Intent Router
SkillDocs & knowledgeLets your agent classify a query's intent and pick the right retrieval strategy, an intent claude skill for routing.
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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About this skill
Classify the information-need of a query and dispatch it to the appropriate retrieval or reasoning strategy. Use before read-side memory access, before multi-strategy retrieval, or any time you'd otherwise default to "one retriever for everything". Returns a strategy label, a token budget, and a ret
What this skill tells your AI
The instructions your AI receives, as published by tibsfox/gsd-skill-creator in project-claude/skills/intent-router/SKILL.md and read by Ahel’s review.
Why
The 2026 frontier converges on one architectural shift: route → strategy → act, replacing the historical one-retriever-for-everything pattern. Different query intents demand categorically different handlers:
- lookup — direct factual recall, single retrieval, small budget
- multi-hop — chain through intermediate facts, deeper retrieval, larger budget
- global — summarise/aggregate across a broad slice, retrieve broadly + compress
- verification — check a claim against evidence, retrieve narrowly + compare
- deep-reason — escalate to a higher-tier model, retrieve generously, allow chain-of-thought
- lexical — exact-match / keyword (BM25 territory), no embedding, narrow budget
The empirical claim: a single fixed strategy is dominated on every axis by the conditional strategy.
How
Before any memory access or multi-strategy retrieval, run a structured-prompt classification pass that returns:
{
"strategy": "lookup|multi-hop|global|verification|deep-reason|lexical",
"token_budget": 2000,
"retrieval_depth": 5,
"refinement_policy": "none|verbal-rerank|consensus-check",
"confidence": 0.0
}
If confidence < 0.6, fall back to the broadest strategy (global) so the system degrades gracefully rather than mis-routing.
Output format vs confidence
The full JSON envelope is the canonical form when downstream consumers read it programmatically (typed pipelines, gating code). For inline human-facing routing decisions, taper the ceremony to match the confidence:
- confidence ≥ 0.9 — emit just the strategy label on one line (e.g.,
route: lexical). The other fields default; don't print the envelope. - 0.6 ≤ confidence < 0.9 — emit the strategy + 1-line rationale. Skip the JSON.
- confidence < 0.6 — emit the full JSON envelope with the
globalfallback explicit. This is the only case where the structured output earns its tokens.
Rationale: a CTA audit (.planning/patterns/skill-audits/intent-router-2026-05-16.md) found that high-confidence lexical-class probes produced full JSON envelopes that downstream consumers did not read. The output was surface-anchoring without information gain.
When to skip
- You're already in a typed pipeline whose strategy is fixed (e.g., a search command that's explicitly lexical).
- The query is short enough that the routing call costs more than it saves (rule of thumb: < 20 tokens of question text).
- Scoped exact-match lookup in a known directory — the surface form is so cleanly lexical that classification cannot change the outcome. Triggers: "find files in
src/X/that contain literal stringY", "grep forZunderpath/", "list all files referencingIDENT". The routing decision is structurally pinned tolexical; justgrep. Added 2026-05-16 from CTA audit (.planning/patterns/skill-audits/intent-router-2026-05-16.md) — the bounded probe showed the routing ceremony added overhead without changing the action. - You're in a streaming/interactive context where added latency dominates the routing payoff.
Integration
wrap:executeandwrap:verify— invoke intent-router as the FIRST step in handler dispatch.gsd-graphify— already does query routing internally; intent-router exposes the same primitive uniformly to other skills.- Future
src/memory/strategies/directory will be the typed substrate; this skill is the policy on top.
Cross-references
- Rosetta concept #7 (Intent Routing) — canonical definition
- College:
agent-systems / agent-memory / agent-intent-routing - Related skills:
graphify(when query is graph-shaped),gsd-explore(when intent is exploratory)
Signals
- GitHub stars
- 70
- Forks
- 10
- Last commit
- Jul 2026
Advanced
- Item type
- skill
- Key
intent-router-tibsfox- Source
- github.com/tibsfox/gsd-skill-creator
github.com/tibsfox/gsd-skill-creator
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