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 Eliyce/paqad-ai --skill stride-threat-modelgit clone --depth 1 https://github.com/Eliyce/paqad-aiWrote 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/eliyce/paqad-ai/stride-threat-model)<a href="https://agentmods.dev/skills/eliyce/paqad-ai/stride-threat-model"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/stride-threat-model/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/eliyce/paqad-ai/stride-threat-model"><img src="https://agentmods.dev/badge/skills/eliyce/paqad-ai/stride-threat-model.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 | $0.00025 | $0.00656 |
| Opus 5 | $0.00013 | $0.00328 |
| Sonnet 5 | $0.00005 | $0.00131 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
stride-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 9d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What It Does
Runs STRIDE threat enumeration across all project modules before any scripted checks execute. Every downstream finding produced in Steps 2–4 must map back to a STRIDE category from this inventory, giving the final report a coherent threat narrative instead of a list of isolated issues.
Use This When
Use this as the first skill in Step 1 so all subsequent skills have a threat inventory to reference. Always run it — even for small projects.
Inputs
- Read all module docs to identify assets (data stores, tokens, sessions, API keys, user actions, background jobs, external integrations).
- Read
references/stride-checklist.mdbefore starting enumeration.
Procedure
- Run
scripts/list-modules.shto enumerate canonical module slugs. - For each module, identify assets (data entities, user-facing actions, background jobs, external integrations, tokens/sessions).
- Walk every STRIDE category in
assets/stride-prompts.txtagainst each asset; never skip a category silently. - Build the inventory per
assets/output.template.jsonand write it to.paqad/pentest/runs/<run_id>/artifacts/stride-threats.json. - Validate with
scripts/validate-threats.sh— enforces required fields, allowed STRIDE category, severity vocabulary, ≤50 entries, and rejects generic/boilerplate threat descriptions. - Downstream skills (
input-validation-review,auth-mechanism-review,permission-boundary-review,finding-normalizer) consume this inventory.
Output Contract
- Match
assets/output.template.json: JSON array of{ module, asset, stride_category, threat_description, severity_hint }entries. stride_category∈spoofing | tampering | repudiation | information-disclosure | denial-of-service | elevation-of-privilege.severity_hint∈critical | high | medium | low.- Cap at 50 entries ordered critical → high → medium.
- Output must pass
scripts/validate-threats.sh(exit 0).
Escalate / Stop Conditions
- Ask when module docs do not describe any assets or workflows — cannot enumerate threats without context.
- Warn when no module docs exist at all — fall back to enumerating threats from route inventory alone.
- Do not produce generic STRIDE boilerplate; every threat entry must name a specific module, asset, or route.
What ships with it
6 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.
- 9d ago First seen · 63 lines · 25 tokens per session scan A bd4bebb47daf
stride-threat-model is a skill published in the GitHub repository Eliyce/paqad-ai (8 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 656 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 2026-09-03.
Other skills, from other repositories
pr-writing-review
Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer…
session-investigator
Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool…
auto-go
A command that implements code from a SPEC, a document describing the required behavior and work.
auto-plan
A code-planning skill that examines a codebase and creates a detailed specification, implementation plan, and acceptance criteria. It can organize requirements using EARS, a structured way to describe how software should behave in different situations.
agent-pipeline
Multi-agent pipeline orchestration skill.
adaptive-quality
Per-task execution profile selection based on complexity in Balanced quality mode.