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Gdd Progress

Community
hegemonart
gdd-progress

Shows current pipeline position and routes to next action. --forensic runs 6-check integrity audit. Activates for requests involving showing current project state, routing to the next action, or a status check.

Overview

Publisherhegemonart
Repositoryget-design-done
Skill namegdd-progress
Stars
3
Forks
1
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Gdd Progress 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/progress
  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/progress .claude/skills/gdd-progress
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gdd Progress 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 Progress 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 Progress 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.

@reference/retrieval-contract.md

{{command_prefix}}progress

Role: Show current position in the pipeline and recommend the next action. With --forensic, run a 6-check integrity audit.

Step 1 - Read state

Two paths - MCP preferred when available, file-read fallback otherwise.

MCP path (preferred)

When the harness exposes mcp__gdd_status (Phase 27.7+, registered via npx @hegemonart/get-design-done --register-mcp):

  1. Call mcp__gdd_status (no args). Returns {phase, branch, last_decisions, last_completed_plans, blocker_count} in one call.
  2. If you need stage / task_progress for the output line, call mcp__gdd_phase_current (no args). Returns {phase, stage, task_progress, status}.
  3. Skip to Step 2.

This path loads the full priming context in 1–2 MCP calls (~3s, ~32k tokens - Storybloq benchmark).

File-read path (fallback)

When MCP tools are not available, fall back to the legacy flow:

  1. Call mcp__gdd_state__get if exposed (Phase 20 STATE.md mutator MCP) → parsed state object. Otherwise, Read .design/STATE.md and parse the frontmatter + <position>, <decisions>, <plans> sections.
  2. Extract: stage, cycle, last_checkpoint, task_progress, status, decisions.length, open todos from .design/TODO.md (count unchecked - [ ] - outside the MCP catalog, so Read is still used).
  3. If STATE.md is missing, print: "No pipeline state. Run {{command_prefix}}brief first." and stop.

This path loads the same context in 5–10 file reads (~100s, ~46.5k tokens - file-reading baseline).

Step 2 - Default output

━━━ Pipeline state ━━━
Stage: <stage>   Cycle: <cycle or "default">   Wave: <wave>
Last checkpoint: <timestamp>
Decisions: <N>   Open todos: <N>
Next: {{command_prefix}}<next-stage>
━━━━━━━━━━━━━━━━━━━━━━

Recommend next stage via the same logic as {{command_prefix}}next (route by which artifacts exist).

First-run connection nudge

After the pipeline state block, if every <connections> entry from the snapshot is not_configured AND .design/config.json > connections_onboarding is absent, append once per session (transient marker .design/.connections-nudge-shown): Tip: run {{command_prefix}}connections to see what integrations can plug in (Figma, Storybook, Chromatic, etc.).

Step 3 - Forensic audit (only if --forensic)

Run these six checks and print PASS/WARN/FAIL per check:

  1. Stale artifacts - compare mtime of .design/DESIGN.md against most recent file under src/ via ls -lt. WARN if DESIGN.md is older by >7 days.
  2. Missing transitions - stage from the mcp__gdd_state__get snapshot vs artifacts present. e.g. stage=plan requires DESIGN-CONTEXT.md. FAIL if expected artifact missing.
  3. Token drift - wc -c .design/DESIGN.md .design/DESIGN-CONTEXT.md; tokens ≈ bytes/4. WARN if combined >50000 tokens.
  4. Aged DESIGN-DEBT - read .design/DESIGN-DEBT.md; any item whose line predates HEAD by >14 days (check git blame or file mtime fallback) → WARN.
  5. Cycle alignment - if cycle from the snapshot is set but .design/CYCLES.md has no matching heading → FAIL.
  6. Connection status - re-probe figma/refero via ToolSearch; compare to the <connections> field in the snapshot. WARN on mismatch.

Also scan .design/SEEDS.md (if present) for seeds whose trigger keywords match the snapshot or CYCLES.md content; list them as "Seed ready to germinate: ".

Print:

━━━ Forensic audit ━━━
[PASS] Stale artifacts
[WARN] Token drift — 53,400 tokens combined
[PASS] Missing transitions
[PASS] Aged DESIGN-DEBT
[PASS] Cycle alignment
[WARN] Connection status — figma now unavailable
Seeds ready: 0
━━━━━━━━━━━━━━━━━━━━━━

Step 3.5 - Composition-graph readiness

After the pipeline state (and forensic audit if run), surface a one-line composition-graph hint from scripts/lib/manifest/skills.json. Count skill records declaring composes_with or next_skills, then probe for structural problems (cycles in the directed graph, or edges pointing at a skill name that has no record). Print one line:

  • Composition graph: <edges> edges, <skills-with-edges> skills wired | cycles: <n> | dangling: <n>

Run scripts/validate-composition-graph.cjs for the authoritative cycle and dangling-edge counts when that validator is present; until then report 0 and note the graph is not yet wired. This is a readiness hint, not a gate.

Step 3.6 - DesignContext graph coverage

When .design/context-graph.json exists, surface one line for the typed DesignContext graph using the coverage helper in scripts/lib/design-context-query.cjs (node scripts/lib/design-context-query.cjs coverage), then point at the Atomic-Design map: DesignContext graph: <pct>% node-type coverage | map: .design/INTEGRATION-MAP.md. Skip this line entirely when the graph is absent (a pre-Phase-52 project); offer {{command_prefix}}migrate-context as the next step instead. Readiness hint, not a gate.

Step 4 - Update notice (safe-window surface)

After printing the pipeline state, emit the plugin-update banner if one is present. This file is written by hooks/update-check.sh subject to the state-machine guard (mid-pipeline stages suppress it) and per-version dismissal.

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

No-op when: no new release exists, state-machine guard is active (stage in plan|design|verify), or the latest tag has been dismissed via {{command_prefix}}check-update --dismiss.

Do Not

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

PROGRESS COMPLETE

Frequently asked questions

What does the Gdd Progress AI skill do?

Shows current pipeline position and routes to next action. --forensic runs 6-check integrity audit. Activates for requests involving showing current project state, routing to the next action, or a status check.

Why use Gdd Progress on TypingMind?

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

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

Which AI models can use Gdd Progress?

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 Progress?

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

Is the Gdd Progress 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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