Prompt Router

Maps a classified request to the optimal skill set, agent strategy, Context7 directives, and output format. Outputs a PromptPlan JSON consumed by the downstream prompt-generator skill.

When to Use

• After running /request-classifier and you have a RequestClassification object
• When a user asks "what skills should I use for this task?"
• When a user asks "should I use subagents or a tmux team?"
• When a user asks "do I need Context7 for this?"
• As Step 3 in the full /generate-prompt pipeline

Quick Routing Guide

Routing Steps

Step 1: Read the classification

Accept a RequestClassification object from /request-classifier or extract it inline from the user's request:

{
  "type": "feature|bug-fix|research|improvement|pipeline|frontend|quick-task|documentation",
  "complexity": 1-10,
  "plan_mode": true|false,
  "domain_signals": ["extraction", "graph", "model", "debug", ...],
  "scope": "single-file|multi-file|cross-cutting",
  "estimated_files": 0
}

If no classification is available, run the classification heuristics from /request-classifier taxonomy before proceeding.

Step 2: Read the routing matrix

Open references/routing-rules.md and locate the row matching type + complexity_band:

• Simple: complexity 1-3
• Medium: complexity 4-6
• Complex: complexity 7-10

Step 3: Match classification to routing matrix row

Find the exact row. If the type is ambiguous (e.g., a feature with pipeline signals), use the more specific type (pipeline beats feature).

Priority order when multiple types match:

1. pipeline — most specific, always wins
2. frontend — specific domain
3. bug-fix — has fixed strategy
4. feature / improvement / research — use complexity band
5. quick-task / documentation — fallback

Step 4: Apply domain skill selection logic

Check domain_signals array from the classification and append additional skills:

Deduplicate the final skill list (routing matrix skills + domain signal skills).

Step 5: Build Context7 directives if needed

Check the context7 column of the matched routing row. If yes (or conditional and condition is met), build directives using the templates in references/routing-rules.md:

• For each library relevant to the request, add one directive: resolve-library-id for "{library}" → query-docs for "{topic from request}"

Always include Context7 for:

• pipeline type → LangGraph + LangChain
• research type → any library mentioned in the request
• feature with explicit library version in request → that library

Step 6: Select agent strategy template

Use references/agent-strategies.md and select the template matching the routing matrix agent_strategy column:

• solo → Template A
• subagent-dispatch → Template B
• tmux-team → Template C

Fill in the {placeholder} variables from the classification data.

Step 7: Determine output format and path

If user explicitly requested "save" or "prompt-pack" → always use prompt-pack format. If user requested "no plan" → downgrade from prompt-pack to copy-paste unless still complex.

Step 8: Output the PromptPlan JSON

Assemble and return the complete PromptPlan object:

{
  "classification": {
    "type": "...",
    "complexity": 0,
    "plan_mode": true,
    "domain_signals": [],
    "scope": "...",
    "estimated_files": 0
  },
  "selected_skills": ["/planning-with-files", "/langgraph-fundamentals"],
  "agent_strategy": "tmux-team|subagent-dispatch|solo",
  "agent_config": {
    "panes": [],
    "subagents": [],
    "solo": true
  },
  "context7_directives": [],
  "output_format": "prompt-pack|copy-paste|terminal",
  "output_path": "docs/sprint-artifacts/prompt-packs/",
  "plan_mode": true,
  "plan_type": "full|debug|research|refactor|none",
  "verification_items": [
    "uv run ruff check .",
    "uv run pytest tests/",
    "cd frontend && npm run build"
  ]
}

The PromptPlan JSON is passed directly to the /prompt-generator skill (S4) as its primary input.

Verification Items by Type

Always include these base verification items in verification_items:

Common Routing Scenarios

"Add a new extraction provider for MinerU v3" → type=pipeline, complexity=7, skills=[/langgraph-fundamentals, /acm-observability, /planning-with-files, /pydantic-models-py], strategy=tmux-team, Context7=LangGraph+LangChain, format=prompt-pack

"Fix the timeout error in the building extraction graph" → type=bug-fix, complexity=5, skills=[/systematic-debugging, /acm-observability, /langgraph-fundamentals], strategy=solo, Context7=conditional (LangGraph if API-related), format=copy-paste

"Rename the extract_all_rows function" → type=quick-task, complexity=1, skills=[], strategy=solo, Context7=no, format=terminal

"Investigate why correction LLM calls are spiking" → type=research, complexity=6, skills=[/acm-observability, /planning-with-files, /langgraph-fundamentals], strategy=subagent-dispatch (parallel research panes), Context7=LangGraph, format=prompt-pack

"Add the building summary panel to the source detail page" → type=frontend, complexity=5, skills=[/react-best-practices, /next-best-practices], strategy=solo, Context7=no, format=copy-paste

"Refactor all pre-extraction stages to reduce LLM calls" → type=improvement, complexity=8, skills=[/planning-with-files, /subagent-driven-development, /verification-before-completion, /systematic-debugging], strategy=subagent-dispatch-with-gates, Context7=conditional (LangGraph if graph patterns change), format=prompt-pack