AWS Cdk Development
zxkane/aws-skills
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python.
A skill your agent uses when planning, running, or learning from chaos engineering experiments.
Install Chaos Engineering
$ npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install alirezarezvani/claude-skills chaos-engineering --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/skills/chaos-engineering .claude/skills/chaos-engineering && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "chaos-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineering into .claude/skills/chaos-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chaos-engineering", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineeringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install alirezarezvani/claude-skills chaos-engineering --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/skills/chaos-engineering .agents/skills/chaos-engineering && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "chaos-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineering into .agents/skills/chaos-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chaos-engineering", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install alirezarezvani/claude-skills chaos-engineering --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/skills/chaos-engineering .cursor/skills/chaos-engineering && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "chaos-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineering into .cursor/skills/chaos-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chaos-engineering", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/alirezarezvani/claude-skills.git --path engineering/skills/chaos-engineering--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install alirezarezvani/claude-skills chaos-engineering --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/skills/chaos-engineering .gemini/skills/chaos-engineering && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "chaos-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineering into .gemini/skills/chaos-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chaos-engineering", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install alirezarezvani/claude-skills chaos-engineeringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/skills/chaos-engineering .github/skills/chaos-engineering && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "chaos-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineering into .github/skills/chaos-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chaos-engineering", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install alirezarezvani/claude-skills chaos-engineering --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/alirezarezvani/claude-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/skills/chaos-engineering .opencode/skills/chaos-engineering && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "chaos-engineering" agent skill from https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineering into .opencode/skills/chaos-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "chaos-engineering", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
chaos-engineering- GitHub stars
- 28k
- Token cost
- ~2.7k tokens
- SKILL.md length
- 850 words
- Files
- 10 (incl. scripts, references, assets)
- Skills in repo
- 342
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when planning, running, or learning from chaos engineering experiments.
- Works in 4 steps: Build a hypothesis around steady-state… → Vary real-world events. Inject realistic… → Run experiments in production. Staging… → …
- Learning from chaos engineering experiments
- SKILL.md covers When to use, When NOT to use, Core principle: chaos without… and Quick start, plus 10 more sections
- Runs Python scripts from its folder; calls python
What it does
Chaos Engineering is an agent skill from alirezarezvani/claude-skills. Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a…
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts, reference files and assets (for example `assets/experiment_template.md`, `assets/postmortem_template.md` and `references/attack_taxonomy.md`).
It sits in DevOps & Cloud, covering Chaos engineering. It works with Python, Amazon Web Services and Kubernetes. The repository describes itself as: 380 Claude Code skills & agent skills & plugins (30+ Agents, 70+ custom commands, 380+ skills, customizable references, scripts)for Claude Code, Codex, Gemini CLI, Cursor, and 8… The licence is MIT.
When your agent uses it
- Learning from chaos engineering experiments
- Chaos experiment
- Fault injection
- Resilience test
Example prompts
- “chaos experiment”
- “fault injection”
- “gameday”
- “/chaos-engineering”
Requirements
- Python 3
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Build a hypothesis around steady-state behavior. Not "what breaks?" but "X holds; will it still hold under fault Y?"
- Vary real-world events. Inject realistic failures: kill nodes, slow networks, lose cache, throttle dependencies.
- Run experiments in production. Staging never has the same failure modes. Start small.
- Automate experiments to run continuously. One-off chaos is a press release; continuous chaos is engineering.
What it can do on your machine
Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Chaos Engineering loads about 2.7k tokens when it runs, and up to ~8.5k if it reads all its reference files. Until then it costs about 171 tokens; SKILL.md has 850 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 850 words, ~2,659 tokens.
Download SKILL.mdSave it as
.claude/skills/chaos-engineering/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.- name
- chaos-engineering
- description
- Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).
- context
- fork
- version
- 2.9.0
- author
- claude-code-skills
- license
- MIT
- tags
- chaos-engineering, resilience, fault-injection, gameday, sre, reliability, chaos-toolkit, chaos-mesh, litmus, gremlin, aws-fis
- compatible_tools
- claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli
Chaos Engineering
Design experiments that surface real weaknesses in production systems — without becoming outages. Most "chaos engineering" attempts skip steady-state measurement, define no abort criteria, and have no blast-radius bound. This skill enforces the discipline that makes chaos experiments safe and useful.
When to use
- Planning a chaos experiment (what to break, where, when, how to abort)
- Calculating blast radius before running the experiment
- Reviewing an existing experiment plan for safety
- Choosing a chaos tool (Chaos Toolkit / Chaos Mesh / Litmus / Gremlin / AWS FIS)
- Writing a chaos experiment postmortem
- Running a Game Day exercise
When NOT to use
- General incident response (use
incident-response) - Threat hunting / red-team (use
red-team,threat-detection) - Performance load testing (different goal — chaos is about failure modes, not capacity)
- Production debugging (chaos discovers weaknesses preemptively, not after-the-fact)
Core principle: chaos without abort criteria is an outage
The 4 Principles of Chaos Engineering (Netflix, 2016):
- Build a hypothesis around steady-state behavior. Not "what breaks?" but "X holds; will it still hold under fault Y?"
- Vary real-world events. Inject realistic failures: kill nodes, slow networks, lose cache, throttle dependencies.
- Run experiments in production. Staging never has the same failure modes. Start small.
- Automate experiments to run continuously. One-off chaos is a press release; continuous chaos is engineering.
Add a fifth: Define abort criteria up front. A chaos experiment with no abort criteria is an outage by another name.
Quick start
SKILL=engineering/chaos-engineering/skills/chaos-engineering
# 1. Design an experiment
python "$SKILL/scripts/experiment_designer.py" --target "checkout-svc" --hypothesis "p99 latency stays <500ms" --attack latency --duration-min 15
# 2. Calculate blast radius
python "$SKILL/scripts/blast_radius_calculator.py" --traffic-share 0.05 --user-pop 1000000 --duration-min 15
# 3. Generate postmortem after the experiment
python "$SKILL/scripts/experiment_postmortem.py" --plan experiment.json --result-log results.txtThe 3 Python tools
All stdlib-only. Run with --help.
experiment_designer.py
Generates a structured experiment plan from inputs. Enforces the required sections (hypothesis, steady-state metric, blast radius, abort criteria, rollback).
python scripts/experiment_designer.py \
--target "checkout-svc" \
--hypothesis "p99 latency stays <500ms when payment-svc is slow" \
--attack latency \
--magnitude "+200ms" \
--duration-min 15 \
--blast-radius "5% of US traffic" \
--abort-if "p99 > 1000ms OR error_rate > baseline + 1pp"Outputs a markdown plan with: hypothesis, steady-state, attack, magnitude, duration, blast radius, abort criteria, rollback procedure, monitoring dashboards, and learning question.
blast_radius_calculator.py
Computes the blast radius of a planned experiment. Given traffic share + user population + duration, calculates expected affected users, expected error budget burn, and a risk score.
python scripts/blast_radius_calculator.py \
--traffic-share 0.05 \
--user-pop 1000000 \
--duration-min 15 \
--baseline-availability 0.999 \
--expected-impact-availability 0.95Outputs:
- Expected affected users
- Error budget consumed (in minutes of error budget)
- Risk score: GREEN / YELLOW / RED
- Recommendation: PROCEED / REDUCE / ABORT
GREEN = <1% error budget; YELLOW = 1-10%; RED = >10%.
experiment_postmortem.py
Produces a structured postmortem from an experiment plan + results. Catches the common postmortem failure modes: no learning recorded, no follow-up actions, blame-laden language.
python scripts/experiment_postmortem.py --plan experiment.json --result-log results.txtOutputs markdown with: summary, hypothesis (was it confirmed/refuted?), what we learned, what surprised us, follow-up actions with owners, and link to next experiment.
The 7 attack types (taxonomy)
Different attacks reveal different weaknesses. See references/attack_taxonomy.md for full detail.
| Attack | What it tests | Tooling |
|---|---|---|
| Latency | Timeouts, retries, circuit breakers | tc, Chaos Mesh NetworkChaos |
| Error | Error handling, fallback paths | Chaos Mesh HTTPChaos, Toxiproxy |
| Resource (CPU, memory, disk) | Saturation handling, autoscaling | Chaos Mesh StressChaos, stress-ng |
| Network partition | Split-brain, consensus, failover | Chaos Mesh NetworkChaos partition |
| Dependency failure | Graceful degradation, fallback | Service mesh fault injection |
| Time | Clock skew, NTP issues | libfaketime, Chaos Mesh TimeChaos |
| Infrastructure (kill instance) | Auto-recovery, failover | AWS FIS, Chaos Monkey |
Pick the attack that matches the hypothesis. "What happens if X is slow?" → latency. "What happens if X loses network?" → partition.
Show full SKILL.md (358 more words)Show less
Tooling chooser
| Tool | Best for | Pricing | Stack |
|---|---|---|---|
| Chaos Toolkit | Lightweight, language-agnostic, JSON experiments | OSS | Any |
| Chaos Mesh | Kubernetes-native, rich CRDs, in-cluster | OSS | Kubernetes |
| Litmus | Kubernetes, Argo-integrated, large library | OSS + Enterprise | Kubernetes |
| Gremlin | Enterprise SaaS, multi-cloud, audit | Paid | Any |
| AWS FIS | AWS-native, IAM-integrated, EC2/ECS/EKS | Paid (AWS) | AWS |
| Custom | Niche needs, single-cloud, low budget | None | Any |
Decision rules:
- k8s-only stack + OSS → Chaos Mesh or Litmus (Litmus has bigger experiment library)
- Multi-cloud + OSS → Chaos Toolkit
- AWS-heavy + simple needs → AWS FIS
- Enterprise + audit/compliance → Gremlin
See references/tooling_landscape.md for trade-offs.
Workflows
Workflow 1: Design and run a single experiment
1. State a hypothesis: "When [fault], steady-state metric X stays within Y."
2. Identify the steady-state metric — must be measurable BEFORE the experiment.
3. Run blast_radius_calculator.py — confirm GREEN before proceeding.
4. Run experiment_designer.py to produce the plan.
5. Get a peer review of the plan; confirm abort criteria are concrete.
6. Notify the on-call team in #incidents (or whatever channel).
7. Run the experiment with monitoring open.
8. If abort criteria are hit, abort immediately; record what happened.
9. Run experiment_postmortem.py to capture learnings.
10. File follow-up actions; link to next experiment.Workflow 2: Game Day exercise
1. Pick a scenario (e.g., "primary database fails over").
2. Identify all dependent services that should keep working.
3. Build a multi-experiment plan covering each layer.
4. Schedule with stakeholders; on-call coverage required.
5. Run with a facilitator who manages the scenario.
6. Capture observations in a shared doc as they happen.
7. Single combined postmortem covering all observations.
8. Track follow-up actions in a board with owners.Workflow 3: Continuous chaos (game days → daily)
1. Start: weekly Game Day in staging.
2. Move to: weekly Game Day in production with limited blast radius.
3. Mature to: continuous chaos via scheduled experiments (Litmus chaos schedule, Gremlin scenarios).
4. Wire to deployment: every prod deploy triggers a baseline chaos sweep.
5. Track: experiments per week, weaknesses discovered, MTTR trend.Composition with other skills
This skill explicitly composes with two others in this library:
| Skill | Composition |
|---|---|
feature-flags-architect | Kill switches defined there are the abort triggers here |
kubernetes-operator | Operators are common chaos targets (test reconcile under fault) |
incident-response | Chaos experiments that escalate become incidents |
Anti-patterns
- No hypothesis — "let's break things" is sabotage, not engineering
- No steady-state metric — without a baseline, you can't tell if X broke
- No blast radius bound — full-prod experiment without limits = outage
- No abort criteria — see above; this is mandatory
- No on-call coverage — chaos without monitoring is unmonitored production
- Chaos in staging only — staging never has prod failure modes
- Chaos in dev — useless; dev has different failure modes from prod
- One-off chaos — single experiment is a press release; learning requires recurrence
- Blame-laden postmortem — record causes, not blame; teams stop running chaos otherwise
References
references/chaos_principles.md— the 4 principles, history, when to startreferences/experiment_design.md— hypothesis structure, steady-state metrics, abort criteriareferences/attack_taxonomy.md— 7 attack types with examples and toolingreferences/tooling_landscape.md— Chaos Toolkit / Mesh / Litmus / Gremlin / FIS / DIY
Slash command
/chaos-experiment — interactive experiment design wizard that runs all 3 tools.
Asset templates
assets/experiment_template.md— fill-in plan templateassets/postmortem_template.md— structured postmortem template
Verifiable success
A team using this skill should achieve:
- 100% of chaos experiments have a written hypothesis, abort criteria, and blast-radius calculation
- Blast radius for any single experiment never exceeds 10% of error budget
- Mean time between chaos experiments <14 days (continuous, not one-off)
- Each experiment produces ≥1 follow-up action that gets shipped
- No chaos experiment escalates to a customer-impacting incident in trailing 90 days
© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 9 other files (scripts, references, assets) in engineering/skills/chaos-engineering of alirezarezvani/claude-skills.
- SKILL.md
- assets/experiment_template.md
- assets/postmortem_template.md
- references/attack_taxonomy.md
- references/chaos_principles.md
- references/experiment_design.md
- references/tooling_landscape.md
- scripts/blast_radius_calculator.py
- scripts/experiment_designer.py
- scripts/experiment_postmortem.py
Open the folder on GitHubat commit 19392f7
Compare with similar skills
Chaos Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Chaos Engineering this skillalirezarezvani/claude-skills | 28k | — | ~2.7k | Automated safety check: Pass | MIT | |
| AWS Cdk Developmentzxkane/aws-skills | 367 | 2 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit | 260 | 6 repos | ~1.1k | Automated safety check: Notes | Custom licence | |
| Vetatilladeniz/Kubeli | 387 | 2 repos | ~1.6k | Automated safety check: Pass | MIT | |
| Install Boltmcpboltmcp/boltmcp | 371 | — | ~2.3k | Automated safety check: Pass | None | |
| Provider Bug Reviewmondoohq/mql | 412 | — | ~2.9k | Automated safety check: Pass | Custom licence |
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Questions about Chaos Engineering
What does Chaos Engineering do?
A skill your agent uses when planning, running, or learning from chaos engineering experiments. Chaos Engineering is an agent skill from alirezarezvani/claude-skills. Use when planning, running, or learning from chaos engineering experiments.
When should I use Chaos Engineering?
Chaos Engineering fits situations like: learning from chaos engineering experiments; chaos experiment; fault injection; resilience test.
How do I install Chaos Engineering in Claude Code?
Run `npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a claude-code`. Or copy the skill folder (engineering/skills/chaos-engineering in alirezarezvani/claude-skills) into .claude/skills/chaos-engineering in your project. Claude Code loads it when a task matches its description.
How do I install Chaos Engineering in Codex?
Run `npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a codex`. Or copy the skill folder (engineering/skills/chaos-engineering in alirezarezvani/claude-skills) into .agents/skills/chaos-engineering in your project. Codex loads it when a task matches its description.
Can I use Chaos Engineering in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alirezarezvani/claude-skills --skill chaos-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/chaos-engineering, .gemini/skills/chaos-engineering, .github/skills/chaos-engineering and .opencode/skills/chaos-engineering in your project.
What does Chaos Engineering need to run?
Going by SKILL.md and its folder, Chaos Engineering needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Chaos Engineering access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Chaos Engineering safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Chaos Engineering use?
Chaos Engineering is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Chaos Engineering use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 5.8k tokens, read only when the agent opens those files.
What are the alternatives to Chaos Engineering?
Skills that share tags, products or a category with Chaos Engineering: AWS Cdk Development (zxkane/aws-skills, 367 stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars), Vet (atilladeniz/Kubeli, 387 stars) and Install Boltmcp (boltmcp/boltmcp, 371 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Chaos Engineering?
alirezarezvani (a GitHub user) maintains it in alirezarezvani/claude-skills, which has 27,891 GitHub stars. The repository holds 342 skills in this directory. The repository was last updated on August 30, 2026.
Source: alirezarezvani/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.