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 skills/0xrafasec/ai-workflow/securitynpx skills add 0xrafasec/ai-workflow --skill securitygit clone --depth 1 https://github.com/0xrafasec/ai-workflowWrote 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/0xrafasec/ai-workflow/security)<a href="https://agentmods.dev/skills/0xrafasec/ai-workflow/security"><img src="https://agentmods.dev/badge/skills/0xrafasec/ai-workflow/security.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.00947 |
| Opus 5 | $0.00039 | $0.00474 |
| Sonnet 5 | $0.00015 | $0.00189 |
| Haiku 4.5 | $0.00008 | $0.00095 |
Grade A, and why
security 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 4d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create the threat model. Argument: $ARGUMENTS
Context Gathering
Before interviewing, understand the system's security surface:
-
Check for existing docs:
- Read
docs/THREAT_MODEL.md— if revising, don't start from scratch - Read
docs/ARCHITECTURE.mdfor system structure and trust boundaries - Read
CLAUDE.md,README.md
- Read
-
Explore the codebase for security-relevant patterns:
- Look for auth middleware, JWT handling, session management
- Check for input validation patterns (or lack thereof)
- Look for database queries (raw SQL vs ORM/parameterized)
- Check for secrets management (env vars, config files, hardcoded values)
- Check for exposed endpoints (route definitions, API handlers)
- Read
docker-compose.ymlor deployment config for service exposure - Summarize what you found to the user — "Here's what I see from a security perspective: ..."
Interview
Use AskUserQuestion to understand the security landscape. Adapt based on what the codebase reveals.
- Trust boundaries — What is trusted? What is untrusted? Where are the boundaries?
- Authentication — How do users/agents/services prove identity? What mechanisms exist today?
- Authorization — Who can do what? How are permissions modeled?
- Sensitive data — What data is sensitive? Credentials, PII, financial? Where does it live? How is it protected at rest and in transit?
- Attack surface — What is exposed to the internet? To local users? To other services? What inputs does the system accept?
- Threat actors — Who would attack this? Script kiddies, insiders, nation states? What are their capabilities?
- Compliance — Any regulatory requirements? SOC2, GDPR, HIPAA, PCI?
- Existing security measures — What's already in place? What's missing?
For inherited projects: "I see you're using X for auth — is that intentional or legacy? I notice Y has no input validation — is that a known gap?"
Write
Write to docs/THREAT_MODEL.md:
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.
- 4d ago First seen · 104 lines · 77 tokens per session scan A b295c1bca9f9
security is a skill published in the GitHub repository 0xrafasec/ai-workflow (9 stars, last pushed 25d ago), licensed MIT. It adds 77 tokens to every session and 947 once invoked, about $0.0004 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 skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…