Compute obsolescence scores for RAG corpus knowledge nodes using configurable weights.
npx skills add https://github.com/arbgjr/smart_alarm --skill decay-scoring
Please help me install this Agent Skill. Skill: decay-scoring Source: https://github.com/arbgjr/smart_alarm/tree/main/.claude/skills/decay-scoring Command: npx skills add https://github.com/arbgjr/smart_alarm --skill decay-scoring
The decay-scoring skill computes a quantitative obsolescence score for knowledge nodes in a RAG corpus. It helps teams identify outdated or under-validated content to prioritize review, update, or archiving.
Run the decay scoring workflow using the included scripts:
High-intent search queries and answers about installing and using this skill.
Decay scoring identifies outdated content in a RAG knowledge base by computing quantitative obsolescence scores across age, validation recency, access frequency, and content type to surface stale nodes for curator actions.
Automating knowledge curation metrics involves applying configurable weights and half-lives to corpus nodes, generating per-node obsolescence scores, status classifications, and a review queue to prioritize updates or archiving.
Yes, you can customize decay scoring by adjusting configurable weights, half-lives, and content-type bonuses defined in YAML metadata to tailor content prioritization across your specific knowledge base nodes.
You track access frequency for RAG nodes by running the decay_tracker script with node access events, feeding node history and decay parameters into the decay calculator to trigger automated scoring updates.
Yes, the automated decay scoring approach works with large scale knowledge bases, applying configurable decay parameters across age, validation recency, access frequency, and content type to prioritize items for review at scale.
arbgjrIndex and validate RAG corpus knowledge nodes using standard-library Python scripts.
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Chulf58Audit docs/solutions against the codebase to flag stale references and archive outdated items.
