Extract patterns, decisions, anti-patterns, and quality-rule candidates from campaign artifacts.
npx skills add https://github.com/mferris77/SpringBoard --skill learn-mferris77
Please help me install this Agent Skill. Skill: learn Source: https://github.com/mferris77/SpringBoard/tree/main/vscode-citadel-harness/skills/learn Command: npx skills add https://github.com/mferris77/SpringBoard --skill learn-mferris77
Campaigns accumulate decisions, telemetry, and informal lessons that are hard to surface and reuse. This Skill automates extraction of successful patterns, anti-patterns, key decisions, and candidate quality rules from completed campaign artifacts so teams retain institutional knowledge and reduce repeated mistakes.
Run the learn skill on a completed campaign to extract patterns, write .planning/knowledge files, and optionally append high-confidence quality rules to your harness.
High-intent search queries and answers about installing and using this skill.
Pattern extraction from postmortems processes completed campaign markdowns and audit.jsonl telemetry to identify successful patterns, anti-patterns, and key decisions, writing actionable knowledge to .planning/knowledge/.
Campaign retrospective learning parses audit.jsonl telemetry and campaign artifacts to infer decisions and outcomes, automatically capturing institutional knowledge to prevent repeated mistakes across engineering harnesses.
Yes, you can generate quality rules from campaign telemetry by extracting evidence-backed, medium-or-higher confidence rule candidates and appending non-duplicative rules directly to .claude/harness.json for automated code-quality enforcement.
Capturing anti-patterns from engineering campaigns involves analyzing failed patterns in postmortems and audit logs, then generating knowledge files with avoidance guidance and evidence links for future reference.
Yes, completed campaign files, postmortems, and audit.jsonl telemetry are required inputs, as the extraction process relies on these artifacts to identify actionable patterns and decisions for your knowledge base.
You should avoid automated pattern extraction when campaigns lack sufficient evidence or when proposed rules fall below the medium confidence threshold, as the system enforces evidence-backed requirements to prevent low-quality knowledge generation.
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