ai-threat-testing
Offensive AI security testing and exploitation framework. Systematically tests LLM…
Skill creation, update and management — generates skill directory structure, validates against best practices, enforces line count limits. Use when creating, updating, or improving skills.
$ npx -y skills add transilienceai/communitytools --skill skill-update --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/skill-updateContext preview
The summary Claude sees to decide when to auto-load this skill.
Skill creation, update and management — generates skill directory structure, validates against best practices, enforces line count limits. Use when creating, updating, or improving skills.
name: skill-update description: Skill creation, update and management — generates skill directory structure, validates against best practices, enforces line count limits. Use when creating, updating, or improving skills. model: opus
Generate or refine Claude Code skills following Anthropic best practices.
skills/<skill-name>/ ├── SKILL.md # ≤150 lines, YAML + navigation ├── reference/ │ ├── *-principles.md # ≤150 lines (decision tree) │ ├── INDEX.md │ ├── *.md # patterns, ≤200 lines │ └── scenarios/ │ └── <category>/ │ └── *.md # ≤400 lines, self-contained └── README.md # optional, ≤100 lines
--- name: <skill-name> description: What it does AND when to use. Include trigger phrases. --- # <Skill Name> <one-paragraph scope> ## When to use - <bullet> ## Workflow / Quick start <≤30 lines> ## References - [reference/...](reference/...) ## Anti-Patterns - <when negative framing is genuinely needed, put it here>
The procedure is a workflow — every step is code-enforced, so none can be skipped.
Workflow('skill-update', { output_dir: 'projects/<engagement>' }) // harvest an engagement
Workflow('skill-update', { learnings: [{ text, technique_type }] }) // judge a known set
Workflow('skill-update', { mode: 'audit' }) // read-only, writes nothingAdd `dryRun: true` to see the write plan without writing. Parent-orchestrator only.
**Phases.** Intake (baseline `skill_linter.py --json`) → Harvest (reframe learnings) → Judge (four gates + blind refuters) → Route (author the block) → Write (persist verbatim) → Sweep (confidentiality guard) → Verify (linter **delta**, not an absolute clean tree — the base carries pre-existing violations).
Every promote/reject/write decision is pure JS in `.claude/workflows/lib/wf-helpers.mjs` (`promotionGate`, `capBudget`, `writeGate`, `lintDelta`, `skillUpdateGate`). No agent decides whether a learning is promoted or a write is allowed.
Process the techniques and failure modes from completed engagements. Promote a learning to the skill base only if **all four** hold:
1. **Generalizable.** Reusable pattern, not target-specific lore. No machine names, lab IDs, target IPs, preserved flags, writeup attributions. 2. **Material improvement.** Adds coverage, efficiency, or decision-quality for future engagements. 3. **Not already captured** elsewhere in the skill base. (`scripts/skill_linter.py` flags duplicates.) 4. **Minimal footprint.** Prefer extending an existing entry over adding a new file. Keep the base lean and high-signal.
Always frame as a reusable pattern: *"when encountering X condition, try Y approach"* — never *"on box-N, Y worked"*. Use `<TARGET_IP>`, `<DC_FQDN>`, `<DOMAIN>` placeholders in tool examples.
Three buckets, built in code so a run cannot claim an edit it did not make: **Updated.** / **Skipped.** (with the gate that failed) / **No changes.**
Open-source Claude Code skills and agents for AI-powered penetration testing, bug bounty hunting, AI threat testing, and security reconnaissance — from the team at Transilience.ai
Repo: transilienceai/communitytools
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