolve `run_state.py` and `idea_discovery_gate.py` through the same canonical helper chain used by `/research-pipeline`: `.aris/tools/` → `tools/` → `$ARIS_REPO/tools/` → `~/.aris/repo/tools/`. If either helper is unavailable, the final report is `BLOCKED`; do not silently continue without a state record.

For a new run, derive `<run_id>` from the direction slug and date, then start this ordered state record:

research-lit,idea-creator,novelty-check,research-review,research-refine-pipeline

For each phase, mark `running` on entry and `done --artifact <path>` only after its artifact is present. Use these artifact locators so the final gate can check the canonical report rather than scattered scratch files:

| Phase | Artifact locator | |---|---| | `research-lit` | `idea-stage/IDEA_REPORT.md#literature-landscape` | | `idea-creator` | `idea-stage/IDEA_REPORT.md#ranked-ideas` | | `novelty-check` | `idea-stage/IDEA_REPORT.md#novelty-verification` | | `research-review` | `idea-stage/IDEA_REPORT.md#external-critical-review` | | `research-refine-pipeline` | `refine-logs/FINAL_PROPOSAL.md` |

At the end of Phase 5, run:

<resolved-python> <resolved-idea_discovery_gate.py> . <run_id> --report idea-stage/IDEA_REPORT.md

The gate writes its result to `gates.idea-discovery-evidence` in the run state. On `PASS`, it records the gate verdict under `gates.idea-discovery-evidence` — per-phase acceptance stays with each stage's own cross-model or deterministic gate (the evidence gate proves execution, never quality). On a non-zero exit, it writes explicit `BLOCKED: <stage> evidence missing` lines to the report; do not present the workflow as complete. On `— resume <run_id>`, start from the first non-terminal phase and re-run the gate before finalizing.

Pipeline

Phase 0: Load Research Brief (if available)

Before starting any other phase, check for a detailed research brief in the project:

1. Look for `RESEARCH_BRIEF.md` in the project root (or path passed as `$ARGUMENTS`) 2. If found, read it and extract:

• Problem statement and context
• Constraints (compute, data, timeline, venue)
• What the user already tried / what didn't work
• Domain knowledge and non-goals
• Existing results (if any)

3. Use this as the primary context for all subsequent phases — it replaces the one-line prompt 4. If both `RESEARCH_BRIEF.md` and a one-line `$ARGUMENTS` exist, merge them (brief