Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/huuanh20/awesome-ai-agent-skillsnpx agentmods add skills/huuanh20/awesome-ai-agent-skills/sr-validateWrote 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/sr-validate)<a href="https://agentmods.dev/skills/huuanh20/awesome-ai-agent-skills/sr-validate"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/sr-validate/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/sr-validate"><img src="https://agentmods.dev/badge/skills/huuanh20/awesome-ai-agent-skills/sr-validate.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 | $0.00059 | $0.00583 |
| Opus 5.5 | $0.00024 | $0.00233 |
| Sonnet 5.5 | $0.00012 | $0.00117 |
| Haiku 4.5 | $0.00006 | $0.00058 |
Grade A, and why
sr:validate 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.
What it actually says
sr:validate
Goal: validate the generated SRS against IEEE 830-1998. Fix all ERRORs before reporting. WARNings are captured for the improvement report.
Step 0 — Identify Project
AskUserQuestion: "Which project to validate? (slug)"
Check that projects/{slug}/srs/ exists and contains .md files.
If missing: tell user to run /sr:generate first.
Step 1 — Run Validator
Execute:
python .claude/scripts/srs_validator.py --dir projects/{slug}/srs/
Capture stdout. Display the full validation table.
Step 2 — Fix ERRORs Immediately
For each ERROR finding:
- Identify the affected SRS section file
- Fix the issue following IEEE 830 rules:
missing-section: add the section with correct heading formatfr-no-shall: rewrite the Requirement line with explicit "shall" clausefr-numbering-gap: renumber FRs to fill gaps (update all references)
- State what was fixed:
FIXED [ERROR] fr-no-shall in FR-07 → added "shall" clause
After fixing all ERRORs: re-run validator and confirm no new ERRORs.
Step 3 — Collect WARNings
List all WARN findings but do NOT fix them here — they go into the improvement report.
Format:
WARN: unresolved-tag — 3 [TBD] tags remain (NFR-03, NFR-07, §1.3)
WARN: fr-incomplete-gwt — FR-12: missing "Then:" field
WARN: nfr-no-numeric — NFR-05 Response Measure has no numeric threshold
Step 4 — Verdict
Output:
## Validation Result
Verdict: COMPLIANT | PARTIALLY COMPLIANT | NON-COMPLIANT
File: projects/{slug}/srs/ ({N} files)
ERRORs fixed: {count}
WARNings open: {count} → captured in /sr:improve report
Next: /sr:improve → improvement report + warning resolution guide
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 · 84 lines · 59 tokens per session scan A ee0600f339f1
sr:validate is a skill published in the GitHub repository huuanh20/awesome-ai-agent-skills (1 stars, last pushed 3mo ago), licensed MIT. It adds 59 tokens to every session and 583 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
obsidian
Read, search, and create Markdown notes inside an Obsidian vault on disk.
Manipulate PDF files — merge, split, extract pages/text, PDF↔images, OCR, info — via the qpdf / poppler / ocrmypdf CLIs. Use to combine, slice, convert, or OCR PDFs.
pandoc
Convert documents between formats (Markdown, DOCX, HTML, PDF, RST, EPUB, LaTeX) via the pandoc CLI. Use to transform a document from one format to another.
xlsx
Create and edit Excel .xlsx workbooks (sheets, cells, formulas, formatting) via Python openpyxl. Use to generate spreadsheets or write/update cells. Reading is via os.fs.readdocument.
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.
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.