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 agents/cveralyon/axel-setup/security-checkgit clone --depth 1 https://github.com/cveralyon/axel-setupWhat 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 | $0.00021 | $0.00309 |
| Opus 5 | $0.00010 | $0.00154 |
| Sonnet 5 | $0.00004 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
security-check 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 2d 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
Run security analysis on the current repository.
-
Detect project type from files present (Gemfile → Rails, package.json → Node, requirements.txt → Python)
-
Rails:
bundle exec brakeman --no-pager(static analysis)bundle audit check --update(dependency vulnerabilities)
-
Node/Next.js:
pnpm auditornpm audit
-
All repos:
- Scan for hardcoded secrets: grep for API keys, passwords, tokens in source files
- Check
.envfiles aren't tracked in git - Verify
.gitignorecovers sensitive files
-
Report with severity levels:
- CRITICAL: must fix now (exposed secrets, known CVEs)
- HIGH: fix soon (security warnings)
- MEDIUM: track (informational findings)
Threat-model verification (escalate when needed)
The scans above are tooling-level (SAST + dependency audit). For auth, permissions, or other sensitive changes — especially within a GSD project/phase — recommend escalating to gsd-secure-phase, which verifies threat mitigations against a threat model. Surface this as a recommendation; the main agent runs the skill.
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.
- 2d ago First seen · 30 lines · 21 tokens per session scan A a083d6cc1b90
security-check is an agent published in the GitHub repository cveralyon/axel-setup (4 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 309 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.
Other agents, from other repositories
syllago-author
/home/hhewett/.local/src/syllago/content/agents/syllago-author/AGENT.md.
feature-flow
Build, test, verify, and review an already planned feature. Operates on a feature branch off trunk; prepares a PR but does not merge.
review
Review PR and build output for quality, security, and compliance. Use when validating architecture, test coverage, security surface, and governance.
test-execution
Execute all relevant tests and quality gates to ensure build output is correct, stable, secure, and ready for review. This is feature-flow's Phase 3 (and Phase 3.5 for live-system verification) — local, pre-push verification. Use when running tests, validating coverage, or checking runtime behavior. Distinct from the…
design
Convert the specification into a clear, actionable technical design with architecture, components, interfaces, and data flows. Use when translating requirements into a buildable system design.
learn
Product retrospective agent. Runs after a release, after a measure agent anomaly flag, or at end of sprint. Maps findings to DORA AI capabilities and produces plan agent action items. Distinct from fawkes learn.md which handles platform incident postmortems.