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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
$ npx -y skills add Tibsfox/gsd-skill-creator --skill intent-router --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/intent-routerContext preview
The summary Claude sees to decide when to auto-load 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
name: intent-router description: > 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 retrieval depth so downstream handlers can be specialised. Backed by Pre-Route (arxiv 2605.10235v2) and MemFlow (arxiv 2605.03312v1), which together show LLMs possess latent routing ability elicitable via a structured prompt — and that externalising the routing decision improves small-model performance by ~2x. Triggers: "route this", "what strategy", "before retrieving", "intent classification", or any query whose ideal handling depends on what KIND of question it is. user-invocable: true version: 1.0.2 format: 2025-10-02 triggers: - "route this query" - "what retrieval strategy" - "what reasoning strategy" - "intent classification before retrieval" - "front-run the memory access" updated: 2026-05-16 status: ACTIVE source: arxiv 2605.10235v2 (Pre-Route), 2605.03312v1 (MemFlow)
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:
The empirical claim: a single fixed strategy is dominated on every axis by the conditional strategy.
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.
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:
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.
An adaptive learning and coprocessor architecture for Claude Code, built as an extension to GSD (open-gsd)
Repo: Tibsfox/gsd-skill-creator
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