Requirements Elicitation logo

Requirements Elicitation

Community
andreaswasita
requirements-elicitation

Turns vague intent into testable requirements.

Overview

Publisherandreaswasita
Repositorycopilot-agents-dojo
Skill namerequirements-elicitation
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 Requirements Elicitation 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/requirements-elicitation
  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/requirements-elicitation .claude/skills/requirements-elicitation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Requirements Elicitation 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 Requirements Elicitation 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 Requirements Elicitation 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.

Requirements Elicitation

Turns vague intent into precise, testable, engineer-ready requirements. This is a technique skill — not a role. Both the TPM and Architect invoke it at different stages of the requirements pipeline.

Requirements are delivery contracts, not meeting minutes. Every requirement must be testable, traceable, and unambiguous before it reaches engineering.

When to Use

  • Starting a new feature, epic, or initiative
  • Requirements feel vague, conflicting, or scope-creeping
  • Someone asks "what does done look like?"
  • Stakeholders keep changing their minds mid-sprint
  • Writing PRDs, feature briefs, or story backlogs
  • Assessing system impact of incoming requirements (Architect stage)
  • Translating user stories into system specifications (Architect stage)

Prerequisites

  • The ask_user Copilot tool to gather clarifications without spamming prose.
  • The edit Copilot tool to write the resulting brief / story cards.
  • A target file for the artefacts (typically requirements/<engagement>/stories.md or docs/feature-brief.md).
  • Access to the primary stakeholder — no requirements loop with a phantom user.
  • For the Architect stage: read access to the TPM-produced brief.

How to Run

text
1. DISCOVER   — Ask 3–5 problem-framing questions; never accept solutions-disguised-as-requirements.
2. INTERROGATE — Drive Socratic questions across scope, edges, success, failure, dependencies, constraints.
3. STRUCTURE  — Write user stories + Given/When/Then acceptance criteria + assumptions + out-of-scope.
4. VALIDATE   — Read each AC back in plain language and capture written sign-off.
5. GATE       — Walk the Definition of Ready checklist. If anything fails, return to step 2.
6. HAND OFF   — TPM passes to Architect; Architect may return stories that are not technically achievable.

Quick Reference

ArtefactMandatory format
User storyAs a [specific role], I want to [action], so that [measurable outcome].
Acceptance criterionGiven [precondition], When [action], Then [expected result]. (≥ 3 per story)
Non-functional requirementNumeric threshold + measurement method (no adjectives)
AssumptionStatement + owner + expiry date
Out of scopeExplicit boundary list (never implied)
Pipeline stageOwnerOutputs
Business elicitationTPMFeature brief + story cards + sign-off log
System specificationArchitectImpact assessment + feasibility note + revised ACs

Procedure

Step 1: DISCOVER — Understand the problem, not the solution

  • Ask: What problem are we solving? Who has it? What happens if we don't solve it?
  • Identify the primary stakeholder, affected users, and business driver.
  • Never accept "we need a button that does X" — trace back to the why.

Step 2: INTERROGATE — Socratic elicitation

Use structured questions to surface hidden requirements:

  • Scope: What is explicitly NOT included?
  • Edge cases: What happens when [exception scenario]?
  • Success: How do we measure that this worked?
  • Failure: What does failure look like? Who notices?
  • Dependencies: What must exist before this can work?
  • Constraints: Time, budget, compliance, technical?

Step 3: STRUCTURE — Write requirements in standard form

User Story format (mandatory):

As a [specific user role],
I want to [action],
So that [measurable outcome].

Acceptance Criteria format (mandatory, minimum 3 per story):

Given [precondition],
When [action],
Then [expected result].

Additional artefacts:

  • Functional Requirements — numbered, atomic, testable
  • Non-Functional Requirements — performance, security, accessibility thresholds (numeric, not adjectives)
  • Assumptions — explicit, dated, owner-assigned
  • Out of Scope — written as a boundary list, not implied

Step 4: VALIDATE — Confirm before proceeding

  • Read back each acceptance criterion in plain language.
  • Get explicit sign-off (written, not verbal).
  • Log open questions with owners and due dates.

Step 5: GATE — Definition of Ready checklist

See Verification below. If any gate fails → return to INTERROGATE. Do not proceed.

Output Templates

Feature Brief (one-pager):

markdown
## Feature: [Name]
**Business Objective:** [Measurable outcome]
**Primary User:** [Specific role]
**Problem Statement:** [What breaks without this?]
**In Scope:** [Explicit list]
**Out of Scope:** [Explicit list]
**Success Metric:** [Threshold + measurement method]
**Target Sprint / Date:** [Committed delivery window]

Story Card:

markdown
## Story: [ID] [Title]
**As a** [role], **I want to** [action], **so that** [outcome].

**Acceptance Criteria:**
- Given [X], when [Y], then [Z].
- Given [X], when [Y], then [Z].
- Given [X], when [Y], then [Z].

**Edge Cases:** [List]
**Out of Scope:** [List]
**Dependencies:** [List]
**Definition of Done:** [List]

Two-Agent Contract

This skill is invoked at two stages of the requirements pipeline:

StageAgentPurpose
Business elicitationTPMDISCOVER → INTERROGATE → STRUCTURE → VALIDATE → GATE. Produces the handoff package.
System specificationArchitectReceives the handoff, runs impact assessment, feasibility check, and specification. Can return stories to TPM if ACs aren't technically achievable.

The bidirectional gate between TPM and Architect is what prevents bad requirements from silently becoming bad code.

Worked Examples

Bad story (fails gate):

As a user, I want the page to load fast so it's a good experience.

Problems: "user" is unspecific, "fast" is unmeasurable, "good experience" is subjective.

Good story (passes gate):

As a retail customer on mobile, I want the product listing page to render within 2 seconds on 3G, so that I can browse without abandoning the session.

  • Given I am on a 3G connection, when I open the product listing, then the first meaningful paint occurs within 2 seconds.
  • Given the listing has 500+ products, when I scroll past the first page, then the next batch loads within 1 second via infinite scroll.
  • Given the API returns an error, when I open the product listing, then I see a retry prompt within 500ms — not a blank page.

Pitfalls

  • Transcribing stakeholder monologues and calling it requirements.
  • Writing "the system shall…" without specifying who, what, and when.
  • Accepting "we'll figure out the edge cases in dev."
  • Letting "fast" or "user-friendly" survive into engineering without a threshold.
  • Skipping sign-off because "everyone was in the room."
  • Writing 40 stories before validating the first 5.
  • Treating scope as flexible once sprint planning begins.
  • Letting the Architect silently absorb feasibility risk instead of returning stories to the TPM.

Verification

Requirements MUST pass all gates before handoff:

  • Business objective is stated and measurable.
  • User role is specific (not "the user" or "admin").
  • Minimum 3 Given/When/Then acceptance criteria, each independently testable.
  • Edge cases documented (minimum 2 per story).
  • Out-of-scope explicitly listed — not implied.
  • All ambiguous language eliminated ("fast" → threshold, "easy" → step count, "intuitive" → measurable heuristic).
  • Non-functional requirements have numeric thresholds.
  • Assumptions recorded with owners and expiry dates.
  • Dependencies identified.
  • Written stakeholder sign-off obtained.
  • Architect impact assessment captured (or stories returned to TPM with reason).

Frequently asked questions

What does the Requirements Elicitation AI skill do?

Turns vague intent into testable requirements.

Why use Requirements Elicitation on TypingMind?

Because you install it once and use it with any model. Requirements Elicitation 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 Requirements Elicitation in TypingMind?

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

Which AI models can use Requirements Elicitation?

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 Requirements Elicitation?

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

Is the Requirements Elicitation 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇