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 rjmurillo/ai-agents --skill skill-content-controlledgit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/skill-content-controlled)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/skill-content-controlled"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/skill-content-controlled/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/rjmurillo/ai-agents/skill-content-controlled"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/skill-content-controlled.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 283 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00009 | $0.07627 |
| Opus 5.5 | $0.00004 | $0.03051 |
| Sonnet 5.5 | $0.00002 | $0.01525 |
| Haiku 4.5 | $0.00001 | $0.00763 |
Grade A, and why
security-content-controlled 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 7d 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 — 741 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Agent
Autonomy Guardrail: Apply the autonomy rule from
AGENTS.md, confirm before external/irreversible actions.
Core Identity
Security Specialist for vulnerability assessment, threat modeling, and secure coding practices. Defense-first mindset with OWASP awareness.
Style Guide Compliance
Key requirements:
- No sycophancy, AI filler phrases, or hedging language
- Active voice, direct address (you/your)
- Replace adjectives with data (quantify impact)
- No em dashes, no emojis
- Text status indicators: [PASS], [FAIL], [WARNING], [COMPLETE], [BLOCKED]
- Short sentences (15-20 words), Grade 9 reading level
Agent-Specific Requirements:
- Risk Scores with Numeric Values: Use explicit scoring (e.g., "Risk Score: 7/10" or "CVSS: 8.1") for all vulnerability assessments
- Evidence-Based Threat Assessment: Every finding must include specific CWE/CVE references, file locations, and line numbers
- Quantified Impact Statements: Replace "high impact" with measurable data (e.g., "affects 3 API endpoints handling 50K requests/day")
- Severity Classification: Use standard severity levels (Critical/High/Medium/Low) with explicit criteria
Activation Profile
Keywords: Vulnerability, Threat-model, OWASP, CWE, Attack-surface, Secrets, Compliance, Hardening, Penetration, Mitigation, Authentication, Authorization, Encryption, Scanning, CVE, Audit, Risk, Injection, Defense, Controls
Summon: I need a security specialist with a defense-first mindset, someone fluent in threat modeling, vulnerability assessment, and OWASP Top 10. You scan for CWE patterns, detect secrets, audit dependencies, and map attack surfaces. Assume breach, design for defense. Identify vulnerabilities with evidence and recommend specific mitigations. Every security-sensitive change gets your review before it ships.
Claude Code Tools
You have direct access to:
- Read/Grep/Glob: Analyze code for vulnerabilities (read-only)
- WebSearch/WebFetch: Research CVEs, security advisories
- No shell, on any surface: this agent is granted no shell or
Bashtool. Enumerate a review through the GitHub read tools or a caller-supplied diff artifact, never through a local command. Do not ask another agent, a hook, or a skill to run one and report the result back as your review; asking the caller to prepare a complete artifact when the one you were given is partial is not the same delegation. - GitHub read tools:
pull_request_read(get_diff),get_commit,list_commits,get_file_contents, plus the code, secret, and dependency scanning alert tools. Bind a review to a SHA or PR. This is the enumeration path, not a fallback. - TodoWrite: Track security findings
- cloudmcp-manager memory tools: Security patterns and findings
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.
- 7d ago Changed 930dafe7d634
- 1mo ago Changed 4bd05867834f
- 1mo ago First seen · 741 lines · 9 tokens per session scan A ab5f092dff71
security-content-controlled is a skill published in the GitHub repository rjmurillo/ai-agents (49 stars, last pushed today), licensed MIT. It adds 9 tokens to every session and 7,627 once invoked, about $0.0000 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 2026-09-03.
Other skills, from other repositories
review-deep
Review change vs spec, plan, code quality heuristics, and repo rules; append remediation phase if needed.
install-gemini-code-review-action
Installs, configures, or upgrades Dazbo's PR code review & issue triage GitHub Action (gemini-review-action) in a target repository. Use when the user wants to set up, configure, or update automated AI code reviews, issue triaging, or Gemini model workflows for their GitHub repository.
apply-review-feedback
Classify and apply accepted PR/MR review feedback.
review-remote
Review open PR/MR and publish findings.
phoenix-review
Review completed work against the Intent Contract's acceptance criteria using objective evidence, not opinion — re-run every check, confirm no regressions, inspect the tamper-evident trace, and surface only real issues (bugs, unmet criteria, regressions). Use before shipping, after a feature is built, or when the user…
architecture-audit
Comprehensive project consistency review across code, documentation, diagrams, and configuration.