# Researcher

> Generates or refreshes research artifacts and RLM evidence for a ticket. Use when research stage should produce or update canonical RLM outputs.

- Skill: `majiayu000/researcher-5` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/researcher-5`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/researcher-5/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/researcher-5

---


Follow `feature-dev-aidd:aidd-core`.

## Steps
1. Set active feature and stage `research`.
2. Run `python3 ${CLAUDE_PLUGIN_ROOT}/skills/researcher/runtime/research.py --ticket <ticket> --auto`.
3. Re-run the same entrypoint with optional overrides (`--paths`, `--keywords`, `--note`) when targeted refresh is needed.
4. Validate RLM outputs (`*-rlm-targets.json`, `*-rlm-manifest.json`, `*-rlm.worklist.pack.json`, optional `*-rlm.pack.json`).
5. Run subagent `feature-dev-aidd:researcher`. First action: read RLM pack/worklist.
6. In `--auto` mode run bounded canonical finalize recovery once (`rlm_finalize --bootstrap-if-missing`) from stage runtime.
7. If RLM is still pending after auto recovery, return deterministic pending reason + explicit next action (`python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_finalize.py --ticket <ticket>`) and append research handoff via `python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-flow-state/runtime/tasks_derive.py --source research --append`.
8. Return the output contract with explicit next step (`/feature-dev-aidd:plan-new <ticket>` when stage handoff is ready).

## Command contracts
### `python3 ${CLAUDE_PLUGIN_ROOT}/skills/researcher/runtime/research.py`
- When to run: always as canonical researcher pipeline entrypoint.
- Inputs: `--ticket <ticket>` with optional path/keyword/note overrides.
- Outputs: research artifacts, stage status, and RLM readiness/handoff markers (`rlm_status`, `rlm_pack_status`).
- Failure mode: non-zero exit when required inputs/artifacts are missing or RLM artifact generation fails.
- Next action: fix missing inputs/paths, rerun this entrypoint; in auto mode finalize/bridge runs once, then pending outcomes include deterministic `reason_code` + `next_action`.

## Notes
- Planning stage: `AIDD:ACTIONS_LOG: n/a`.

## Migration policy
- Legacy pre-RLM research context/targets artifacts are ignored by runtime/gates.
- For old workspace state, rerun:
  `python3 ${CLAUDE_PLUGIN_ROOT}/skills/researcher/runtime/research.py --ticket <ticket> --auto`.
- If `rlm_status` remains `pending` after bounded auto recovery, hand off to shared owner:
  `python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_finalize.py --ticket <ticket>`.

## Additional resources
- Research template source: [templates/research.template.md](templates/research.template.md) (when: drafting or reviewing `aidd/docs/research/<ticket>.md`; why: keep artifact shape aligned with canonical workspace template).
- Shared RLM owner skill: [../aidd-rlm/SKILL.md](../aidd-rlm/SKILL.md) (when: `rlm_status` is not `ready` or RLM pack is missing; why: run canonical shared RLM API outside stage-local orchestration).