Agent skill

aidd-rlm

Owns shared RLM evidence workflow for subagents (slice, build, verify, finalize, pack). Use when preload roles require canonical RLM evidence operations.

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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/aidd-rlm

SKILL.md

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, spec-interview-writer, 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).

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