Researcher

Skill GrinRus/ai_driven_dev/skills/researcher

Generates or refreshes research artifacts and RLM evidence for a ticket. Use when research stage should produce or update canonical RLM outputs. Do not use when the request is initial ticket bootstrap in `idea-new` or implementation planning in `plan-new`.From its SKILL.md

Install

npx -y skills add GrinRus/ai_driven_dev --skill researcher

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

5 stars5 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.0 KB, as published. Nobody here has run it

Follow feature-dev-aidd:aidd-core.

Steps

Set active feature and stage research.
Run python3 ${CLAUDE_PLUGIN_ROOT}/skills/researcher/runtime/research.py --ticket <ticket> --auto.
Re-run the same entrypoint with optional overrides (--paths, --keywords, --note) when targeted refresh is needed.
Validate RLM outputs (*-rlm-targets.json, *-rlm-manifest.json, *-rlm.worklist.pack.json, optional *-rlm.pack.json).
Run subagent feature-dev-aidd:researcher. First action: read RLM pack/worklist.
In --auto mode run bounded canonical finalize recovery once (rlm_finalize --bootstrap-if-missing) from stage runtime.
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.
Ready path: return /feature-dev-aidd:plan-new <ticket> only when RLM readiness is ready and the research document is synchronized with the current evidence.
Pending or blocked path: if readiness is not ready, return the deterministic blocker or pending output instead of guessing a downstream handoff.
Runtime-path safety: execute only canonical runtime commands from this contract (python3 ${CLAUDE_PLUGIN_ROOT}/skills/.../runtime/...).
Root-relative /skills/... runtime paths are forbidden; use only ${CLAUDE_PLUGIN_ROOT}/skills/.../runtime/....

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 (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 (when: rlm_status is not ready or RLM pack is missing; why: run canonical shared RLM API outside stage-local orchestration).