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intent-router

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.

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包含的文件(1)

  • SKILL.md4.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Intent Router

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 global fallback 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 string Y", "grep for Z under path/", "list all files referencing IDENT". The routing decision is structurally pinned to lexical; just grep. 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:execute and wrap: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)

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