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 skills add vignesh2027/AI-AGENT-SKILLS --skill requirements-analysisgit clone --depth 1 https://github.com/vignesh2027/AI-AGENT-SKILLSWrote 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/skills/vignesh2027/ai-agent-skills/requirements-analysis)<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/requirements-analysis"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/requirements-analysis/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vignesh2027/ai-agent-skills/requirements-analysis"><img src="https://agentmods.dev/badge/skills/vignesh2027/ai-agent-skills/requirements-analysis.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00013 | $0.00603 |
| Opus 5 | $0.00006 | $0.00302 |
| Sonnet 5 | $0.00003 | $0.00121 |
| Haiku 4.5 | $0.00001 | $0.00060 |
Grade A, and why
requirements-analysis 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 10d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Requirements analysis converts stakeholder requests into verified, unambiguous, testable requirements. It identifies conflicts, gaps, and hidden constraints before they become expensive bugs.
When to Use
- After gathering stakeholder input but before writing specs
- When requirements come from multiple sources that may conflict
- Before estimating effort on a project
- When the system must integrate with external systems or regulations
Process
Step 1: Collect all sources
Gather: tickets, meeting notes, existing docs, similar systems, regulatory requirements, user research. Document each source.
Step 2: Extract and categorize requirements
Sort into:
- Functional — What the system does
- Non-functional — How well it does it (performance, security, availability)
- Constraints — What the system cannot do or cannot use
- Assumptions — Things believed true but not verified
Step 3: Check for ambiguity
Flag any requirement that uses: "fast," "easy," "simple," "scalable," "secure," or any other unmeasured adjective. Replace with measurable criteria.
Step 4: Check for conflicts
List pairs of requirements that could contradict each other. Example: "must respond in under 100ms" vs "must encrypt all data at rest and in transit." Resolve or prioritize explicitly.
Step 5: Check for completeness
Ask: What happens when X fails? What are the edge cases for Y? What permissions are required? What should happen with invalid input?
Step 6: Validate with stakeholders
Walk through the requirements list with at least one stakeholder. Every ambiguity you resolve costs nothing here; every ambiguity you miss costs exponentially more later.
Step 7: Prioritize
Label each requirement: Must Have / Should Have / Nice to Have (MoSCoW). Scope the first version to Must Haves only.
Step 8: Create a traceability matrix
Link each requirement to its source. This allows you to answer "why does this requirement exist?" at any point.
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.
- 10d ago First seen · 65 lines · 13 tokens per session scan A d1b6cb0a8779
requirements-analysis is a skill published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 12d ago), licensed MIT. It adds 13 tokens to every session and 603 once invoked, about $0.0001 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 2026-08-31.
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