appsec-engineer

Security Skills for AI Coding Agents

Drop structured security skills into your AI coding agent. Get instant, framework-grounded security expertise.

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Why This Exists

AI coding agents can perform security reviews, but they hallucinate framework control numbers, miss entire vulnerability categories, and produce inconsistent output across runs. The result is security guidance that sounds authoritative but falls apart under scrutiny.

These skills ground agents in real published frameworks -- OWASP, NIST, MITRE ATT&CK, and CIS Controls -- so that every finding maps to a verifiable control. They are not prompt dumps. They are structured, framework-referenced, injection-hardened skill files that produce reliable, auditable security output.

Quick Start

git clone https://github.com/UnitOneAI/SecuritySkills.git
cd SecuritySkills

Claude Code (native format — auto-discovery and /slash-commands)

# Global install — all skills available via auto-discovery and /skill-name
cp -r skills/*/* ~/.claude/skills/

# Or project-local
mkdir -p .claude/skills && cp -r skills/*/* .claude/skills/

# Then use naturally:
# "Review this code for security issues"    → Claude auto-loads secure-code-review
# /threat-modeling                          → Direct invocation
# /cve-triage CVE-2024-1234                 → With arguments

Gemini CLI

# Reference skills via @ commands
cp -r skills/ ~/.gemini/skills/

Cursor

# Add as Cursor rules
cp -r skills/ .cursor/rules/

Codex CLI / Kiro / Generic

# Point any agent at a skill's SKILL.md file
codex --context skills/appsec/threat-modeling/SKILL.md "Review this design"
kiro spec --skill skills/ai-security/llm-top-10/SKILL.md

Each skill is a directory with SKILL.md as the entrypoint, following the Agent Skills open standard. Claude Code discovers skills automatically; other tools can load them by path.

Skill format

Every skill is a directory at skills/<domain>/<skill-name>/ with SKILL.md as the entrypoint, following the Agent Skills open standard.

SKILL.md frontmatter

All skills use the same YAML frontmatter fields:

name: threat-modeling                 # kebab-case, matches the directory
description: >                        # what it does + when it auto-invokes
  Runs a structured STRIDE threat model on any design, API spec, or codebase...
tags: [appsec, design, architecture]  # domain + activity keywords
role: [security-engineer, architect]  # which role bundles include it
phase: [design, review]               # SDLC phase
frameworks: [STRIDE, MITRE-ATT&CK]    # cited frameworks — real control IDs only
difficulty: intermediate              # beginner | intermediate | advanced
time_estimate: "30-60min"
version: "1.0.0"
author: unitoneai
license: MIT
allowed-tools: Read, Grep, Glob       # tools the skill may use
injection-hardened: true              # reviewed against OWASP LLM01
argument-hint: "[target-file-or-directory]"
# context: fork                       # optional

The machine-readable schema for this frontmatter lives at schemas/skill.schema.json. Validate all skills and role bundles locally with:

ruby scripts/validate_skill_schema.rb

Validate skill fixture manifests and expected evidence strings with:

ruby scripts/test_skill_fixtures.rb

Validate remediation regression manifests and expected auto-fix diffs with:

ruby scripts/test_remediation_fixtures.rb

Regenerate the deterministic skill quality scorecard with:

ruby scripts/generate_quality_scorecard.rb
ruby scripts/generate_quality_scorecard.rb --check

Validate framework provenance, versions, owners, and review dates with:

ruby scripts/validate_framework_registry.rb
ruby scripts/validate_framework_registry.rb --stale --max-age-days 365

Validate required domain CODEOWNERS review gates with:

ruby scripts/validate_codeowners.rb

Release archives include SHA-256 checksums generated by the release workflow. See docs/release-integrity.md for verification steps.

CI/CD examples for GitHub Actions, GitLab CI, Azure DevOps, Jenkins, pre-commit, and local agent usage are available in docs/ci-cd-examples.md. Validate those examples locally with:

ruby scripts/validate_ci_cd_examples.rb

Normalized finding JSON

Every skill must be able to emit findings as normalized JSON that validates against schemas/finding.schema.json. The top-level envelope, required run/skill metadata, finding fields, evidence, framework/CWE references, remediation fields, and test strategy requirements are documented in docs/normalized-json-output.md. When SARIF is requested, skills should map those normalized findings to SARIF 2.1.0-compatible JSON using docs/sarif-output.md. When tracker handoff is requested, skills should map normalized findings to tracker-ready work items using docs/tracker-handoff.md and schemas/tracker-handoff.schema.json.

Progressive disclosure (keep SKILL.md lean)

Claude's skill guidance: when a SKILL.md would exceed ~500 lines, don't inline everything — split detail into sibling reference files in the same directory and link to them from SKILL.md. The agent loads a reference only when it needs it, so the entrypoint stays cheap to load.

skills/appsec/threat-modeling/
├── SKILL.md                  ← entrypoint (lean): when-to-use, rules, output format
├── threat-actor-profiles.md  ← reference, loaded on demand
└── csharp-dotnet.md          ← language-specific reference

This is why some skills ship extra .md files alongside SKILL.md (e.g. cloud/aws-review/benchmark-checklist.md, compliance/soc2-gap/tsc-criteria.md) — it is the intended pattern, not duplication.

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Skills

45 skills across 10 security domains.

Application Security

AI Security

Identity & Access