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 huuanh20/awesome-ai-agent-skills --skill srs-generatorgit clone --depth 1 https://github.com/huuanh20/awesome-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/huuanh20/awesome-ai-agent-skills/srs-generator)<a href="https://agentmods.dev/skills/huuanh20/awesome-ai-agent-skills/srs-generator"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/srs-generator/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/huuanh20/awesome-ai-agent-skills/srs-generator"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/srs-generator.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 | $0.00054 | $0.01670 |
| Opus 5.5 | $0.00022 | $0.00668 |
| Sonnet 5.5 | $0.00011 | $0.00334 |
| Haiku 4.5 | $0.00005 | $0.00167 |
Grade A, and why
cl:srs 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 1mo 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.
This is a copy
100% identical to cl:srs — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
cl:srs — IEEE 830 SRS from Raw Requirements
Pipeline: Brainstorm → Receive → Extract → Gap Scan → Clarify (P1 → P2 → P3) → Generate → Review Gate → Save
Reference files (load before starting):
.claude/skills/srs-generator/references/srs-template.md.claude/skills/srs-generator/references/gap-detection-guide.md
Context files (load if present — created by .claude/scripts/init_project.py):
projects/{name}/_context/vision.md→ pre-fills §1.2 Scope and §2.1projects/{name}/_context/features.md→ pre-fills §2.2 and IN/OUT tableprojects/{name}/_context/tech_stack.md→ pre-fills §3.5 Design Constraintsprojects/{name}/_context/glossary.md→ pre-fills Appendix Aprojects/{name}/_context/quality_standards.md→ pre-fills §3.3–§3.6 NFR
If context files exist: skip Brainstorm Gate questions already answered there.
Brainstorm Gate — Understand Context First
Do NOT ask for raw requirements yet. First understand project context.
Ask these 3 questions in one AskUserQuestion batch:
- System type: Web app / Mobile app / API / Internal tool / SaaS / Desktop / Other?
- Primary users: Who will use this system? (end customers, internal staff, admins, B2B clients…)
- Core problem: What problem does this system solve? (1–2 sentences)
Wait for answers. Use responses to seed §2.1 Product Perspective, §2.3 User Characteristics, and §1.2 Scope.
After receiving answers, prompt:
Context noted. Now paste your raw requirements — any format works:
client email, bullet list, chat transcript, PRD draft.
Wait for raw input, then proceed to Step 0.
Step 0 — Receive Input
Read full input silently. Emit:
Input received: ~{N} words | type: [email prose | bullet list | partial PRD | mixed]
Step 1 — Extract & Classify
Output structured block:
- Actors — named and implied stakeholders. Tag undefined:
[GLOSSARY-GAP: {actor}] - Features — FR-01, FR-02… in "Subject can do X" form. Note strategy: prose / bullets / PRD
- Constraints — verbatim fragments only (tech stack, deadline, compliance, budget)
- Out-of-Scope signals — explicit exclusions. If absent:
[CONTEXT-GAP: no out-of-scope boundary stated]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 179 lines · 54 tokens per session scan A 5d455927ae22
cl:srs is a skill published in the GitHub repository huuanh20/awesome-ai-agent-skills (1 stars, last pushed 3mo ago), licensed MIT. It adds 54 tokens to every session and 1,670 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to cl:srs, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
learn
Record a correction, decision, finding or failed attempt in the wiki. Use when the user corrects you, when a decision is made with a reason, when an experiment returns a verdict, or when you learn something the next session would otherwise re-derive.
loop
Work through the taskboard unattended for hours, pacing against the usage limits and filling the gap before a reset with CPU-only jobs. Use when asked to run all day, work unattended, or loop over tickets.
wrap-up
Close out a task by folding what it taught into the wiki, updating the taskboard, and linting. Use at the end of a task, before clearing a session, or when asked to wrap up.
handoff
Write one self-contained goal that a fresh session can continue from. Use when context is running out, before clearing a session, when usage passes 95% on a long run, or when asked for a handoff.
init
Set up Dory in this repo by exploring it, creating wiki/ from the template, and connecting the usage statusline. Use when a project has no wiki/ yet, or when asked to set up Dory.
check-mcp-json
Safely review, triage, repair, and merge ToolSDK MCP Registry package JSON pull requests. Use when an agent needs to validate files under packages/, detect duplicate registry keys, classify community PRs, make authorized fixes on contributor branches, close invalid or duplicate PRs, or squash-merge approved PRs.