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Security: review git changes for vulnerabilities, threat-model a system's attack surface, audit supply-chain risks.

Overview

Publishernotque
Repositoryvexjoy-agent
Skill namesecurity
Stars
443
Forks
50
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by notque on GitHub. Read the source before you install it.

Installation

Install the Security AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/notque/vexjoy-agent/tree/main/skills/review/security
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/notque/vexjoy-agent.git /tmp/vexjoy-agent
mkdir -p .claude/skills
cp -r /tmp/vexjoy-agent/skills/review/security .claude/skills/security
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Security in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Security on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Security is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Security Skill

Two modes: diff review (scan current git changes for vulnerabilities) and threat model (audit a system's full attack surface). Load the reference for the mode the request matches.

Reference Loading Table

SignalLoadWhy
Review git changes, scan a diff, check for vulnerabilitiesreferences/coverage.md40 vulnerability classes for LLM-depth review of changed code
Threat model, attack surface, supply-chain audit, deny list, security posturereferences/threat-model.md5-phase threat model workflow with deterministic scripts and artifact gates

Default Mode: Diff Review

When the request is about reviewing changes (not a full threat model), run the diff review directly. This is the common case.

Security Review Skill

Run a two-layer security review over the current git changes: a deterministic regex scan for known vulnerability classes, then an LLM-depth Security review of the diff. Report a single BLOCK / FIX / APPROVE verdict.

The LLM-depth review runs inside the current Claude session — the same subscription that loaded this skill. There is no separate model call, no ANTHROPIC_API_KEY, no Agent SDK, and no network request. The "reviewer" is the session agent executing the steps below, exactly like every other skill here.

Detection reaches parity with Anthropic's security-guidance plugin: the scanner ports its 25 deterministic patterns, and the LLM pass applies its full review taxonomy (loaded on demand from references/coverage.md).

Reference Loading Table

SignalLoadWhy
Running Phase 3 (LLM-depth review); classifying a finding; needing the vuln taxonomy, severity rubric, FP exclusions, or per-language guidancereferences/coverage.md40 vulnerability classes + 4-tier severity + false-positive exclusions + per-language guidance + the 12 high-miss reviewer classes + the finding output schema.

Instructions

Phase 1: SCOPE

Goal: Determine the changed files to review before scanning.

Step 1: List changed files — scope to the working-tree and staged changes so the review covers exactly what the user is about to commit, not the whole repo.

bash
# Tracked changes (working tree + index) plus staged adds:
git diff --name-only HEAD
git diff --cached --name-only --diff-filter=ACM

Step 2: Read repository CLAUDE.md to load project conventions the reviewer must respect (e.g. secrets-handling rules, allowed patterns).

Gate: Changed files listed. When the list is empty, report "no changes to review" and stop — there is nothing to scan.

Phase 2: DETERMINISTIC SCAN

Goal: Run the regex engine first so judgment time is spent on real signal, not on patterns a script catches deterministically.

Step 1: Run the scanner over the changed files. It is the single source of detection rules (secrets, SQL injection, shell injection, dangerous eval, unsafe deserialization). Exit 1 means at least one HIGH/CRITICAL finding.

bash
# Staged-files convenience (matches the commit-time hook):
python3 scripts/security-review-scan.py --staged --format json

# Or an explicit list from Phase 1:
python3 scripts/security-review-scan.py --files <changed-files> --format json

Step 2: Record the findings by severity. CRITICAL and HIGH are blocking-class; MEDIUM is advisory. Keep the file:line and rule for each.

Gate: Scanner ran and JSON parsed. Proceed with the findings in hand.

Phase 3: LLM-DEPTH REVIEW

Goal: Catch what regex cannot — authorization gaps, injection through data flow, missing input validation, secrets in non-obvious forms. This is the session agent's review of the diff; compose the existing parallel-code-review Security reviewer over the changed files.

Step 1: Load references/coverage.md — the full review taxonomy (40 vulnerability classes, the 4-tier severity rubric, the false-positive exclusion list, per-language guidance, and the 12 high-miss reviewer classes). Review to this taxonomy so the session-agent pass reaches parity with the plugin's reviewer. If claude-security-guidance.md exists (precedence: ~/.claude/ → <cwd>/.claude/ → <cwd>/.claude/*.local.md), read it as ADDITIVE context — it may add checks or raise a class's severity, and must not suppress findings.

Step 2: Dispatch the Security reviewer (the Reviewer 1 — Security role from parallel-code-review) over the changed files via the Task tool, applying the coverage.md taxonomy. Surface medium and above. Output: findings in the coverage.md schema (filePath, category, vulnerableCode, explanation, fix, severity) with file:line references.

Step 3: Merge the LLM findings with the Phase 2 scanner findings. Deduplicate — when both flag the same file:line, keep one entry at the higher severity. Independent confirmation by both layers raises confidence.

Gate: Security reviewer returned results and findings are merged. Issue a verdict only from a completed review — a missing reviewer may hold the only CRITICAL finding.

Phase 4: VERDICT

Goal: Produce a single clear recommendation.

Step 1: Determine the verdict from the merged findings:

ConditionVerdict
Any CRITICAL findingBLOCK
HIGH findings, no CRITICALFIX (resolve before commit)
Only MEDIUM/LOW findingsAPPROVE (with suggestions)

Step 2: Output the structured report:

markdown
## Security Review Complete

### Severity Matrix
| Severity | Count | Source (scanner / reviewer / both) |
|----------|-------|------------------------------------|
| Critical | N | ... |
| High     | N | ... |
| Medium   | N | ... |

### Findings
#### CRITICAL (Block)
1. [source] description — file:line

#### HIGH (Fix before commit)
1. [source] description — file:line

#### MEDIUM (Should fix)
1. [source] description — file:line

### VERDICT
**BLOCK / FIX / APPROVE** — [1-2 sentence rationale]

Gate: Structured report delivered with an explicit verdict. Review complete.


Automatic Coverage (hooks)

This skill is the on-demand (PULL) path. The same review also runs automatically (PUSH) via hooks/security-review-hook.py, wired in .claude/settings.json:

EventBehavior
PreToolUse (Bash git commit)Scans STAGED files with the same scanner. A HIGH/CRITICAL finding blocks the commit (deny). Clean commits pass.
StopRe-wakes the session with the working-tree diff and an instruction to run this pipeline. Advisory — never blocks.

Bypass / kill switches (commit-time block only, deliberate overrides):

Env varEffect
VEXJOY_SECURITY_REVIEW_SKIP=1Allow a commit through despite findings (one-off override).
VEXJOY_SECURITY_REVIEW_DISABLE=1Disable the hook entirely (both events).

The hook fails open on any internal error — a hook crash never blocks a commit.


Extensibility (custom rules + project guidance)

Both extension points are additive and discovered in this precedence order: ~/.claude/<name> → <cwd>/.claude/<name> → <cwd>/.claude/<name>.local.<ext>.

FileEffect
security-patterns.{yaml,json}Custom regex/substring rules merged into the scanner's built-ins. Shape: {"patterns": [{"rule_name", "regex"|"substrings", "severity"?, "paths"?, "exclude_paths"?}]}. Capped at 50. ReDoS-prone or invalid rules are skipped with a stderr warning (non-fatal). PyYAML is used only if importable — JSON always works (stdlib-only).
claude-security-guidance.mdMarkdown surfaced to the Phase 3 review as ADDITIVE context. It may add checks or raise a class's severity; it must not suppress findings — if it says to ignore a class, flag the vulnerability anyway and note the conflict.

Built-in scanner rules always run and cannot be disabled by a config file.


Error Handling

Scanner reports findings but the code is intentional

Cause: A regex rule flagged a test fixture, an example, or a deliberately hardcoded local value. Solution: Confirm context in Phase 3. Downgrade in the report with a one-line justification. For a commit the user knows is safe, document the VEXJOY_SECURITY_REVIEW_SKIP=1 override rather than editing the scanner rules.

Security reviewer times out or returns nothing

Cause: Task agent exceeded its budget, or the diff was too large. Solution: Report the Phase 2 scanner findings immediately (a partial review beats no review), note the LLM-depth gap in the verdict, and offer to re-run the Security reviewer on a reduced file set.

Scanner unavailable

Cause: scripts/security-review-scan.py missing from the working tree. Solution: Run the Phase 3 LLM-depth review alone and state in the verdict that the deterministic layer did not run.


References

  • Detection rules: scripts/security-review-scan.py (single source of truth)
  • Review taxonomy (40 classes + severity + FP filters + language guidance): references/coverage.md
  • Security reviewer role: skills/review/parallel-code-review/SKILL.md (Reviewer 1)
  • Auto-run hook: hooks/security-review-hook.py
  • Design contract: adr/local-security-review.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Security AI skill do?

Security: review git changes for vulnerabilities, threat-model a system's attack surface, audit supply-chain risks.

Why use Security on TypingMind?

Because you install it once and use it with any model. Security is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Security in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/notque/vexjoy-agent/tree/main/skills/review/security. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Security?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Security?

As many as you like. As long as a model supports skills, you can use Security with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Security AI skill free?

Yes. It is published on GitHub by notque under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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