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/backspace-shmackspace/claude-devkit/threat-modelnpx skills add backspace-shmackspace/claude-devkit --skill threat-modelgit clone --depth 1 https://github.com/backspace-shmackspace/claude-devkitWrote 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/backspace-shmackspace/claude-devkit/threat-model)<a href="https://agentmods.dev/skills/backspace-shmackspace/claude-devkit/threat-model"><img src="https://agentmods.dev/badge/skills/backspace-shmackspace/claude-devkit/threat-model.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 | $0.00070 | $0.12680 |
| Opus 5 | $0.00035 | $0.06340 |
| Sonnet 5 | $0.00014 | $0.02536 |
| Haiku 4.5 | $0.00007 | $0.01268 |
Grade A, and why
threat-model 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 3d 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 — 1,311 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeling
Structured threat identification and risk assessment for software systems using STRIDE threat categorization and DREAD risk rating, producing both machine-readable Open Threat Model (OTM) JSON and human-readable markdown reports.
Core Principles
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Risk-First: Focus analysis effort on high-value assets and exposed trust boundaries. Not every component warrants equal analysis depth -- prioritize by data sensitivity and exposure.
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Evidence-Based: Every identified threat must be backed by specific data flows, components, and trust zone crossings. A threat without a concrete scenario is speculation, not analysis.
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Repeatable: The same project context must yield structurally consistent models regardless of which analyst or AI assistant performs the analysis. The three-phase workflow and verification checklist enforce this constraint.
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Dual-Output: Always produce both OTM JSON (for toolchain integration) and a markdown report (for human review). Neither is a subset of the other -- both must be complete.
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Honest: Explicitly state scope limits, assumptions, confidence levels, and what was NOT analyzed. A threat model that overstates its coverage is worse than one that honestly declares its boundaries.
A note on DREAD. Microsoft deprecated DREAD in 2008 in favor of CVSS-based bug bar approaches. This skill retains DREAD for three reasons: (1) CVSS scores vulnerabilities, not threats -- DREAD is purpose-built for threat modeling; (2) DREAD's five-axis model is simpler and more practical for systematic analysis than CVSS's base/temporal/ environmental scoring; (3) STRIDE-GPT and other modern threat modeling tools validate DREAD's continued utility. Organizations that require CVSS should map DREAD outputs to their internal risk scale after completing the threat model.
Output sensitivity. Threat model artifacts contain detailed descriptions of exploitable weaknesses, unmitigated threats, and attack scenarios. Treat all outputs (OTM JSON, markdown reports, working notes) as confidential. Store them in access-controlled repositories. Do not share threat model artifacts over unencrypted channels or in public forums. Consult your organization's data classification policy for specific handling requirements.
What ships with it
2 files 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.
- 3d ago First seen · 1,311 lines · 70 tokens per session scan A c692b8aaa9a4
threat-model is a skill published in the GitHub repository backspace-shmackspace/claude-devkit (15 stars, last pushed 10d ago), licensed MIT. It adds 70 tokens to every session and 12,680 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 2026-08-30.
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