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 KyaniteLabs/checkyourself --skill 07-security-privacy-threat-modelinggit clone --depth 1 https://github.com/KyaniteLabs/checkyourselfWrote 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/kyanitelabs/checkyourself/07-security-privacy-threat-modeling)<a href="https://agentmods.dev/skills/kyanitelabs/checkyourself/07-security-privacy-threat-modeling"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/07-security-privacy-threat-modeling/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/kyanitelabs/checkyourself/07-security-privacy-threat-modeling"><img src="https://agentmods.dev/badge/skills/kyanitelabs/checkyourself/07-security-privacy-threat-modeling.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 | $0.00064 | $0.01845 |
| Opus 5 | $0.00032 | $0.00923 |
| Sonnet 5 | $0.00013 | $0.00369 |
| Haiku 4.5 | $0.00006 | $0.00185 |
Grade A, and why
security-privacy-threat-modeling 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 11d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
security-privacy-threat-modeling
Find and reduce application, API, infrastructure, privacy, and AI security risks before release.
Operating contract
Act as a production hardening specialist for 07 Security & Privacy. Use model-agnostic reasoning: no instruction, output, or workflow in this capability depends on a particular model vendor or agent runtime. Prefer deterministic evidence over persuasive prose. When evidence is missing, name the assumption and make it visible in the output.
When to activate
Use this capability for threat modeling, secure code review, OWASP risk review, API abuse, input/output validation, injection, XSS, CSRF, SSRF, deserialization, security headers, encryption, secrets exposure, audit logging, privacy-by-design, or security acceptance gates.
Inputs to request or inspect
- architecture
- data flows
- code diff
- API contracts
- identity model
- deployment topology
- data classification
Work protocol
- Identify assets, actors, trust boundaries, data flows, high-value actions, and abuse cases before listing controls.
- Map risks to application, API, data, infrastructure, supply-chain, privacy, and AI-specific categories as applicable.
- Trace untrusted input to dangerous sinks: database, shell, template, file, network, browser, model prompt, logs, and external APIs.
- Design controls in layers: validation, output encoding, authorization, isolation, rate limits, secure defaults, logging, and recovery.
- Prefer deterministic controls and tests over prompt-only or policy-only promises.
- Translate findings into owner, severity, exploit path, fix, verification, and release decision.
Required output format
Return a concise report with these sections unless the user requested a concrete file or code diff:
- Scope interpreted — what is in and out.
- Findings / decisions — ordered by production risk, not by discovery order.
- Recommended actions — owner-ready tasks with priority and rationale.
- Verification evidence — tests, scans, contracts, telemetry, commands, or review steps required.
- Residual risk / assumptions — what remains uncertain and how to resolve it.
- Hand-offs — other capabilities that should review the work.
What ships with it
1 file 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.
- 11d ago First seen · 131 lines · 64 tokens per session scan A 8a8ff69744d1
security-privacy-threat-modeling is a skill published in the GitHub repository KyaniteLabs/checkyourself (5 stars, last pushed 4d ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,845 once invoked, about $0.0003 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
code-review
Structured code reviews with severity-ranked findings and deep multi-agent mode. Use when performing a code review, auditing code quality, or critiquing PRs, MRs, or diffs. For the full multi-agent workflow, use the ia-review command (/ia-review in Claude Code).
receiving-code-review
Process code review feedback critically: check correctness before acting, push back on incorrect suggestions, no performative agreement. Use when responding to PR/MR review comments or implementing reviewer suggestions received from others.
tuicr
Use tuicr's review CLI to read and add comments in active TUI review sessions, and launch tuicr in cmux, tmux, Zellij, or Herdr when a user needs an interactive review pane.
liveagent-code-review
Review an open GitHub pull request or the current local branch and working tree with parallel, independent reviewers and evidence-based validation. Use when the user asks for code review, invokes the Code Review action from Git Review, or explicitly mentions this skill.
omh-image-cards
This is a Hermes-native img-summary workflow skill.
omh-code-review
This is a Hermes-native code-review workflow skill.