Aidd rlm

Skill GrinRus/ai_driven_dev/skills/aidd-rlm

Owns shared RLM evidence workflow for subagents (slice, build, verify, finalize, pack). Use when preload roles require canonical RLM evidence operations. Do not use when the request is stage-local research execution in `researcher` or stage handoff-only guidance in `aidd-stage-research`.From its SKILL.md

Install

npx -y skills add GrinRus/ai_driven_dev --skill aidd-rlm

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SKILL.md

3.6 KB, as published. Nobody here has run it

Scope

This skill is preload-only for subagents.
Use it to keep RLM behavior consistent across agents without duplicating long instructions.
Preload matrix v2 roles: analyst, planner, plan-reviewer, prd-reviewer, researcher, reviewer, tasklist-refiner, validator.
Do not preload for implementer or qa.

Canonical command paths

Slice (shared): python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_slice.py
RLM runtime entrypoints:
- python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_nodes_build.py
- python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_verify.py
- python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_links_build.py
- python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_jsonl_compact.py
- python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_finalize.py
- python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/reports_pack.py

Command contracts

`python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_slice.py`

When to run: when agent needs targeted evidence from existing RLM artifacts without full JSONL scans.
Inputs: --ticket <ticket> with one or more --query tokens.
Outputs: compact slice payload suitable for pack-first reasoning.
Failure mode: non-zero exit when source artifacts are missing or query context is invalid.
Next action: generate/repair missing RLM artifacts, then rerun slice query.

`python3 ${CLAUDE_PLUGIN_ROOT}/skills/aidd-rlm/runtime/rlm_finalize.py`

When to run: handoff/finalization path when rlm_status remains pending after stage-local bounded recovery.
Inputs: --ticket <ticket> and optional bootstrap/recovery flags.
Outputs: finalized RLM nodes/links/pack status with deterministic readiness metadata.
Failure mode: non-zero exit on unresolved manifest/worklist integrity issues.
Next action: fix upstream targets/worklist inconsistencies and rerun finalize.

Fallback paths

Use canonical Python runtime entrypoints only for new prompts/integrations.

Evidence policy

Read pack-first: aidd/reports/research/<ticket>-rlm.pack.json.
Use slice queries for targeted context extraction.
Avoid full JSONL reads unless pack/slice is insufficient.

Additional resources

Runtime finalize reference: runtime/rlm_finalize.py (when: finalize/handoff behavior is unclear; why: confirm canonical finalize flags and pending/ready transitions).
Cross-skill stage contract: ../aidd-stage-research/SKILL.md (when: researcher stage orchestration and RLM ownership boundary needs clarification; why: keep shared/runtime and stage responsibilities split).