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Requirement Convergence

Community
shinpr
requirement-convergence

Separates the outcome a change must produce from the requirements proposed to reach it, records what the user excluded, and bands cost from structure. Use when a requirement enters a workflow, before design begins.

Overview

Publishershinpr
Repositoryclaude-code-workflows
Skill namerequirement-convergence
Stars
682
Forks
103
Bundled files
1
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.

  • 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 shinpr on GitHub. Read the source before you install it.

Installation

Install the Requirement Convergence 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.
  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/shinpr/claude-code-workflows.git /tmp/claude-code-workflows
mkdir -p .claude/skills
cp -r /tmp/claude-code-workflows/dev-skills/skills/requirement-convergence .claude/skills/requirement-convergence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requirement Convergence 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 Requirement Convergence 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 Requirement Convergence 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.

Requirement Convergence

Purpose

Requirements arrive bloated, ambiguous, or aimed at the wrong outcome. A capable model reconciles all three into a coherent plan and builds it faithfully — delivering exactly what was asked for when what was asked for was wrong.

This skill converges what to build. How to build it, and which documents the change requires, are settled after the what is.

Convergence Fields

FieldPass condition
outcomeOne observable result. A requirement that does not serve it is excess.
requirements[]Every build-relevant item labeled current-state or desired-future.
nonGoals[]Authored by the user, or the user stated there are none.
costA band with the structural evidence that places it, plus the unknowns that remain.

cost is a rough band, not the effort estimate a work plan schedules against; requirements cannot support person-days. Its unknowns carry more decision weight than its size.

Classify from the user's own retained wording, not from an analyzer's restatement of it: wording that asks for an evaluation, describes a speculative idea, or suggests a mechanism stays in active convergence context as a judgment-only candidate. requirements[] and durable documents receive a candidate only after explicit user confirmation.

Each field carries a readiness label: ready, weak, or weak-but-explicit (weak, and the user agreed to leave it unresolved). Only the user sets weak-but-explicit. Requirements are converged when every applicable field is ready or weak-but-explicit.

Judgment rules per field: references/criteria.md.

Hearing Protocol

Run the hearing after scope analysis has produced the facts needed to judge the convergence fields. The workflow using this skill owns the interaction method and routing; this skill defines the hearing content and pass conditions.

Register these steps before starting and record each step's evidence as it completes:

StepActionCompletion evidence
1Render the Scope Confirmation below, asking only about the fields below readyEach fact cites the analysis output it came from, and User decisions holds one question per field below ready
2Record each answer as that field's valueThe value uses wording the user supplied, not wording the hearing offered
3Re-ask once when a recorded value still fails its pass condition, then mark the field weak-but-explicit when the user agrees to leave the second answer as it standsTwo recorded answers, or the user's agreement to stop
4Hand the record to the step that judges the fieldsAn updated record returned from that step

Step 2's evidence is what keeps the hearing reviewable: a value restating the hearing's own candidates fails it, so the user's judgment survives however the question was put.

Scope Confirmation

Render this shape at every requirements confirmation stop, whether or not the hearing ran, using only what can change the user's requirement decision or the workflow route.

SectionContents
Confirmed scopeThe requirements and exclusions the user has already selected, in the user's wording
Decision evidenceEach material observed fact with its source, followed by what that fact can change about scope, outcome, or cost
User decisionsEach unresolved product, UX, or operational question, followed by the scope, outcome, or cost effects of its materially different answers
WorkflowRough cost band with the unknowns that remain, Structural Scale, and the selected document and workflow route

Keeping the first three sections separate is what makes the decision informable: merged, the reader cannot tell which line is the user's own settled boundary, which is a repository observation, and which is still open.

Only an explicit user answer moves an item from User decisions into Confirmed scope. Because Step 2 records the user's own wording, pose each open decision as a question and let the user supply its answer. The orchestrator owns Workflow and presents it as a selected route.

Storage Protocol

CarrierHoldsWritten by
The convergence record in the judging step's outputEvery field with its readiness labelThe judging step
PRD Success Criteria and Future / Out of Scopeoutcome; user-authored nonGoalsThe PRD production step
Design Doc Requirement ConvergenceThe same when no PRD exists, and the fields left weak-but-explicit in every caseThe Design Doc production step

A flow that produces neither document carries the record in its own context to the next step.

Reference Protocol (For Downstream Consumers)

  1. Read the convergence record from the prompt.
  2. Treat nonGoals as excluded from the current change and desired-future requirements as buildable scope. Evaluation requests, speculative ideas, prescribed mechanisms, and agent-proposed capabilities that were not promoted create no downstream obligation; an accepted ADR may retain evaluated options as decision history.
  3. Treat a weak-but-explicit field as a recorded open question rather than a settled decision. When work depends on it, return the missing decision and its effect to the owning workflow.

Quality Checklist

  • Scope facts were presented before questions were asked
  • nonGoals came from the user, or the user stated there are none
  • Every applicable field is ready, or weak-but-explicit by the user's agreement

References

  • references/criteria.md — judgment rules per field, cost inputs, challenge intensity, solution-in-disguise test

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 Requirement Convergence AI skill do?

Separates the outcome a change must produce from the requirements proposed to reach it, records what the user excluded, and bands cost from structure. Use when a requirement enters a workflow, before design begins.

Why use Requirement Convergence on TypingMind?

Because you install it once and use it with any model. Requirement Convergence 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 Requirement Convergence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/shinpr/claude-code-workflows/tree/main/dev-skills/skills/requirement-convergence. 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 Requirement Convergence?

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 Requirement Convergence?

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

Is the Requirement Convergence AI skill free?

Yes. It is published on GitHub by shinpr 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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