Gdd Health

Community

hegemonart

gdd-health

Reports .design/ artifact health - staleness, missing files, token drift, broken state transitions. Activates for requests involving checking .design artifact health, staleness, token drift, or broken state transitions.

Overview

Publisher	hegemonart
Repository	`get-design-done`
Skill name	`gdd-health`
Stars	3
Forks	1
Bundled files	2
License	MIT
Links	Skill source Repository

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.
2 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 Health AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/health
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.

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/health .claude/skills/gdd-health

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gdd Health 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 Health 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 Health 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.

{{command_prefix}}health

Role: Report the health of the .design/ directory. Print a score and list the checks that failed.

Checks

Artifact inventory - ls -la .design/*.md with size and mtime. Print a table.
Missing expected artifacts - by stage field from the mcp__gdd_state__get snapshot:
- brief expects BRIEF.md
- explore expects DESIGN.md, DESIGN-DEBT.md, DESIGN-CONTEXT.md
- plan expects DESIGN-PLAN.md
- design expects DESIGN-SUMMARY.md
- verify expects DESIGN-VERIFICATION.md FAIL per missing.
Token drift - wc -c .design/DESIGN.md .design/DESIGN-CONTEXT.md; approx tokens = bytes/4. WARN if combined >40000.
Aged DESIGN-DEBT - items in .design/DESIGN-DEBT.md not touched in >14 days (file mtime). WARN.
Broken state transitions - stage field from the snapshot inconsistent with artifacts present (e.g. stage=verify but DESIGN-SUMMARY.md missing). FAIL.
Pending sketch/spike wrap-ups - any .design/sketches/* or .design/spikes/* directory lacking a SUMMARY.md. WARN.
Seed germination - scan .design/SEEDS.md (if present) for seeds whose trigger keywords match the snapshot or CYCLES.md content. List as "Seed ready: ".

State snapshot

Call mcp__gdd_state__get once at the start to pull the snapshot used by checks 2, 5, and 7. Aggregate health math stays prose-level:

Count available connections from <connections>.
Count open blockers from <blockers> where resolved is absent.
Count pending must-haves from <must_haves> where status: "pending".

Output


━━━ Design health ━━━
Artifacts:
  BRIEF.md           2.1 KB   2026-04-14
  DESIGN.md          18.4 KB  2026-04-17
  DESIGN-CONTEXT.md  7.2 KB   2026-04-17

Checks:
  [PASS] Missing artifacts
  [WARN] Token drift (42,100)
  [PASS] Aged DESIGN-DEBT
  [PASS] State transitions
  [PASS] Sketch/spike wrap-ups
  [PASS] Seed germination

Health: 5 / 6 checks passing.
━━━━━━━━━━━━━━━━━━━━━

Figma-extract readiness (figma_extract)

After the health table, the gdd_health MCP surface (scripts/lib/health-mirror/index.cjs) reports a figma_extract check so a user knows whether figma-extract is usable. The detail is one of three exact strings:

figma extract: ready (token set) - FIGMA_TOKEN (or FIGMA_PERSONAL_ACCESS_TOKEN) is present (status ok).
figma extract: token missing - no token env is set (status warn).
figma extract: plugin sync needed for variables (Free tier detected) - token present but a prior pull recorded a 403/skip on the Variables REST path, so run the plugin-sync step (status warn).

Token PRESENCE only is detected (D-10) - the token value is never read, logged, or shown. The Free-tier signal is read from the local raw-pull cache only; no network call is made.

Skill-discipline bootstrap (skill_discipline)

The gdd_health MCP surface also reports a skill_discipline check (Phase 32) confirming the using-gdd SessionStart bootstrap is live - detail is one of three exact strings:

skill-discipline: ready - skills/using-gdd/SKILL.md exists AND hooks/hooks.json SessionStart wires inject-using-gdd.sh (status ok).
skill-discipline: missing using-gdd (skill absent) or skill-discipline: hook not wired (skill present, no SessionStart inject) - both warn. The MCP surface also reports a harness_freshness check (Phase 44): per-harness last_verified age, status-aware (only tested harnesses warn at 60d / fail at 180d; others n/a). Full taxonomy in HARNESSES.md; refresh with npm run verify:harness <id>.

Check MCP registration (gdd-mcp)

After the health table, inspect whether gdd-mcp (Phase 27.7+) is registered with any installed harness and render a one-line status row. Dismissable via .design/config.json#mcp_nudge=false. Non-blocking: failure paths render MCP server: unknown rather than crash. Full detection procedure (dismissal check, detection via scripts/lib/install/mcp-register.cjs, row rendering for claude/codex/both/neither, fallback) lives in ./health-mcp-detection.md.

Update notice (safe-window surface)

After the health table, emit the plugin-update banner if one is present:

bash
[ -f .design/update-available.md ] && cat .design/update-available.md

Written by hooks/update-check.sh; suppressed mid-pipeline and when the latest release is dismissed.

Skill-length report

After the health table, surface the Phase 28.5 skill-authoring contract drift signal by running node scripts/validate-skill-length.cjs --quiet --json and reading summary from stdout. Print two prose lines:

Skill-length: <total> total | <clean> clean | <warnings> warn (>=100) | <blockers> block (>=250)
If blockers > 0: list each blocker as a row - <name> (<lines> lines). Else: print All skills within contract.

Thresholds: warn >=100, block >=250 (D-01). Strict description-format off by default (D-02). Then add two compact Phase 50 lines from scripts/lib/manifest/skills.json: Skills: <n>/<total> in v3 description form (descriptions matching /Activates for requests involving/i) and Composition: <edges> edges | <cycles> cycles (composes_with/next_skills fan-out; cycles via scripts/validate-composition-graph.cjs when present, else 0). See ./health-skill-length-report.md for the JSON shape and threshold rationale.

Do Not

Do not mutate STATE.md - this skill is read-only. Only mcp__gdd_state__get is permitted.

HEALTH COMPLETE

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 Health AI skill do?

Why use Gdd Health on TypingMind?

Because you install it once and use it with any model. Gdd Health 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 Health in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/health. 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 Health?

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 Health?

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

Is the Gdd Health 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

{{command_prefix}}health

Checks

State snapshot

Output

Figma-extract readiness (figma_extract)

Skill-discipline bootstrap (skill_discipline)

Check MCP registration (gdd-mcp)

Update notice (safe-window surface)

Skill-length report

Do Not

HEALTH COMPLETE

Bundled files

Frequently asked questions

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