skill-router
Skill Router
Coverage
- Routing by keyword pattern: matching inbound query terms to skill
keywordsarrays to identify the best candidate - Routing by trigger label: matching explicit skill-router labels (
triggersfield) to eliminate ambiguity when the intent is declared - Routing by file path: matching touched or mentioned file paths against skill
pathsarrays for file-activated skills - Fallback ordering: how to rank skills when multiple candidates score equally, including
scopeandtypetiebreakers - Coverage gaps: detecting when no skill matches a request and how to surface that gap as an authoring signal
Philosophy
Routing is adversarial against convenience. The tempting move — "if nothing matches exactly, just pick the closest skill and activate it" — is the one that silently degrades every agent that depends on the router. A wrong skill that activates confidently is worse than a coverage gap that surfaces loudly, because silent wrongness has no signal for anyone to fix. The router's job is to produce either a certain winner or an explicit non-answer, never a confident guess.
Four principles follow from that stance:
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Use when deciding responsive page or screen structure: section order, scan pattern, grid/flex composition, breakpoints, viewport hierarchy, responsive media, and density. Do NOT use for user-goal decomposition (use `task-analysis`), navigation taxonomy (use `information-architecture`), visual polish (use `visual-design-foundations`), or component/token contracts (use `design-system-architecture`).
8context-graph
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8visual-design-foundations
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Use when extracting durable project knowledge from code, docs, issues, incidents, reports, screenshots, or conversations into reusable context such as skills, ADRs, glossaries, context docs, or memory. Do NOT use for writing a new skill contract (use `skill-scaffold`), maintaining library tooling (use `skill-infrastructure`), or generic documentation polish (use `documentation`).
6problem-framing
Use when a team is converging on solutions before agreeing on the problem, when a brief reads as a feature request, when symptoms and root needs are tangled, or when assumptions need surfacing before design work proceeds. Do NOT use for code-level bug triage, runtime failure diagnosis, or root-cause analysis of system errors — those are engineering investigation tasks, not design problem framing.
6ai-native-development
Use when reasoning about agent autonomy levels, designing auto-improve loops, evaluating AI-generated code quality, or measuring agent productivity in an LLM-assisted codebase. Covers Karpathy's three eras of software (1.0 explicit / 2.0 learned / 3.0 natural-language), the vibe-coding-vs-agentic-engineering distinction, the 0–5 autonomy slider with task-type recommendations, the one-asset / one-metric / one-time-box AutoResearch loop, Software 3.0 productivity metrics, and the documented quality regressions of ungated AI-generated code (the 'vibe hangover'). Do NOT use for choosing a specific autonomy-loop topology (use `agent-engineering`), for the per-prompt authoring discipline (use `prompt-craft`), or for reviewing the AI-generated code that comes out of a Software 3.0 workflow (use `code-review`).
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