autosearch-model-routing

name	autosearch:model-routing
description	Advisory skill — three-tier (Fast / Standard / Best) model routing catalog for autosearch skills. Tells the runtime AI which tier each leaf skill needs, and how to escalate or de-escalate. Autosearch does not switch models itself; the runtime AI is the decision-maker.
version	0.1.0
layer	meta
domains	["meta","model-routing"]
scenarios	["model-tier-decision","cost-optimization","quality-vs-speed"]
trigger_keywords	["model tier","模型档","which model","cost","tier decision"]
model_tier	Standard
auth_required	false
cost	free
experience_digest	experience.md

Model Tier Routing — Advisory

Routing principle: most steps use the runtime's cheapest model; only the critical 1–2 steps use the best model.

Autosearch stamps every leaf skill with a model_tier suggestion. This skill tells the runtime AI what the three tiers mean, which skills default to which tier, and when to escalate or de-escalate.

Three Tiers

Tier	Typical runtime pick	When used	Share of skills
Fast	Claude Haiku / GPT-5-mini / Gemini 2.5 Flash / Qwen local	Retrieval, normalization, schema checks, URL reading, metadata	~60%
Standard	Claude Sonnet / GPT-5.4 / Gemini 2.5 Pro	Semantic ranking, evidence extraction, mid-complexity planning	~25%
Best	Claude Opus / GPT-5 / Gemini 2.5 Ultra	Clarify, decompose, synthesize, evaluate delivery, skill evolution — the 1-2 steps that shape everything	~15%

Tier Assignments

Each autosearch skill carries model_tier: Fast|Standard|Best in its frontmatter. The runtime AI reads that field before choosing which provider/model to call.

Best (~13 skills — the critical 1-2 steps per session)

• clarify — disambiguate intent (wrong clarification cascades)
• systematic-recall — global recall planning (missed angles compound)
• decompose-task — breaking a multi-part problem
• synthesize-knowledge — produce frameworks, not link lists
• evaluate-delivery — quality gate on final output
• knowledge-map — cross-evidence relation graph
• check-rubrics / generate-rubrics — rubric-driven evaluation
• auto-evolve / create-skill — anything that changes future behavior
• goal-loop — multi-round goal convergence
• graph-search-plan (when present) — research plan as graph
• perspective-questioning (when present) — multi-persona question generation
• reflective-search-loop (when present) — explicit gaps / visited / bad-URLs loop

Standard (~20 skills — semantic judgment, structurable)

• select-channels — pick 5-10 channels from 41
• gene-query — combinatorial query generation
• consult-reference — prior art lookup
• rerank-evidence — semantic ranking of results
• llm-evaluate — per-item relevance score
• anti-cheat — spam / score-gaming detection
• assemble-context — token-budgeted context assembly
• extract-knowledge — structured extraction from text
• fetch-crawl4ai / fetch-playwright / fetch-firecrawl / follow-links
• experience-compact — rule promotion
• observe-user — user preference inference
• research-mode — speed vs. deep choice
• delegate-subtask (when present) / trace-harvest / citation-index / recent-signal-fusion
• interact-user / pipeline-flow / outcome-tracker

Fast (~60 skills — bulk of every session)

• All 41 channel skills (search-*) — retrieval, not reasoning
• fetch-jina, fetch-webpage — URL → Markdown
• yt-dlp + three video-to-text transcription skills — mechanical extraction
• mcporter routing skill
• discover-environment, provider-health — env probing
• normalize-results, extract-dates — schema + dedupe
• autosearch:router — routing decision, no deep reasoning
• experience-capture — append-only event write, often no LLM
• context-retention-policy (when present) — keep-last-k rules

Escalation Rules

Start a step at its default tier. Escalate only when:

• Conflicting evidence from multiple sources needs semantic reconciliation → Standard from Fast.
• Final synthesis or skill-evolution output depends on this step → Best from Standard.
• User explicitly asks for deeper analysis / higher quality on the topic.

De-escalate when:

• A Standard step is running on highly structured / deterministic input (e.g. rerank-evidence on 3 items with clear metadata) → drop to Fast.
• Exploratory / draft iteration loop — first passes can be Fast, final pass Best.

Runtime Advisory (non-binding)

Autosearch cannot force the runtime to change models. The model_tier field is a suggestion to help the runtime AI choose a provider/model route that matches the quality bar of the step. The runtime AI may ignore the advisory if it has better information (e.g. user specified a fixed model).

Boss Rules

• Cost: run the cheapest model that clears the quality bar; reserve best model for the 1-2 steps that shape the whole session outcome.
• Judge: keep LLM-as-judge (pairwise A/B preference, open_deep_research pattern). Do not invent N-dim × 0/3/5 rubrics.
• AVO: any Best-tier skill that modifies SKILL.md (like auto-evolve) must commit via scripts/committer and be reversible via git revert.

Quality Bar

• Evidence items have non-empty title and url.
• No crash on empty or malformed API response.
• Source channel field matches the channel name.