Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/rafaelgorski/problem-based-srs/copilot-instructionsgit clone --depth 1 https://github.com/RafaelGorski/Problem-Based-SRSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/rafaelgorski/problem-based-srs/copilot-instructions)<a href="https://agentmods.dev/instructions/rafaelgorski/problem-based-srs/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/rafaelgorski/problem-based-srs/copilot-instructions.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.10911 | $0.10911 |
| Opus 5 | $0.05456 | $0.05456 |
| Sonnet 5 | $0.02182 | $0.02182 |
| Haiku 4.5 | $0.01091 | $0.01091 |
Grade A, and why
Problem-Based-SRS copilot-instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 719 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Copilot Instructions for Problem-Based SRS
Project Overview
This repository provides AgentSkills for following a Problem-Based Software Requirements Specification (SRS) methodology. The focus is on enabling AI-assisted requirements engineering through structured, problem-first approaches.
The repository follows the AgentSkills standard and the Claude Code Plugins layout. Compatibility priority is GitHub Copilot first, then Claude Code/Claude.ai.
Plugin Standards
This repository follows the Claude Code plugins specification:
- Plugins Guide: https://code.claude.com/docs/en/plugins.md
- Plugins Reference: https://code.claude.com/docs/en/plugins-reference.md
When modifying this repository structure, ensure compliance with these plugin standards.
Compatibility Priority (GHCP → Claude)
- GitHub Copilot first: Keep skills and instructions directly usable in Copilot workflows.
- Claude second: Keep
.claude-plugin/plugin.json,.claude-plugin/marketplace.json,skills/,agents/,hooks/, andsettings.jsonaligned with Claude plugin docs. - Consistency over time: Keep compatibility guidance consistent when it changes.
Core Principles
- Problem-First Thinking: Always identify the problem before proposing solutions
- Lightweight Methodology: Favor simplicity over complex frameworks
- AI-Native Design: Content designed for consumption by AI agents (following AgentSkills standard)
- Practical Guidance: Focus on actionable skills and templates
🔄 Problem-Based SRS Iteration Guidelines
Using the Methodology
When analyzing or working with this repository, use the Problem-Based SRS methodology to iterate on problems, needs, and requirements (both functional and non-functional).
Load and follow the methodology from these skills:
| Step | Action file |
|---|---|
| 0. Business Context (CONTEXT) | skills/problem-based-srs/reference/business-context.md |
| 1. Customer Problems (WHY) | skills/problem-based-srs/reference/problems.md |
| 2. Software Glance | skills/problem-based-srs/reference/software-glance.md |
| 3. Customer Needs (WHAT) | skills/problem-based-srs/reference/needs.md |
| 4. Software Vision | skills/problem-based-srs/reference/software-vision.md |
| 5. Functional Requirements (HOW) | skills/problem-based-srs/reference/functional-requirements.md |
| Validation | skills/problem-based-srs/reference/validate.md |
| Complexity (Optional) | skills/problem-based-srs/reference/complexity.md |
| Live canvas | skills/problem-based-srs/reference/live.md |
| Orchestrator | skills/problem-based-srs/SKILL.md |
| Agent | agents/problem-based-srs/AGENT.md |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 719 lines · 10,911 tokens per session scan A 42904db9050f
Problem-Based-SRS copilot-instructions.md is an instructions file published in the GitHub repository RafaelGorski/Problem-Based-SRS (32 stars, last pushed 15d ago), licensed MIT. It adds 10,911 tokens to every session, about $0.0546 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
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digital-innovation-agents GEMINI.md
Gemini CLI instructions for pssah4/digital-innovation-agents, a project described as: Digital Innovation Agents is an AI-augmented workflow that connects business analysis and software engineering in one consistent skill set.
rdq-skill-chatgpt-app AGENTS.md
Instructions for mathruffian-dot/rdq-skill-chatgpt-app, covering rdq method — chatgpt app 版（專案藍圖）, 專案簡介, 關鍵時程, 目標與路線圖 and 專案入口.
ae-framework AGENTS.md
Instructions for itdojp/ae-framework, covering agents — ae-framework router, decision table, invariants（不変条件）, progressive disclosure（参照順） and scope.
ae-framework CLAUDE.md
Instructions for itdojp/ae-framework, covering claude — ae-framework router, read order, role boundary, invariants and scope.
digital-innovation-agents AGENTS.md
AGENTS.md instructions for pssah4/digital-innovation-agents, a project described as: Digital Innovation Agents is an AI-augmented workflow that connects business analysis and software engineering in one consistent skill set.