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chaos-engineering

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).

76

Quality

96%

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is chaos-engineering in alirezarezvani/claude-skills

SKILL.md
Quality
Evals
Security

Quality

Content

92%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An exceptionally well-structured skill body: copy-paste executable commands with concrete thresholds, gated workflows with abort criteria for a production-risky domain, and clean one-level progressive disclosure verified against real bundle files. The only trimmable fat is the brief recap of the well-known Netflix chaos principles.

Suggestions

Compress 'The 4 Principles of Chaos Engineering' recap to a single line pointing at references/chaos_principles.md, keeping only the fifth abort-criteria principle as novel guidance.

Unify script invocation paths between the Quick Start ('python "$SKILL/scripts/..."') and the per-tool sections ('python scripts/...') so examples are consistent and copy-pasteable from either context.

DimensionReasoningScore

Conciseness

The body is dense and efficient — tables, decision rules, thresholds, and anti-patterns with little padding — but the recap of 'The 4 Principles of Chaos Engineering (Netflix, 2016)' re-explains a concept Claude already knows (only the added fifth abort-criteria principle is novel), and tool output descriptions slightly duplicate what `--help` would show. Not anchor 3: the unnecessary explanation is confined to a few lines and everything else earns its tokens.

4 / 5

Actionability

Fully executable: the Quick Start and each tool section give copy-paste-ready invocations with all flags ('--traffic-share 0.05 --user-pop 1000000 --duration-min 15 --baseline-availability 0.999'), plus concrete output semantics (GREEN <1% / YELLOW 1-10% / RED >10% error budget) and if/then decision rules for tool selection. This matches the top anchor exactly.

5 / 5

Workflow Clarity

Workflow 1 — the production-risky core operation — has explicit validation gates and an error-recovery loop: 'confirm GREEN before proceeding', 'Get a peer review... confirm abort criteria are concrete', and 'If abort criteria are hit, abort immediately; record what happened'. Since this is a destructive/risky-operation skill and validation checkpoints are present, the cap-at-3 rule does not apply; the sequence matches the top anchor.

5 / 5

Progressive Disclosure

Clear overview with one-level-deep references that all exist on disk (references/attack_taxonomy.md, chaos_principles.md, experiment_design.md, tooling_landscape.md; all three scripts; both asset templates). Each reference is introduced inline with a one-line scope description, and summaries (attack table, tooling chooser) are appropriately split from full detail in the referenced files. Easy to navigate.

5 / 5

Total

19

/

20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A model description: it explicitly states what the skill ships and when to use it, with an exhaustive list of natural trigger terms including the specific chaos tooling names, all in third person with no padding. The only conceivable critique is length, but every clause carries information.

DimensionReasoningScore

Specificity

The description lists multiple concrete capabilities — 'experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python)', '4 references', 'a /chaos-experiment slash command' — covering design, blast-radius analysis, and postmortem comprehensively. Every clause names a specific artifact or action; no vague filler.

5 / 5

Completeness

Both halves are explicit: 'Use when planning, running, or learning from chaos engineering experiments' plus an enumerated trigger list answers 'when', and the shipped-tools/reference/slash-command inventory answers 'what'. Third-person voice is used throughout ('Ships...', 'Composes...').

5 / 5

Trigger Term Quality

Trigger coverage is comprehensive and natural: 'chaos experiment', 'fault injection', 'gameday', 'resilience test', 'blast radius', 'steady state', 'abort criteria', plus the tool names users would actually say (Chaos Toolkit, Chaos Mesh, Litmus, Gremlin, AWS FIS) and 'any deliberate failure-injection question' as a catch-all. No common synonym is obviously missing.

5 / 5

Distinctiveness Conflict Risk

Chaos engineering is a clear niche with highly specific triggers (blast radius, steady state, abort criteria, named chaos tools), and the composition notes ('Composes with feature-flags-architect... and kubernetes-operator') further delimit it from adjacent skills. Minimal risk of triggering for the wrong skill.

5 / 5

Total

20

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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