skilltotal 0.14.0

AI Component Security Platform — static security analysis for AI components (CLI engine)

SkillTotal

AI Component Security Platform — open-source CLI engine.

SkillTotal statically analyzes AI-related components — agent skills/plugins, MCP servers, npm / Python packages, repositories, and AI-generated projects you upload as an archive or file — to surface supply-chain risks, dangerous capabilities, prompt-injection surfaces, and data-exfiltration paths before the component is installed or trusted. Point it at a path, a git URL, an npm: / pypi: package, or a project archive (.zip / .tar.gz) / single file.

Try it online (no install, no account): www.skilltotal.ai — the website runs this same engine. Prefer the CLI? pipx install skilltotal (below).

It analyzes only the component itself — never your user, company, environment, deployment, or runtime context. Every score and finding is derived exclusively from the files inside the component.

Core principle: every confirmed finding carries evidence (file, line range, code snippet). Anything that cannot be evidenced is placed in needs_review, never in findings, and never affects the score.

Why SkillTotal

• 100% local & offline — the component's code never leaves your machine. No account, no API token, no cloud upload (unlike cloud scanners that send your components to a backend).
• Zero runtime dependencies, pure Python stdlib — auditable and easy to vendor/air-gap.
• Deterministic — regex + AST, no LLM in the static engine; the same input always yields the same report.
• Evidence-anchored & low false-positive — every finding points at an exact file:line.
• Free and open source (Apache-2.0) — the full static report is free, forever.

Install

Requires Python 3.10+. Zero runtime dependencies. git is required only for scanning remote URLs.

Recommended for the CLI — pipx (isolated install; also works on Debian/Ubuntu where bare pip install is blocked by PEP 668):

pipx install skilltotal

Or into a virtual environment / as a library:

pip install skilltotal

From source (development):

pip install -e ".[dev]"

Usage

# Human-readable report
skilltotal scan ./path/to/component

# Scan a remote repository (shallow git clone)
skilltotal scan https://github.com/owner/repo

# Scan a project archive or a single file (e.g. an AI-generated project downloaded as a ZIP)
skilltotal scan ./my-project.zip
skilltotal scan ./app.tar.gz
skilltotal scan ./suspicious.py

# Scan a package from a registry (latest, or a pinned version)
skilltotal scan npm:left-pad
skilltotal scan npm:left-pad@1.3.0
skilltotal scan pypi:requests
skilltotal scan pypi:requests==2.31.0

# JSON to stdout
skilltotal scan ./component --json

# SARIF 2.1.0 (GitHub Code Scanning / IDE)
skilltotal scan ./component --sarif --output report.sarif

# Write the report to a file (SARIF if --sarif, else JSON)
skilltotal scan ./component --output report.json

# CI gate: exit code 2 by severity level or by risk score
skilltotal scan ./component --fail-on-high             # alias for --fail-on high
skilltotal scan ./component --fail-on medium
skilltotal scan ./component --fail-on-score 50

# Skip paths (repeatable; combined with the config file's `exclude`)
skilltotal scan ./component --exclude "vendor/*" --exclude "*.min.js"

# Baseline: snapshot current findings, then suppress them on later scans
skilltotal scan ./component --write-baseline .skilltotal-baseline.json
skilltotal scan ./component --baseline .skilltotal-baseline.json --fail-on-high

# Inventory: discover AI components already installed on this machine and scan them
# (reads agent configs for Claude Desktop/Code, Cursor, Windsurf, VS Code, Gemini, and
#  local skills; derives an npm:/pypi:/local source per MCP server and runs the engine)
skilltotal inventory
skilltotal inventory --json
skilltotal inventory --no-scan          # list only, do not scan
skilltotal inventory --project .        # also include this project's agent configs

# List every detection rule
skilltotal rules list
skilltotal rules list --json

Baseline suppresses findings by a stable fingerprint of (rule id, file, code snippet) — independent of line numbers, so it survives edits. Suppressed findings are removed before scoring and do not affect the risk score.

Project config (optional) — commit a .skilltotal.toml instead of repeating flags (CLI flags override it):

fail_on = "high"           # low | medium | high | critical
fail_on_score = 50         # or gate on the 0-100 risk score
exclude = ["vendor/*", "*.min.js"]
ignore = ["ST-NET-PY"]     # rule ids to drop
baseline = ".skilltotal-baseline.json"

Suppress a single finding inline with a # skilltotal:ignore (or # skilltotal:ignore[ST-ID]) comment on its line.

python -m skilltotal ... works identically to the skilltotal console script.

Exit codes

Code	Meaning
0	Success
1	Usage / collection error (e.g. path missing, clone failed)
2	A configured gate tripped (--fail-on/--fail-on-high severity, or --fail-on-score)

CI / GitHub Action

Run SkillTotal in CI and surface findings in your repository's Security → Code scanning tab.

# .github/workflows/skilltotal.yml
name: SkillTotal
on: [push, pull_request]
permissions:
  contents: read
  security-events: write   # required to upload SARIF to Code Scanning
jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: pezhik/skilltotal@v0.10.4
        with:
          source: .          # a path, a git URL, or an npm:/pypi:<name> spec
          fail-on: high      # fail the build on a high/critical finding (or 'none')

The action installs the CLI, scans source, uploads SARIF (so findings appear inline on pull requests and in Code Scanning), and fails the job on a high/critical finding unless fail-on: none. Pin the engine with version: 0.10.4. Prefer plain CLI? It is the same thing: skilltotal scan . --sarif --output skilltotal.sarif --fail-on-high.

Methodology

SkillTotal performs static security analysis of AI components — MCP servers, agent skills/plugins, npm and PyPI packages, and AI-generated projects/repositories. The engine combines capability analysis, dangerous-pattern detection, privilege analysis, supply-chain (install-time) analysis, prompt-surface analysis, and data-flow correlation (e.g. secret access combined with network egress). Findings are mapped to risk categories and contribute to a 0–100 risk score; capabilities are reported but never inflate the score — capability ≠ risk. Nothing is executed and no LLM is called, so results are deterministic and reproducible.

What it detects

Category	Examples
Shell execution	subprocess.*, os.system, child_process.exec
Filesystem access	open, read_text/write_text, fs.readFile/writeFile
Sensitive paths	~/.ssh, ~/.aws, .env, id_rsa, credentials, secrets
Network egress	requests, urllib, aiohttp, fetch, axios
Install-time execution	npm preinstall/postinstall/prepare, setup.py hooks
Dynamic code execution	eval, exec, compile, new Function, vm.runInNewContext
Obfuscation	decode-and-execute chains, base64 blobs, hex escaping, minification
MCP risks	manifests, dangerous tools (shell/fs/network/credential), server commands
Prompt surface	"ignore previous instructions", "reveal system prompt", exfiltration phrasing

Coverage by component type

Legend: ✅ analyzed by default for this component type · ⚠️ the engine detects this, but that surface is uncommon for this type — so it is flagged only when the component actually contains it (e.g. prompt-injection text inside an npm/PyPI package) · ❌ not applicable to this type · 🚧 planned (SkillTotal Cloud).

Columns are the component types SkillTotal scans. AI project = a scanned repository or folder — an agent skill/plugin, an AI-generated codebase, or a set of prompts/configs — that is not a published npm/PyPI package.

Category	MCP	npm	PyPI	AI project
Prompt injection / instruction override	✅	⚠️	⚠️	✅
Tool poisoning (MCP tool metadata)	✅	❌	❌	⚠️
Dangerous capabilities (shell / fs / network)	✅	✅	✅	⚠️
Data exfiltration (secret access + egress)	✅	✅	✅	⚠️
Secret theft / sensitive-path access	✅	✅	✅	⚠️
Dynamic code execution	✅	✅	✅	⚠️
Obfuscation (decode-and-execute)	✅	✅	✅	✅
Hidden-Unicode smuggling	✅	✅	✅	✅
Embedded secrets (hardcoded keys/tokens)	✅	✅	✅	✅
Install-time / supply-chain hooks	⚠️	✅	✅	❌
Overprivileged / auto-approved tools	✅	❌	❌	⚠️
Runtime behavior analysis	🚧	🚧	🚧	🚧
Sandbox analysis	🚧	🚧	🚧	🚧

Typical findings

• An MCP tool can execute arbitrary shell commands
• A package downloads and runs code from an external URL
• Access to credential locations (~/.aws, ~/.ssh, .env) detected
• Dynamic code execution (eval / exec) detected
• Prompt-injection / instruction-override phrasing in a tool description or skill
• Sensitive-data access combined with outbound network egress
• Hardcoded API keys or tokens
• An MCP server with auto-approved or overprivileged tools
• Untrusted input (environment, sys.argv, a request/response body) flowing into exec or a shell — a proven injection path, not just a dangerous API in isolation
• An agent skill does more than its declared allowed-tools allow (undeclared capability / least-privilege violation)

Out of scope

SkillTotal statically analyzes a single component's own files. It does not execute code, observe runtime behavior, or assess your environment, deployment, or infrastructure. It is not a substitute for:

• a penetration test
• an application-security (app-sec) review
• an architecture / design review
• a cloud-security or infrastructure assessment
• a Kubernetes / container runtime audit
• a business-logic review
• a manual code review

Runtime behavior and sandbox analysis are planned for SkillTotal Cloud (paid).

Output

A normalized report containing the component identity, a risk score (0–100) and risk level (low / medium / high / critical), detected capabilities (each evidence-backed), findings, needs_review, and metadata. See docs/report-schema.md and docs/scoring.md.

Architecture

The package under skilltotal/ (except cli.py) is a pure, side-effect-free library so the same engine can power the future web app and enterprise SaaS. See docs/architecture.md.

Development

pip install -e ".[dev]"
pytest

Accuracy notes

• Python is analyzed via an AST (resolves import aliases, tells open(p,'w') from a read, ignores API names that only appear in strings/comments). Node.js/config use regex.
• Test code (__tests__/, *.test.*, tests/, conftest.py, …) is demoted to needs_review — it is not executed by consumers, so it does not affect the score.
• Ambiguous signals (bare secrets/credentials words, lone base64 blobs, "before answering" phrasing, minified files) go to needs_review, never to findings.
• Hidden Unicode (ASCII-smuggling tag characters, Trojan-Source bidi overrides, zero-width chars) is detected and decoded — a real evasion used to smuggle instructions past human review. See tests/manual_eval/ for calibration against real-world attacks.
• Shell execution covers subprocess/os.system, asyncio.create_subprocess_*, Node child_process, and common process-spawning libraries (Python sh/plumbum/pexpect/ invoke/fabric; Node zx/execa/cross-spawn/shelljs/tinyexec/node-pty).
• MCP dangerous tools are classified by name/description both in JSON manifests and when defined in code (server.tool("run_command", …), @mcp.tool over def read_file).
• Limitations: detection is at the call/import level. Capability via an unrecognized higher-level library (e.g. a git library that writes files internally, a browser library) may not be flagged as a raw filesystem/shell call. Capabilities indicate presence, not proven misuse.

Open source vs SkillTotal Cloud

SkillTotal is open core. This engine (analysis + all detection rules + CLI) is open source and complete on its own — run it locally or in CI, free, offline, with zero runtime dependencies. It tells you what a component does, with evidence.

Paid features are delivered only via SkillTotal Cloud (the website) and explain why it matters: LLM interpretation and prioritization of findings, dynamic sandbox execution, hosting, scan history, and monitoring. They are server-side services on top of this engine — their code is not part of this repository. See docs/open-core.md.

License

Apache-2.0. See also NOTICE.