Workflow Status

Community

nurettincoban

workflow-status

Report which workflow artifacts exist, which RFCs are implemented and reviewed, what has drifted, and the next step.

Overview

Publisher	nurettincoban
Repository	`ai-prd-workflow`
Skill name	`workflow-status`
Stars	305
Forks	33
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by nurettincoban on GitHub. Read the source before you install it.

Installation

Install the Workflow Status 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/nurettincoban/ai-prd-workflow/tree/main/skills/workflow-status
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/nurettincoban/ai-prd-workflow.git /tmp/ai-prd-workflow
mkdir -p .claude/skills
cp -r /tmp/ai-prd-workflow/skills/workflow-status .claude/skills/workflow-status

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

Use it in TypingMind

Enable Workflow Status 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 Workflow Status 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 Workflow Status 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.

You are guiding a project through an RFC-driven development workflow with these stages:

#	Stage	Artifact	Command / Prompt
0	Document an existing codebase (instead of stages 1, 3 and 4)	PRD.md, FEATURES.md, RULES.md	`/document-existing` (document-existing-prompt.md)
1	Create PRD	PRD.md	`/create-prd` (interactive-prd-creation-prompt.md)
2	Verify PRD	PRD.md (improved) + PRD-REVIEW.md	`/verify-prd` (prd-comprehensive-verification-prompt.md)
3	Extract features	FEATURES.md	`/extract-features` (prd-to-features-prompt.md)
4	Generate rules	RULES.md	`/generate-rules` (prd-to-rules-prompt.md)
5	Generate RFCs	RFCs/ folder + RFCS.md	`/generate-rfcs` (prd-to-rfcs-prompt.md)
6	Testing strategy	TEST-STRATEGY.md	`/test-strategy` (testing-strategy-prompt.md)
7	Implement RFCs (each once its declared predecessors are done)	Code	`/implement-rfc <id>` (implementation-prompt-template.md)
8	Review each implementation	`reviews/REVIEW-RFC-<id>.md`	`/review-rfc <id>` (code-review-prompt.md)
9	Manage changes (whenever requirements move)	`changes/CHANGE-REQUEST-<nnn>.md`	`/manage-changes` (prd-change-management-prompt.md)
10	Status check (anytime)	this report	`/workflow-status` (workflow-status-prompt.md)

Stage 6 comes before stage 7 deliberately: a test plan written after the code is a coverage audit, not a plan.

Inspect the current project to determine workflow progress. If trace-check.py is available -- in a scripts/ folder beside these instructions, or in the project's own scripts/ folder -- run it on the project first (python3 <path>/trace-check.py .): it checks IDs, coverage, RFC dependencies and review records mechanically, and its FAIL lines belong in your Inconsistencies section.

Which artifacts exist: PRD.md, PRD-REVIEW.md, FEATURES.md, RULES.md, RFCS.md, RFCs/ folder, TEST-STRATEGY.md, reviews/, changes/?
Which RFCs are implemented? Start from the Status column in RFCS.md, which /implement-rfc and /review-rfc keep current, then verify it: spot-check the acceptance criteria of every RFC it marks Implemented or Reviewed against the actual code, and report any status the code does not support. Do not assume an RFC is done just because code exists, or because the column says so.
Which RFCs have been reviewed? Check reviews/ for a report per implemented RFC — an implemented RFC with no review is the most common real-world gap, and reviews are exactly what gets skipped under deadline.
Are there open change requests in changes/ whose decisions are still pending?
Any signs of drift: code that contradicts the PRD or RULES.md, features in the codebase with no RFC, an RFC implemented before its declared predecessors, deviations in code that the RFC's Implementation Notes do not record?

If you cannot inspect files directly, ask me to describe or paste the artifacts before reporting.

Then report:

Status table — each workflow stage with its artifact and status (Done / In progress / Missing / Stale)

Per-RFC progress — implementation and review status side by side, one row per RFC:


RFC-001  implemented ✅   reviewed ❌
RFC-002  implemented ✅   reviewed ❌
RFC-003  implemented ✅   reviewed ✅

A single "reviews done" row hides which RFCs were actually reviewed. Report them individually.

Inconsistencies — anything drifted, skipped, or contradictory, with file references
Next step — the single recommended next action, with the exact command or prompt to run

Bundled files

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

scripts/trace-check.py13 KB

Frequently asked questions

What does the Workflow Status AI skill do?

Report which workflow artifacts exist, which RFCs are implemented and reviewed, what has drifted, and the next step.

Why use Workflow Status on TypingMind?

Because you install it once and use it with any model. Workflow Status 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 Workflow Status in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nurettincoban/ai-prd-workflow/tree/main/skills/workflow-status. 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 Workflow Status?

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 Workflow Status?

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

Is the Workflow Status AI skill free?

Yes. It is published on GitHub by nurettincoban 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

Bundled files

Frequently asked questions

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