add-feature
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Implement a feature using the knowledge layer — spec first, surgical diffs, tests before done, knowledge updated after.
AI-Fication Kit: A Simple & Elegant Way for Making any Codebase AI-native through Scaffolded, Human-verified Context and Development Loop.
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Implement a feature using the knowledge layer — spec first, surgical diffs, tests before done, knowledge updated after.
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Deep, judgement-based adversarial code audit — hunts for stale cross-references, unescaped interpolation, platform gaps, generated-file ownership conflicts, and cross-module consistency rot that no mechanical check can catch. Read-only; writes a dated findings report.
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Always-on pointer to this repo's AI knowledge layer and provenance rule.
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Run verification and drift checks on the AI knowledge-base to identify missing documentation or stale references.
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Bootstrap the ai/guide maps and diagrams (the cold-start pass). Drafts everything as [inferred] for a human to audit.
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Deep-mine the source code to discover implemented features; writes ai/analysis/FEATURECATALOG.md.
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Fix a bug using the knowledge layer — reproduce first, failing regression test, root cause, surgical fix, review and ledger entry after.
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Dry-run the add-feature workflow for a proposed feature — friction report and per-phase readiness score, without writing code.
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Audit every ai/ file for gaps, stale placeholders, and inconsistencies after cold-start. Produces a prioritized findings report.
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Diagnostic gate that checks CLAUDE.md and AGENTS.md for completeness, consistency, and stale artifacts — run right after /cold-start and before /add-feature. Read-only; produces a findings report.
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Review a completed change against its spec in fresh context — evidence-based checks, severity-ranked findings, a written verdict for the human's merge decision.
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Holistic assessment of the AI knowledge layer on a 5-level maturity scale; flags agent-blocking gaps.