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Gdd Router

Community
hegemonart
gdd-router

Routes a /gdd command to fast|quick|full path + S|M|L|XL complexity_class and returns {path, complexity_class, model_tier_overrides, resolved_models, estimated_cost_usd, cache_hits}. A SKILL.md prompt the model executes to emit a routing-decision JSON from rule tables (no separate agent spawn). Optional/advisory - invoked only by the skills that opt into routing; the budget-enforcer hook tolerates its absence. Read by hooks/budget-enforcer.ts.

Overview

Publisherhegemonart
Repositoryget-design-done
Skill namegdd-router
Stars
3
Forks
1
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by hegemonart on GitHub. Read the source before you install it.

Installation

Install the Gdd Router AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/router
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hegemonart/get-design-done.git /tmp/get-design-done
mkdir -p .claude/skills
cp -r /tmp/get-design-done/scripts/skill-templates/router .claude/skills/gdd-router
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gdd Router in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Gdd Router on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Gdd Router is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

gdd-router

Role

You are a deterministic routing skill. You do not spawn agents. You read .design/budget.json, reference/model-prices.md, .design/cache-manifest.json (if present), and the agent frontmatter list, then emit a single JSON object describing the planned spawn graph. The budget-enforcer hook (hooks/budget-enforcer.ts) consumes your output on every Agent tool call.

Invocation Contract

  • Input: intent-string (e.g., "run discover stage on greenfield project") + optional comma-separated list of target artifacts (files this command will touch).
  • Output: a single JSON object to stdout - nothing else on the line, no prose wrapper:
    json
    {
      "path": "fast",
      "complexity_class": "M",
      "model_tier_overrides": {"design-verifier": "haiku"},
      "resolved_models": {
        "design-reflector": "gpt-5",
        "design-context-checker": "gpt-5-nano",
        "design-verifier": "gpt-5-nano"
      },
      "estimated_cost_usd": 0.034,
      "cache_hits": ["design-context-builder:abc123"]
    }
  • path enum: fast (single Haiku + no checkers), quick (Sonnet mappers + Haiku verify), full (Opus planners + full quality gates). Stays unchanged for back-compat per D-05.
  • complexity_class enum: S | M | L | XL (Phase 25 / D-04, D-05). Additive to path - existing consumers reading only path keep working. Mapping is documented in the Path Selection Heuristic table below.
  • model_tier_overrides merges agent frontmatter default-tier with .design/budget.json.tier_overrides - budget.json wins per D-04. Enum stays opus|sonnet|haiku for back-compat across all 14 runtimes; consumers that need the concrete model name for the active runtime read resolved_models instead.
  • resolved_models is a per-agent map of concrete model IDs for the runtime in use (Phase 26 / D-07). Keys are agent names; values are runtime-specific model strings (e.g. "gpt-5" under codex, "gemini-2.5-pro" under gemini, "claude-opus-4-7" under claude) or null when the resolver can supply no model (missing tier-map row, missing tier on the row). Additive to model_tier_overrides - existing consumers reading the tier-name map keep working unchanged; new consumers (budget-enforcer cost computation, cost telemetry, bandit posterior store) read resolved_models for runtime-correct cost. See Runtime-aware model resolution below for the computation contract.
  • estimated_cost_usd is the sum of per-spawn estimates using the D-06 formula and reference/model-prices.md.
  • cache_hits is a list of {agent}:{input-hash} strings that exist in .design/cache-manifest.json and are within TTL; emitting a hit lets the hook short-circuit that spawn per D-05.

Output schema versioning

The router output contract is additive across phases. The current shape (Phase 26, v1.26.0) carries:

FieldAdded inStatus
pathv1.10.1 (10.1-01)stable
model_tier_overridesv1.10.1 (10.1-01)stable, enum unchanged
estimated_cost_usdv1.10.1 (10.1-01)stable
cache_hitsv1.10.1 (10.1-01)stable
complexity_classv1.25.0 (25-02)stable, additive
resolved_modelsv1.26.0 (26-04)stable, additive

Existing consumers reading any subset of the older fields keep working unchanged across these bumps - the schema is a strict superset at every phase boundary. New fields are documented inline in this skill rather than in a separate JSON-schema file (the SKILL is the contract - same convention Phase 25 followed for complexity_class).

Path Selection Heuristic

The router emits path (3-tier: fast|quick|full, legacy enum, stable for back-compat) AND complexity_class (4-tier: S|M|L|XL, Phase 25 / D-04 additive). Full mapping table, bucket-assignment signal list, --dry-run downgrade rule, and the S-class short-circuit semantics live in ./router-rules.md#path-selection-heuristic. The S-class short-circuit is essential: when complexity_class would be S, the router does not run; the deterministic skip list lives in the {{command_prefix}}* SKILL.md entry, and the budget-enforcer hook treats "no payload + matching command name" as the S signal.

Cost Estimation Algorithm

Standard cost-estimation pseudocode (sum over planned spawn graph; per-agent (in_tok / 1e6) * in_rate + (out_tok / 1e6) * out_rate using ./reference/model-prices.md) lives in ./router-rules.md#cost-estimation-algorithm.

Runtime-aware model resolution

Computation contract for resolved_models, implementation surfaces (scripts/lib/runtime-detect.cjs + scripts/lib/tier-resolver.cjs), per-agent emission rules (including the JSON-null contract), and the Claude-runtime back-compat assertion live in ./router-rules.md#runtime-aware-model-resolution. Top-line: model_tier_overrides keeps its opus|sonnet|haiku enum for back-compat; resolved_models runs the per-runtime translation additively on top.

Cache-Hit Detection

Delegate to skills/cache-manager/SKILL.md (Plan 10.1-02). The router lists candidate {agent}:{input-hash} tuples; the cache-manager confirms freshness against TTL from budget.json.cache_ttl_seconds.

Integration Point

The router is optional and advisory, not a universal first step. Only the handful of skills that explicitly opt into routing reference it (today: the root pipeline SKILL.md / {{command_prefix}}handoff, and {{command_prefix}}style documents that it deliberately does not invoke the router because it is a leaf invocation). The pipeline stage skills (explore / plan / design / verify) do not spawn the router. When a skill does invoke it, the flow is: invoke the router via Task or inline invocation; receive the JSON blob; pass it to downstream agents as context so the budget-enforcer hook has the router decision available in tool_input metadata when the first Agent spawn fires.

When no skill supplies a router decision, the budget-enforcer hook reads tool_input.context.router_decision as absent and falls back to its legacy back-compat path - the router's absence is tolerated by design, never an error.

Failure Modes

If .design/budget.json is missing, assume defaults from reference/config-schema.md per D-12. If reference/model-prices.md is missing, emit estimated_cost_usd: null and log a warning - do not block.

Emitting capability_gap on unmatched intent

When the router cannot resolve intent-string to a known agent (no description match, no default-tier rule, no path-selection fallback), emit ONE capability_gap event with source: "router" before returning the conservative-fallback JSON. Feeds Phase 29 Stage-0 telemetry - see ./capability-gap-emitter.md for the synchronous Node snippet, semantic notes (suggested_kind = "agent", MCP-probe exclusion per D-08, back-compat invariant on router output), and the opaque-extras payload routing through appendChainEvent.

Emitting router_pick on a resolved pick

When the router DID resolve a pick - it has the path/complexity_class/resolved_models decision and is about to return the decision JSON - emit ONE router_pick event (source: "router") recording which skill/agent was auto-picked, as the last step before returning. Side-effect only; the output JSON contract is UNCHANGED. Feeds the D-02 under-reached-skill instrument (Phase 33 baselines per-skill pick rates) - see ./router-pick-emitter.md for the synchronous Node snippet, the 7-field no-PII payload (context_hash only - never the raw prompt), and the opaque-extras routing through appendChainEvent.

Non-Goals

The router does not: (a) make a model call, (b) write files, (c) enforce budget caps (that's the hook's job), (d) learn from history (Phase 11 reflector territory per D-07). The router does not author capability-gap CLUSTERS - Stage-0 emit is single-event-per-failure. Aggregation across events is Plan 29-03's reflector pass.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Gdd Router AI skill do?

Routes a /gdd command to fast|quick|full path + S|M|L|XL complexity_class and returns {path, complexity_class, model_tier_overrides, resolved_models, estimated_cost_usd, cache_hits}. A SKILL.md prompt the model executes to emit a routing-decision JSON from rule tables (no separate agent spawn). Optional/advisory - invoked only by the skills that opt into routing; the budget-enforcer hook tolerates its absence. Read by hooks/budget-enforcer.ts.

Why use Gdd Router on TypingMind?

Because you install it once and use it with any model. Gdd Router is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Gdd Router in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/router. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Gdd Router?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Gdd Router?

As many as you like. As long as a model supports skills, you can use Gdd Router with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Gdd Router AI skill free?

Yes. It is published on GitHub by hegemonart under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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