awesome-codex-skills

Public scan — anyone with this URL can view this analysis. Sign up to track your own repos privately, run scheduled re-scans, and get AI fix prompts via your dashboard.

https://github.com/ComposioHQ/awesome-codex-skills.git · scanned 2026-05-16 02:44 UTC (2 weeks, 6 days ago) · 10 languages

58 findings (16 legacy + 42 scanner) 16th percentile · Python · small (2-20K LoC) Scanner says 99 (lower by 49)

File as issue Image

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 2 weeks, 5 days ago · v1 · 8 findings from 1 source. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

JSON

Score breakdown â 2026-05-17-v4 calibration-aware

Component	Sub-score	Weight	Contribution
`structure_score`	40.0	0.15	6.00
`security_score`	96.0	0.25	24.00
`testing_score`	0.0	0.20	0.00
`documentation_score`	63.0	0.15	9.45
`practices_score`	30.0	0.15	4.50
`code_quality`	69.0	0.10	6.90
Overall		1.00	50.8

Calibrated penalty buckets (security_score): agent: 1.1 Â· threat: 3.0

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 0 High 1 Medium 4 Low 1 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 8 9-layer 0 Crowd 0 Layer: Security 2 Software 1 Quality 5

Exclude dismissed / FP Exclude test files

Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

Fragile runtime 1 finding

What it is: Code that runs but breaks under predictable input — division by zero, missing keys, unbounded loops, off-by-one slicing.

Why it matters: Reaches production undetected because happy-path tests pass. First user with a weird input crashes the request.

How AI causes it: AI loves writing the happy path; doesn't probe edge cases unless explicitly asked.

Fix approach: Add property-based tests. Wrap external inputs with explicit validators. Use the framework's typed deserializer (Pydantic, attrs).

1 matching finding on this repo

medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … slack-gif-creator/core/frame_composer.py:310

View all fragile runtime findings →

Config drift 1 finding

What it is: Settings duplicated across env files, Docker compose, K8s, and code defaults, all with slightly different values.

Why it matters: Production behaviour depends on whichever copy your loader reads first. Subtle bugs in staging that don't reproduce in dev.

How AI causes it: AI writes new config from memory rather than reading the existing source.

Fix approach: Pick one source of truth (env vars + a settings module). Have every other place import from there. Lint for duplicates in CI.

1 matching finding on this repo

medium No CI/CD configuration found

View all config drift findings →

For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.

For AI agents + API integrations

Email me when this repo regresses

Free. We re-scan periodically; new criticals → your inbox. No signup required for the scan itself.

API access

This page is publicly accessible at: https://repobility.com/scan/28eb09ce-accb-4997-acbd-105126a9e802/

To check status programmatically (no auth required):

curl -s https://repobility.com/api/v1/public/scan/28eb09ce-accb-4997-acbd-105126a9e802/

Important — please don't re-submit the same URL repeatedly. The submission endpoint is idempotent: re-submitting the same git URL returns this same scan_token, not a new one. To re-scan this repo, sign up free and use the dashboard.