agentleFS
gonimargonimar/claude-web-studio/skills/threat-model/SKILL.md

threat-model

Skill4 starsChanged 46 days agoMITView on GitHub

Builds or updates the STRIDE threat model — assets, trust boundaries, attack surfaces (auth, GraphQL/REST, WebSocket, uploads, webhooks, admin, CI/CD, dependencies, infra), threats with likelihood/impact/mitigation, verification. Produces docs/architecture/threat-model.md. Required before build and when a new surface appears.

57 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use vuln-scan, supply-chain-review and 9 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • AnthropicSkill

    vuln-scan

    Static source-code vulnerability scan. Reads a target directory (and THREAT_MODEL.md if present), spawns parallel review subagents per focus area, and writes VULN-FINDINGS.json + .md for /triage to consume. Read-only — no building, running, or network. For execution-verified crashes, use vuln-pipeline instead. Use when asked to "scan for vulns", "review this code for security issues", "find bugs in <dir>", or as the step between /threat-model and /triage.

    Also in 5 projects that use this

  • MesteriisSkill

    supply-chain-review

    Reviews lockfiles, installers, provenance, CI permissions, dependency confusion, artifacts, registries, and publishing.

    Also in 3 projects that use this

  • AnthropicSkill

    patch

    Generate candidate fixes for verified security findings. Consumes

    Also in 4 projects that use this

  • mbwsimsSkill

    orbit

    Run for repo-wide or diff-wide readiness sweeps: "/orbit", "/orbit pr", "/orbit deep", "/orbit quick", "audit this project", "project-wide review", "is this PR ready overall", or "combined security, tests, codebase, evolution, and instruction review". Use this for broad multi-system assessment, not for a single-file review or a one-domain question.

    Also in 3 projects that use this

  • mbwsimsSkill

    test-plan

    This skill should be used when the user asks to "plan tests", "what should I test", "what tests do I need", "test plan for", "testing strategy for", "test matrix for", "what cases should I cover", "help me plan tests", mentions "/test-plan", or wants to understand what tests are needed before writing any test code.

    Also in 3 projects that use this

  • patrickserranoSkill

    performance-audit

    Audit and improve SwiftUI runtime performance. Use for requests to diagnose slow rendering, janky scrolling, high CPU/memory usage, excessive view updates, or layout thrash in SwiftUI apps.

    Also in 3 projects that use this

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 51 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    When HEAD is a story branch, name it and ask one AskUserQuestion: switch to the default branch and commit there (Recommended — a …

    line 39

“Common” is measured: kept by at least 6 in 10 of the 51 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Threat Model

Reply in the project conversation language (CLAUDE.md → Language); code, identifiers, paths and commit messages stay in English.

Template .claude/docs/templates/threat-model.md; security-baseline.md, security-standards.md, graphql.md (security), llm-integration.md (Security) for LLM/MCP surfaces. In the commands below, <hooks> is .claude/hooks/ in copy mode and ${CLAUDE_PLUGIN_ROOT}/hooks/ in plugin mode.

Phase 1: System

  1. Scope from the argument: full (default) walks every surface; <surface> analyses only that surface and updates its rows in the existing model.
  2. Read the product spec (data, jurisdiction), technical-preferences (including Deploy target, Infra repo and Proxy config — proxy requirements need an owner and a file), the API contract, compose/infra, the existing threat model. No product spec → continue from technical-preferences and the code, and say so in the model.
  3. Draw the DFD (mermaid) with trust boundaries; ask about the non-obvious (external integrations, admin access, payments).

Phase 2: Surfaces and threats

  1. Per surface: STRIDE threats with likelihood/impact; mitigations referencing the baseline; status (exists/planned/none).
  2. Surfaces that need their own rows:
    • Data export / deletion, when the data model classifies personal data: who may request, how identity is verified, what is exported (and what must not be), how deletion propagates to replicas, backups and logs, and the evidence kept.
    • GraphQL: introspection, complexity, batching, field authorisation, persisted ops.
    • Games: anti-cheat, modified clients, chat spam, room DoS.
    • LLM features — when technical-preferences § LLM features is not none, or the code imports an LLM/MCP SDK (@anthropic-ai/sdk, anthropic, anthropic-sdk-go, anthropic-ai/sdk, @modelcontextprotocol/*, github.com/modelcontextprotocol/go-sdk, mcp, mcp/sdk — the bare Python names mcp and anthropic are grep-able only in requirements.txt / pyproject.toml, not in code): read references/llm-surface.md and add its rows — trust boundaries for model inputs (direct and indirect prompt injection), tool-call side effects and exfiltration, RAG / document ingestion, and every MCP server the product runs or connects to — with the same STRIDE, likelihood/impact and status columns; absent when neither signal exists.
  3. appsec-engineer and network-security-engineer via Task in parallel complete their areas.

Phase 3: Priorities

Top 5 unmitigated threats → proposed stories (/create-stories) or ADRs (/architecture-decision); residual risks explicitly accepted by the user.

Phase 4: Write

The gate follows coordination-rules rule 7's exact order: the draft (or a tight summary of it) in the chat message first, then "May I write docs/architecture/threat-model.md?" — one AskUserQuestion: write (Recommended) · show the draft/diff first · not now — and Write only after that answer, never write-then-ask. Add a "Security" section to affected feature specs with consent. After the "write" answer: touch .claude/.write-consent (rule 7 — the consent-guard hook checks the marker).

Phase 5: Commit (documents lane)

Right after the write, one commit gate (rule 7 (4), .claude/docs/git-workflow.md § Documents): docs: threat model (or docs: threat model — <surface> for a single-surface update), staging exactly the written files — docs/architecture/threat-model.md and the feature specs whose "Security" section was added with consent. Record the gate before asking — <hooks>session-state.sh set Gate "/threat-model Phase 5: commit?" — and clear it after the answer (<hooks>session-state.sh set Gate "—").

  • On the default branch when no story work is in progress: one AskUserQuestion — commit (Recommended) · leave uncommitted.
  • When HEAD is a story branch, name it and ask one AskUserQuestion: switch to the default branch and commit there (Recommended — a pipeline-wide document) · commit here (the document belongs to this story) · leave uncommitted.
  • This skill writes no code; should a mitigation snippet have been written on request, it is not a document and never rides the docs: commit — name it and offer the chore lane (git-workflow.md § Chore / infra) or the story it belongs to.

Nothing is committed without the answer.

Verdict: COMPLETE | HIGH RISK (N unmitigated). Next step — one AskUserQuestion: /create-stories for the mitigations (Recommended) · /security-audit after implementation · revise the model.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.