code-complexity
Skill1 starsChanged 29 days agoMITView on GitHub
Instructions for upstream Policy A1 analysis: cyclomatic complexity above 10 or function length above 50 AST statements, with five policy finding fields. Use to plan Python complexity review and interpret evidence. Not for duplication (code-duplication), global state/import cycles (code-state) or passthrough functions (code-wrappers).
2 other projects use a version of this.
Adopted by
Most recently taken up by
- AnastasiyaW/codex-claude-code-configtheir own version, first seen Oct 30, 2026
- AnastasiyaW/claude-code-configtheir own version, first seen Oct 8, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- AnastasiyaW/claude-code-config★ 214same name
- AnastasiyaW/codex-claude-code-config★ 214same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository114 other files it gives its agents.
- agent-cacheSkill
- agent-compressionSkill
- agent-intelSkill
- agent-overlapSkill
- agent-stateSkill
- ambient-hudSkill
- app-testSkill
- audit-dagSkill
- auto-distillSkill
- auto-memorySkill
- auto-routerSkill
- benchSkill
- bootstrapSkill
- botte-learnSkill
- botte-nnSkill
- botte-proxySkill
- botte-wrapSkill
- cacheSkill
- call-chainsSkill
- capabilitiesSkill
- cardinalSkill
- checkupSkill
- clarificationSkill
- clusterSkill
- code-duplicationSkill
- code-fingerprintSkill
- code-rulesSkill
- code-stateSkill
- code-wrappersSkill
- comfyuiSkill
- completion-proofSkill
- conductorSkill
- context-budgetSkill
- context-compressorSkill
- context-profilerSkill
- context-slicerSkill
- context-windowsSkill
- control-loopSkill
- cost-estimatorSkill
- dag-optimizerSkill
- dashboardSkill
- decision-ladderSkill
- demoSkill
- diff-languageSkill
- directives-auditSkill
- docgenSkill
- docs-stewardSkill
- dynamic-workflowsSkill
- efficiencySkill
- eventsSkill
- fallow-likeSkill
- fallowSkill
- fast-contextSkill
- fixSkill
- fleetSkill
- hailo-visionSkill
- harness-deltaSkill
- harvestSkill
- hermes-bridgeSkill
- hermes-second-brainSkill
- The first 60 shown.
The file
Files
code-complexity — anomaly A1 detector
Package scope and prerequisites
This catalog ships instructions, not the executable runtime. Python modules, scripts, local services and optional tools named below must be installed separately from the upstream project. Check their availability before running commands; if they are missing, explain the prerequisite instead of claiming execution or results. The host agent may use model tokens while following these instructions.
Commands, APIs and runtime behavior described below are conditional examples for a compatible upstream installation; they are not evidence of execution in this package.
When NOT to use
- Two function bodies look like copies of each other — use code_duplication.
- The smell is global mutable state or a circular import — use code_state.
- The smell is a function that only forwards its arguments — use code_wrappers.
The upstream detector is described by A1 of the upstream docs/local-analysis-policy.md (not bundled here): cyclomatic
complexity (1 + branch points: if/elif, loops, except, ternaries, boolean
operators, comprehension guards, asserts, match cases) and function length
(AST statements, nested scopes excluded and reported separately).
python -m skills.code_complexity.cli [path] # human-readable report
python -m skills.code_complexity.cli skills --json # compact JSON
python -m skills.code_complexity.test_code_complexityReports ONLY anomalies (at least one threshold exceeded). The documented upstream mode is report-only; verify the installed command exit status and CI integration. Pairs with future detectors of the same policy (duplication A6, wrappers A3, …) which will share the five-field finding shape.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

