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Product Capability

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affaan-m
product-capability

Translate PRD intent, roadmap asks, or product discussions into an implementation-ready capability plan that exposes constraints, invariants, interfaces, and unresolved decisions before multi-service work starts. Use when the user needs an ECC-native PRD-to-SRS lane instead of vague planning prose.

Overview

Publisheraffaan-m
RepositoryECC
Skill nameproduct-capability
Stars
270.2K
Forks
40.4K
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 affaan-m on GitHub. Read the source before you install it.

Installation

Install the Product Capability 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/affaan-m/ECC/tree/main/.agents/skills/product-capability
  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/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/product-capability .claude/skills/product-capability
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Capability 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 Product Capability 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 Product Capability 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.

Product Capability

This skill turns product intent into explicit engineering constraints.

Use it when the gap is not "what should we build?" but "what exactly must be true before implementation starts?"

When to Use

  • A PRD, roadmap item, discussion, or founder note exists, but the implementation constraints are still implicit
  • A feature crosses multiple services, repos, or teams and needs a capability contract before coding
  • Product intent is clear, but architecture, data, lifecycle, or policy implications are still fuzzy
  • Senior engineers keep restating the same hidden assumptions during review
  • You need a reusable artifact that can survive across harnesses and sessions

Canonical Artifact

If the repo has a durable product-context file such as PRODUCT.md, docs/product/, or a program-spec directory, update it there.

If no capability manifest exists yet, create one using the template at:

  • docs/examples/product-capability-template.md

The goal is not to create another planning stack. The goal is to make hidden capability constraints durable and reusable.

Non-Negotiable Rules

  • Do not invent product truth. Mark unresolved questions explicitly.
  • Separate user-visible promises from implementation details.
  • Call out what is fixed policy, what is architecture preference, and what is still open.
  • If the request conflicts with existing repo constraints, say so clearly instead of smoothing it over.
  • Prefer one reusable capability artifact over scattered ad hoc notes.

Inputs

Read only what is needed:

  1. Product intent
    • issue, discussion, PRD, roadmap note, founder message
  2. Current architecture
    • relevant repo docs, contracts, schemas, routes, existing workflows
  3. Existing capability context
    • PRODUCT.md, design docs, RFCs, migration notes, operating-model docs
  4. Delivery constraints
    • auth, billing, compliance, rollout, backwards compatibility, performance, review policy

Core Workflow

1. Restate the capability

Compress the ask into one precise statement:

  • who the user or operator is
  • what new capability exists after this ships
  • what outcome changes because of it

If this statement is weak, the implementation will drift.

2. Resolve capability constraints

Extract the constraints that must hold before implementation:

  • business rules
  • scope boundaries
  • invariants
  • trust boundaries
  • data ownership
  • lifecycle transitions
  • rollout / migration requirements
  • failure and recovery expectations

These are the things that often live only in senior-engineer memory.

3. Define the implementation-facing contract

Produce an SRS-style capability plan with:

  • capability summary
  • explicit non-goals
  • actors and surfaces
  • required states and transitions
  • interfaces / inputs / outputs
  • data model implications
  • security / billing / policy constraints
  • observability and operator requirements
  • open questions blocking implementation

4. Translate into execution

End with the exact handoff:

  • ready for direct implementation
  • needs architecture review first
  • needs product clarification first

If useful, point to the next ECC-native lane:

  • project-flow-ops
  • workspace-surface-audit
  • api-connector-builder
  • dashboard-builder
  • tdd-workflow
  • verification-loop

Output Format

Return the result in this order:

text
CAPABILITY
- one-paragraph restatement

CONSTRAINTS
- fixed rules, invariants, and boundaries

IMPLEMENTATION CONTRACT
- actors
- surfaces
- states and transitions
- interface/data implications

NON-GOALS
- what this lane explicitly does not own

OPEN QUESTIONS
- blockers or product decisions still required

HANDOFF
- what should happen next and which ECC lane should take it

Good Outcomes

  • Product intent is now concrete enough to implement without rediscovering hidden constraints mid-PR.
  • Engineering review has a durable artifact instead of relying on memory or Slack context.
  • The resulting plan is reusable across Claude Code, Codex, Cursor, OpenCode, and ECC 2.0 planning surfaces.

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 Product Capability AI skill do?

Translate PRD intent, roadmap asks, or product discussions into an implementation-ready capability plan that exposes constraints, invariants, interfaces, and unresolved decisions before multi-service work starts. Use when the user needs an ECC-native PRD-to-SRS lane instead of vague planning prose.

Why use Product Capability on TypingMind?

Because you install it once and use it with any model. Product Capability 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 Product Capability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/product-capability. 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 Product Capability?

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 Product Capability?

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

Is the Product Capability AI skill free?

Yes. It is published on GitHub by affaan-m 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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