Source profileQuality 73/100

openai/skills/skills/.curated/security-threat-model/SKILL.md

security-threat-model

Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

Source repository stars
25,188
Declared platforms
0
Static risk flags
1
Last source update
2026-07-14
Source checked
2026-08-26

Decision brief

What it does: where it fits

Deliver an actionable AppSec-grade threat model that is specific to the repository or a project path, not a generic checklist. Anchor every architectural claim to evidence in the repo and keep assumptions explicit. Prioritizing realistic attacker goals and concrete impacts over…

Best for

    Not for

    • Tasks that require unconfirmed production actions or broad system permissions.
    • Environments where the pinned source and install steps cannot be inspected.

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/openai/skills --skill "skills/.curated/security-threat-model"
    Safe inspection promptEditorial

    Inspect the Agent Skill "security-threat-model" from https://github.com/openai/skills/blob/49f948faa9258a0c61caceaf225e179651397431/skills/.curated/security-threat-model/SKILL.md at commit 49f948faa9258a0c61caceaf225e179651397431. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      Quick start

      1) Collect (or infer) inputs: - Repo root path and any in-scope paths. - Intended usage, deployment model, internet exposure, and auth expectations (if known). - Any existing repository summary or architecture spec. - Use prompts in references/prompt-template.md to generate a re…

      Collect (or infer) inputs:Repo root path and any in-scope paths.Intended usage, deployment model, internet exposure, and auth expectations (if known).
    2. 02

      Workflow

      Identify primary components, data stores, and external integrations from the repo summary.

      Identify primary components, data stores, and external integrations from the repo summary.Identify how the system runs (server, CLI, library, worker) and its entrypoints.Separate runtime behavior from CI/build/dev tooling and from tests/examples.
    3. 03

      1) Scope and extract the system model

      Identify primary components, data stores, and external integrations from the repo summary.

      Identify primary components, data stores, and external integrations from the repo summary.Identify how the system runs (server, CLI, library, worker) and its entrypoints.Separate runtime behavior from CI/build/dev tooling and from tests/examples.
    4. 04

      2) Derive boundaries, assets, and entry points

      Enumerate trust boundaries as concrete edges between components, noting protocol, auth, encryption, validation, and rate limiting.

      Enumerate trust boundaries as concrete edges between components, noting protocol, auth, encryption, validation, and rate limiting.List assets that drive risk (data, credentials, models, config, compute resources, audit logs).Identify entry points (endpoints, upload surfaces, parsers/decoders, job triggers, admin tooling, logging/error sinks).
    5. 05

      3) Calibrate assets and attacker capabilities

      List the assets that drive risk (credentials, PII, integrity-critical state, availability-critical components, build artifacts).

      List the assets that drive risk (credentials, PII, integrity-critical state, availability-critical components, build artifacts).Describe realistic attacker capabilities based on exposure and intended usage.Explicitly note non-capabilities to avoid inflated severity.

    Permission review

    Static risk signals and limitations

    Writes files

    medium · line 63

    The documentation asks the agent to create, modify, or delete local files.

    Write the final Markdown to a file named `<repo-or-dir-name>-threat-model.md` (use the basename of the repo root, or the in-scope directory if you were asked to model a subpath).

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score73/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars25,188SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    openai/skills
    Skill path
    skills/.curated/security-threat-model/SKILL.md
    Commit
    49f948faa9258a0c61caceaf225e179651397431
    License
    Not declared
    Collected
    2026-08-26
    Default branch
    main
    View the original SKILL.md

    Threat Model Source Code Repo

    Deliver an actionable AppSec-grade threat model that is specific to the repository or a project path, not a generic checklist. Anchor every architectural claim to evidence in the repo and keep assumptions explicit. Prioritizing realistic attacker goals and concrete impacts over generic checklists.

    Quick start

    1. Collect (or infer) inputs:
    • Repo root path and any in-scope paths.
    • Intended usage, deployment model, internet exposure, and auth expectations (if known).
    • Any existing repository summary or architecture spec.
    • Use prompts in references/prompt-template.md to generate a repository summary.
    • Follow the required output contract in references/prompt-template.md. Use it verbatim when possible.

    Workflow

    1) Scope and extract the system model

    • Identify primary components, data stores, and external integrations from the repo summary.
    • Identify how the system runs (server, CLI, library, worker) and its entrypoints.
    • Separate runtime behavior from CI/build/dev tooling and from tests/examples.
    • Map the in-scope locations to those components and exclude out-of-scope items explicitly.
    • Do not claim components, flows, or controls without evidence.

    2) Derive boundaries, assets, and entry points

    • Enumerate trust boundaries as concrete edges between components, noting protocol, auth, encryption, validation, and rate limiting.
    • List assets that drive risk (data, credentials, models, config, compute resources, audit logs).
    • Identify entry points (endpoints, upload surfaces, parsers/decoders, job triggers, admin tooling, logging/error sinks).

    3) Calibrate assets and attacker capabilities

    • List the assets that drive risk (credentials, PII, integrity-critical state, availability-critical components, build artifacts).
    • Describe realistic attacker capabilities based on exposure and intended usage.
    • Explicitly note non-capabilities to avoid inflated severity.

    4) Enumerate threats as abuse paths

    • Prefer attacker goals that map to assets and boundaries (exfiltration, privilege escalation, integrity compromise, denial of service).
    • Classify each threat and tie it to impacted assets.
    • Keep the number of threats small but high quality.

    5) Prioritize with explicit likelihood and impact reasoning

    • Use qualitative likelihood and impact (low/medium/high) with short justifications.
    • Set overall priority (critical/high/medium/low) using likelihood x impact, adjusted for existing controls.
    • State which assumptions most influence the ranking.

    6) Validate service context and assumptions with the user

    • Summarize key assumptions that materially affect threat ranking or scope, then ask the user to confirm or correct them.
    • Ask 1–3 targeted questions to resolve missing context (service owner and environment, scale/users, deployment model, authn/authz, internet exposure, data sensitivity, multi-tenancy).
    • Pause and wait for user feedback before producing the final report.
    • If the user declines or can’t answer, state which assumptions remain and how they influence priority.

    7) Recommend mitigations and focus paths

    • Distinguish existing mitigations (with evidence) from recommended mitigations.
    • Tie mitigations to concrete locations (component, boundary, or entry point) and control types (authZ checks, input validation, schema enforcement, sandboxing, rate limits, secrets isolation, audit logging).
    • Prefer specific implementation hints over generic advice (e.g., "enforce schema at gateway for upload payloads" vs "validate inputs").
    • Base recommendations on validated user context; if assumptions remain unresolved, mark recommendations as conditional.

    8) Run a quality check before finalizing

    • Confirm all discovered entrypoints are covered.
    • Confirm each trust boundary is represented in threats.
    • Confirm runtime vs CI/dev separation.
    • Confirm user clarifications (or explicit non-responses) are reflected.
    • Confirm assumptions and open questions are explicit.
    • Confirm that the format of the report matches closely the required output format defined in prompt template: references/prompt-template.md
    • Write the final Markdown to a file named <repo-or-dir-name>-threat-model.md (use the basename of the repo root, or the in-scope directory if you were asked to model a subpath).

    Risk prioritization guidance (illustrative, not exhaustive)

    • High: pre-auth RCE, auth bypass, cross-tenant access, sensitive data exfiltration, key or token theft, model or config integrity compromise, sandbox escape.
    • Medium: targeted DoS of critical components, partial data exposure, rate-limit bypass with measurable impact, log/metrics poisoning that affects detection.
    • Low: low-sensitivity info leaks, noisy DoS with easy mitigation, issues requiring unlikely preconditions.

    References

    • Output contract and full prompt template: references/prompt-template.md
    • Optional controls/asset list: references/security-controls-and-assets.md

    Only load the reference files you need. Keep the final result concise, grounded, and reviewable.

    Frequently asked questions

    What to verify before installation and use

    What does the security-threat-model source document cover?

    Deliver an actionable AppSec-grade threat model that is specific to the repository or a project path, not a generic checklist. Anchor every architectural claim to evidence in the repo and keep assumptions explicit. Prioritizing realistic attacker goals and concrete impacts over…

    How do I install security-threat-model?

    The source record exposes this install command: npx skills add https://github.com/openai/skills --skill "skills/.curated/security-threat-model". Inspect the command and pinned source before running it.

    Which permission-related actions were detected?

    Static rules flagged write-files in the source; the page lists the matching lines and excerpts.

    Alternatives

    Compare before choosing

    Computed 9860

    magnus919/agent-skills

    software-architecture-analysis

    Use this skill to reverse-engineer an existing software system, map its architecture, data flow, privacy posture, coupling, quality characteristics, and feature surface, then produce an evidence-grounded clean-room design document, PRD, or migration plan under new constraints. Use for codebase archaeology, implicit contract extraction, architecture health assessment, or decomposition-readiness analysis. Do not use for greenfield architecture design, direct code review, bug hunting, security audi

    Computed 9764

    Jamie-BitFlight/claude_skills

    standards-for-python-development

    Shared Python 3.11+ development standards covering type safety (ty, native generics, Protocol, TypeIs), layered architecture, error handling, performance, identifier naming, UI/CLI patterns (Rich/Typer), testing requirements (pytest, 80% coverage, TDD), and quality gates. Activates when any Python skill or agent needs to apply shared standards for implementation, code review, refactoring, or test authoring.

    Computed 9584

    mblode/agent-skills

    pr-reviewer

    Reviews the local diff or branch and returns a read-only, severity-tiered findings report. Modes cover standard bugs, structural quality, AI slop, and security audit. Use when asked to run /pr-reviewer, "review my changes", "code review", "thermo-nuclear review", "structural review", "deslop this", "clean up AI code", "security audit", "find vulnerabilities", or before commit, push, or handoff. For fixes use tidy; for PR creation use pr-creator; for CI or review comments use pr-babysitter; for f

    Computed 9425

    Borda/AI-Rig

    review

    Multi-agent code review of local Python files, directories, or the current git diff covering architecture, tests, performance, docs, lint, security, and API design. Scope: Python source files in local working tree. Python-file-free targets (pure JS/TS/Go/Rust projects) are out of scope. TRIGGER when: user asks to review local Python files, a directory, or the current git diff/working-tree changes, with no GitHub PR number involved; phrases: "review this", "review my changes", "code review this d