lore_skill
- Shell
8
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
3 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill simota/agent-skills --skill lore- SKILL.md10.7 KB
Overview
This skill is an ecosystem memory curator that reads agent journals, extracts cross-cutting patterns, and distributes validated insights to relevant agents. It does not write code; it focuses on discovering, cataloging, and propagating institutional knowledge to reduce repeated mistakes and amplify proven practices. The skill tracks evidence strength, freshness, and contradictions to maintain a reliable METAPATTERNS catalog.
How this skill works
Lore scans all agent journals and postmortems, then clusters and deduplicates observations to surface recurring successes, failures, heuristics, and anti-patterns. Each discovery is classified by domain, type, confidence, and scope, recorded in a structured metapattern entry, and tagged with evidence and freshness metadata. The skill flags contradictions and decay, updates confidence with new evidence, and pushes succinct handoffs to consuming agents according to a propagation matrix.
When to use it
- Maintain institutional memory across many specialized agents
- Extract generalizable lessons from incident postmortems and remediation logs
- Detect stale or contradicted knowledge and trigger audits
- Propagate validated best practices to design, routing, and remediation agents
- Catalog ecosystem-wide anti-patterns for continuous improvement
Best practices
- Always read source journal entries before synthesizing and cite agent name, date, and context for each evidence item
- Classify confidence by evidence count: anecdote (1), emerging (2), pattern (3+), established (6+)
- Tag every pattern with a last-validated date and a freshness state for automated decay detection
- Check for contradictions before registering a new pattern and request human arbitration when conflicts persist
- Propagate only to agents with clear relevance; avoid noisy broadcasts
Example use cases
- Harvest recurring deployment failure modes from Triage postmortems and register remediation patterns for Mend
- Detect routing anti-patterns from chain logs and deliver optimized hints to Nexus
- Synthesize UX testing failures into cross-project heuristics and notify Sigil and Architect
- Flag a pattern as AGING after 90 days without new evidence and trigger targeted re-verification
- Aggregate successful test strategies across agents and publish ranked best practices to Builder/Artisan
FAQ
Confidence is based on evidence count and diversity: 1 instance = anecdote, 2 = emerging, 3–5 = pattern, 6+ = established; source diversity increases trust.
What happens when new evidence contradicts a registered pattern?
The pattern is marked CONTESTED, evidence and context are recorded, and the conflict is routed for resolution before propagation.