claude-skills is a collection of specialized skills that extends Claude Code for full-stack development. Developers use it for programming languages, frameworks, infrastructure, APIs, testing, DevOps, security, data and machine learning, platform tasks, and project workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Jeffallan/claude-skills --skill chaos-engineergit clone --depth 1 https://github.com/Jeffallan/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jeffallan/claude-skills/chaos-engineer)<a href="https://agentmods.dev/skills/jeffallan/claude-skills/chaos-engineer"><img src="https://agentmods.dev/badge/skills/jeffallan/claude-skills/chaos-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jeffallan/claude-skills/chaos-engineer"><img src="https://agentmods.dev/badge/skills/jeffallan/claude-skills/chaos-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00088 | $0.01577 |
| Opus 5 | $0.00044 | $0.00788 |
| Sonnet 5 | $0.00018 | $0.00315 |
| Haiku 4.5 | $0.00009 | $0.00158 |
Grade A, and why
chaos-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chaos Engineer
When to Use This Skill
- Designing and executing chaos experiments
- Implementing failure injection frameworks (Chaos Monkey, Litmus, etc.)
- Planning and conducting game day exercises
- Building blast radius controls and safety mechanisms
- Setting up continuous chaos testing in CI/CD
- Improving system resilience based on experiment findings
Core Workflow
- System Analysis - Map architecture, dependencies, critical paths, and failure modes
- Experiment Design - Define hypothesis, steady state, blast radius, and safety controls
- Execute Chaos - Run controlled experiments with monitoring and quick rollback
- Learn & Improve - Document findings, implement fixes, enhance monitoring
- Automate - Integrate chaos testing into CI/CD for continuous resilience
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Experiments | references/experiment-design.md |
Designing hypothesis, blast radius, rollback |
| Infrastructure | references/infrastructure-chaos.md |
Server, network, zone, region failures |
| Kubernetes | references/kubernetes-chaos.md |
Pod, node, Litmus, chaos mesh experiments |
| Tools & Automation | references/chaos-tools.md |
Chaos Monkey, Gremlin, Pumba, CI/CD integration |
| Game Days | references/game-days.md |
Planning, executing, learning from game days |
Safety Checklist
Non-obvious constraints that must be enforced on every experiment:
- Steady state first — define and verify baseline metrics before injecting any failure
- Blast radius cap — start with the smallest possible impact scope; expand only after validation
- Automated rollback ≤ 30 seconds — abort path must be scripted and tested before the experiment begins
- Single variable — change only one failure condition at a time until behaviour is well understood
- No production without safety nets — customer-facing environments require circuit breakers, feature flags, or canary isolation
- Close the loop — every experiment must produce a written learning summary and at least one tracked improvement
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 185 lines · 88 tokens per session scan A 80619e93302d
chaos-engineer is a skill published in the GitHub repository Jeffallan/claude-skills (11,401 stars, last pushed 1mo ago), licensed MIT. It adds 88 tokens to every session and 1,577 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
tdd-xfail
Use when fixing a bug through strict xfail reproduction and needing proof that the test fails for the intended reason.
pytest-optimizer
Use when a pytest suite or its fixtures are slow and need measured, safety-gated optimization across the full pipeline.
tdd-fix
TDD bug-fix workflow — reproduce a bug as a failing test, find root cause, fix, and verify.
pytest-optimizer-00-scan
Use when starting or re-baselining a pytest optimization pass by profiling tests and fixtures without editing the suite.
pytest-optimizer-01-benchmark
Use when benchmarking pytest optimization hypotheses from 00-scan in isolation to identify speedups that beat measured noise.
pytest-optimizer-03-execute
Use when applying an approved pytest optimization plan as separate verified commits with resumable progress.