Follow-up R4 from the microsoft/AI-Engineering-Coach exploration (docs/superpowers/explorations/2026-06-16-ai-engineering-coach-findings.md, issue #340).
Idea
The MS Skill Finder detects repeated prompt patterns and proposes promoting them to reusable skills. Our analog — the reflection-driven regression-detection GC rule — is failure-oriented (recurring surprises → proposed constraints). It does not surface reuse-oriented candidates (recurring work shapes → proposed skills).
Scope
- A detector that groups reflections / task patterns by shape (not just failure theme) and flags 2+ recurrences as skill-authoring candidates.
- Feeds the skill-authoring pipeline (e.g.
component-design-with-tdad) rather than /harness-constrain.
- Likely a new GC rule or a
/reflect-adjacent surface; emits candidates with evidence (dates, the repeated shape) for human disposition.
Constraint
Without session-log data this works over reflections/specs only (artifact substrate). A richer prompt-level version depends on R1's behavioural data.
Follow-up R4 from the microsoft/AI-Engineering-Coach exploration (
docs/superpowers/explorations/2026-06-16-ai-engineering-coach-findings.md, issue #340).Idea
The MS Skill Finder detects repeated prompt patterns and proposes promoting them to reusable skills. Our analog — the reflection-driven regression-detection GC rule — is failure-oriented (recurring surprises → proposed constraints). It does not surface reuse-oriented candidates (recurring work shapes → proposed skills).
Scope
component-design-with-tdad) rather than/harness-constrain./reflect-adjacent surface; emits candidates with evidence (dates, the repeated shape) for human disposition.Constraint
Without session-log data this works over reflections/specs only (artifact substrate). A richer prompt-level version depends on R1's behavioural data.