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Common Software Requirements

Community
HoangNguyen0403
common-software-requirements

Standardize SRS and FRS specifications for technical behavior, interfaces, data contracts, quality constraints, and verification mapping. Use when writing SRS, functional specification, system behavior requirements, API/data contracts, or non-functional thresholds.

Overview

PublisherHoangNguyen0403
Repositoryagent-skills-standard
Skill namecommon-software-requirements
Stars
573
Forks
166
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by HoangNguyen0403 on GitHub. Read the source before you install it.

Installation

Install the Common Software Requirements 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/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/common/common-software-requirements
  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/HoangNguyen0403/agent-skills-standard.git /tmp/agent-skills-standard
mkdir -p .claude/skills
cp -r /tmp/agent-skills-standard/.agents/skills/common/common-software-requirements .claude/skills/common-software-requirements
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Common Software Requirements 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 Common Software Requirements 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 Common Software Requirements 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.

Software Requirements Expert

Priority: P0 (CRITICAL)

Define the technical "How" with verifiable requirements.

1. SRS/FRS Discovery

  • Confirm linked PRD requirements (REQ-*) and AC IDs.
  • Preserve trace: BRD-OBJ-* -> REQ-* -> AC-* -> SRS-* -> test evidence.
  • Block or route back to PRD (plan-feature) when REQ-* or AC-* inputs are missing; do not infer product scope from code.
  • Define functional flows: trigger, inputs, validations, outputs, errors.
  • For complex flows, use one actor, one goal, one session; split normal, alternate, and exception courses.
  • Define interface contracts: API, events, storage, external integrations.
  • Define NFR thresholds: latency, availability, security, scalability.
  • Define constraints: migration, compatibility, compliance, rollout.

2. Drafting Workflow

  • Load references/srs-template.md.
  • Slug Alignment: Use the same [slug] from the source docs/prd/prd-[slug].md to maintain filename-level traceability.
  • Write one requirement card per statement with stable SRS-* IDs.
  • Map each SRS-* to source PRD REQ-* and verification lane.
  • Map every technical behavior to PRD ACs, test lane, and evidence target.
  • Include statement, priority, status, input/output/error behavior, NFR impact, measurement method, and evidence target.
  • Add outcome report: feature_status, requirement trace, completed/missing evidence, decision needed, and recommended next workflow.
  • Write to docs/srs/srs-[slug].md.

3. Verification Mapping

  • Each SRS-* has test evidence plan (unit/integration/E2E/manual).
  • Failure modes and fallback behavior are explicit.
  • Permissions and privacy controls mapped to requirements.
  • Measurement method exists for each NFR.

Anti-Patterns

  • No mixed requirements and implementation tasks in same statement.
  • No NFR claims without numeric threshold.
  • No interface contract without input/output/error schema.
  • No requirement without trace link to source and verification.
  • No implementation handoff without mapped PRD AC IDs and test lanes.
  • No happy-path-only flow for complex user/system interactions.

References

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 Common Software Requirements AI skill do?

Standardize SRS and FRS specifications for technical behavior, interfaces, data contracts, quality constraints, and verification mapping. Use when writing SRS, functional specification, system behavior requirements, API/data contracts, or non-functional thresholds.

Why use Common Software Requirements on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HoangNguyen0403/agent-skills-standard/tree/develop/.agents/skills/common/common-software-requirements. 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 Common Software Requirements?

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 Common Software Requirements?

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

Is the Common Software Requirements AI skill free?

Yes. It is published on GitHub by HoangNguyen0403 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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