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Derive Nfr From Driver

Community
andreaswasita
derive-nfr-from-driver

Derives measurable NFRs from a parent business driver.

Overview

Publisherandreaswasita
Repositorycopilot-agents-dojo
Skill namederive-nfr-from-driver
Stars
54
Forks
15
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 andreaswasita on GitHub. Read the source before you install it.

Installation

Install the Derive Nfr From Driver 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/andreaswasita/copilot-agents-dojo/tree/main/skills/derive-nfr-from-driver
  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/andreaswasita/copilot-agents-dojo.git /tmp/copilot-agents-dojo
mkdir -p .claude/skills
cp -r /tmp/copilot-agents-dojo/skills/derive-nfr-from-driver .claude/skills/derive-nfr-from-driver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Derive Nfr From Driver 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 Derive Nfr From Driver 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 Derive Nfr From Driver 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.

Derive NFR From Driver

Turns a ratified business driver (BR) — and the functional requirements (FRs) derived from it — into measurable Non-Functional Requirements with verified parentage. This skill does NOT elicit drivers and does NOT invent FRs; it derives NFRs against parents that already exist on disk.

When to Use

  • A BR-* and at least one FR-* exist for the engagement and need performance, availability, scalability, or accessibility targets before Phase D begins.
  • The architect is about to design components and needs SLO-shaped inputs, not adjectives.
  • A reviewer asks "what does fast mean?" — derive an NFR with a numeric target instead of arguing prose.
  • NOT when the driver itself is unratified — go to requirements-elicitation first.

Prerequisites

  • requirements/<engagement>/BR/ contains at least one ratified BR-* artifact (ratified_by is non-empty).
  • requirements/<engagement>/FR/ contains the FR(s) this NFR will constrain.
  • spec/artifact-schema.md is the contract for frontmatter fields.

If a prerequisite is missing the skill must stop and report — never emit an NFR pointing at a non-existent or unratified parent.

How to Run

bash
# 1. Confirm the parent exists and is ratified
bash scripts/verify-traceability.sh requirements/<engagement>

# 2. Create the NFR file (filename MUST equal {id}.md)
#    Then run the gate again to prove the new link resolves
bash scripts/verify-traceability.sh requirements/<engagement>

Quick Reference

ActionToolNotes
Read parent BR/FRviewGet the exact id values.
List existing NFRsglob requirements/<engagement>/NFR/*.mdPick the next free NFR-NNN.
Write NFRcreateFrontmatter per spec/artifact-schema.md.
Verify lineagebash scripts/verify-traceability.shMust pass before commit.

The frontmatter the agent emits:

yaml
---
id: NFR-007
layer: NFR
title: Classification latency under load
parent_ids: [BR-001, FR-002]
owner: jane.doe@example.com
measurable: true
ratified_by: ""
target: "p95 < 2s on 3G, p99 < 4s"
derivation_skill: derive-nfr-from-driver
tags: [latency, classification]
---

Procedure

Step 1: Assess

  • Read the parent BR-* and any candidate FR-* with view.
  • Identify the quality attribute the driver implies: latency, throughput, availability, durability, accessibility, recoverability, observability, portability.
  • Reject the derivation outright if the driver is unratified (ratified_by empty). Send back to requirements-elicitation.

Step 2: Derive

For each quality attribute, write one NFR with:

  • A numeric target in the target field (percentile + threshold + operating envelope — never just "fast"). If you cannot write a number, you have not derived an NFR yet.
  • A measurement method stated in the body (synthetic probe, RUM, load test fixture). Without it the NFR is unfalsifiable.
  • An operating envelope: load profile, network condition, payload size. NFRs without envelopes drift on contact with production.

Step 3: Link

  • parent_ids MUST include the driver's BR-* and SHOULD include the most specific FR-* the NFR constrains.
  • derivation_skill: derive-nfr-from-driver is mandatory — the curator uses this to attribute learned patterns.

Step 4: Confirm

bash
bash scripts/verify-traceability.sh requirements/<engagement>

The gate must pass with zero failures. A warning on ratified_by is expected until a human ratifies the NFR at the architecture review board.

Pitfalls

  • DO NOT invent a parent BR to justify an NFR you already wanted — the skill exists specifically to prevent this. If the parent does not exist, stop.
  • DO NOT write measurable: false to bypass the gate; the gate fails on it for NFRs by design (see spec/artifact-schema.md §2.3).
  • DO NOT parent an NFR to another NFR — the schema disallows it. NFRs are leaves of the requirement layer, not branches.
  • DO NOT set ratified_by from inside the agent. Ratification is the human's signal that the derivation is accepted. The agent leaves it blank.
  • DO NOT copy the driver's prose into the target field. The target is a number with units; the prose belongs in the body.

Verification

  • New NFR-*.md filename equals its id.
  • parent_ids lists at least one BR-* that exists on disk.
  • target contains a numeric threshold and an operating envelope.
  • bash scripts/verify-traceability.sh requirements/<engagement> exits 0.
  • bash scripts/gen-rtm.sh requirements/<engagement> shows the new NFR linked under its driver in docs/rtm.md.
  • bash scripts/verify.sh passes the full gate.

Frequently asked questions

What does the Derive Nfr From Driver AI skill do?

Derives measurable NFRs from a parent business driver.

Why use Derive Nfr From Driver on TypingMind?

Because you install it once and use it with any model. Derive Nfr From Driver 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 Derive Nfr From Driver in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/andreaswasita/copilot-agents-dojo/tree/main/skills/derive-nfr-from-driver. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Derive Nfr From Driver?

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 Derive Nfr From Driver?

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

Is the Derive Nfr From Driver AI skill free?

Yes. It is published on GitHub by andreaswasita 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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