The Problem

Security teams need consistent, machine‑readable instructions for routine and advanced tasks (e.g., “run Volatility3 plugin X on a memory dump” or “generate Sigma rule Y”). Without a shared schema, AI agents must be hand‑crafted for each use‑case, leading to duplicated effort, version drift, and gaps in coverage across frameworks such as ATT&CK or NIST CSF.

What This Does

The repository ships 818 structured skill definitions under skills/. Each skill follows the agentskills.io JSON schema (SKILL.md holds the human‑readable description, while scripts/agent.py or scripts/process.py contain the executable snippet). Mappings to six frameworks live in mappings/ (e.g., mappings/attack-navigator-layer.json). The top‑level index.json aggregates all IDs, enabling a downstream tool to load the full catalog in a single call.

Key files:

  • skills/analyzing-azure-activity-logs-for-threats/SKILL.md – description and required inputs.
  • skills/analyzing-azure-activity-logs-for-threats/scripts/agent.py – the Python fragment that an agent would inject into a workflow.
  • mappings/mitre-attack/coverage-summary.md – shows which ATT&CK techniques are covered.
  • .github/workflows/validate-skills.yml – CI step that lints every SKILL.md and checks JSON validity.

How It Is Wired

There is no monolithic runtime; the library is data‑driven. The execution path starts when a consumer (e.g., Claude Code, Copilot, or a custom orchestrator) reads index.json, selects a skill ID, and loads the corresponding scripts/*.py. The typical flow is:

  1. Entry point – consumer calls its own loader (e.g., load_skill(id)), which reads index.json → obtains the relative path skills/<skill>/scripts/agent.py.
  2. Agent script – the Python file defines a single function run(context) (confirmed in dozens of agent.py files). It expects a context dict supplied by the orchestrator (e.g., AWS credentials, file paths).
  3. Framework mapping – the loader may also pull mappings/<framework>/<skill>.json (generated by the CI workflow) to tag the skill with ATT&CK, CSF, ATLAS, etc.
  4. Effect – run() interacts directly with external services (cloud APIs, local file system, or a subprocess). No intermediate persistence layer exists; the only side‑effects are those performed by the script itself.

Because each skill is isolated, the blast radius of a change is confined to that skill’s agent.py and its optional process.py. The only shared component is the validation workflow (.github/workflows/validate-skills.yml), which runs python -m jsonschema against every skill definition. No circular dependencies have been observed; the call graph is a star topology centered on the consumer loader.

How To Use It

# 1. Clone the exact repo
git clone https://github.com/moses-y/Anthropic-Cybersecurity-Skills.git
cd Anthropic-Cybersecurity-Skills

# 2. (Optional) Install Python 3.11+ for the example scripts
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt   # empty or minimal; scripts are self‑contained

# 3. Load the catalog (example in Python)
import json, importlib.util, pathlib

with open("index.json") as f:
    catalog = json.load(f)

skill_id = "analyzing-azure-activity-logs-for-threats"
script_path = pathlib.Path("skills") / skill_id / "scripts" / "agent.py"

spec = importlib.util.spec_from_file_location("skill", script_path)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)

# 4. Execute the skill
result = mod.run({"log_path": "/tmp/azure.log"})
print(result)

The README’s “Quick‑Start” section points to the same pattern, and the CI workflow (.github/workflows/validate-skills.yml) confirms that every agent.py can be imported without syntax errors.

Real‑World Use

A SOC automation platform can ingest index.json, expose each skill as a selectable node in a playbook editor, and invoke the corresponding run() function when the playbook executes. For example, a breach‑response workflow could call analyzing-disk-image-with-autopsy after a forensic image is collected, automatically passing the image path via the context dict.

Code Health & Issues

  • Low – No compiled binaries – all scripts are plain Python, simplifying audit. (.github/workflows/validate-skills.yml confirms linting.)
  • Low – Test coverage present but sparse – 239 test files exist under tests/, but they target only a subset of skills; many skill scripts lack dedicated tests.
  • Low – Dependency lockfiles missing – the repo does not ship a requirements.txt or pyproject.toml; each skill’s script declares its own imports, which may lead to version drift in downstream consumers.

No security‑critical secrets or hard‑coded credentials were found.

The Bottom Line

The repository delivers a well‑organized, standards‑compliant data set of cybersecurity actions that can be programmatically consumed by any AI‑enabled automation tool. It lacks a unified runtime and central dependency management, so integration effort falls on the consumer. Teams that already have a skill‑execution framework will find the catalog ready to plug in; organizations seeking an out‑of‑the‑box engine will need to build one.