anthropics/skills

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70 of your 103 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Scan timing: clone 4.85s · analysis 6.22s · 10.3 MB · GitHub preflight 424ms

https://github.com/anthropics/skills · scanned 2026-06-05 04:46 UTC (12 hours, 47 minutes ago) · 10 languages

240 findings (96 legacy + 144 scanner) 26th percentile · Python · small (2-20K LoC) Scanner says 97 (lower by 38)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 12 hours, 47 minutes ago · v2 · 168 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

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Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	40.0	0.15	6.00
`security_score`	52.9	0.25	13.22
`testing_score`	70.0	0.20	14.00
`documentation_score`	62.0	0.15	9.30
`practices_score`	70.0	0.15	10.50
`code_quality`	60.0	0.10	6.00
Overall		1.00	59.0

Severity distribution — click a segment to filter

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Severity: Critical 1 High 66 Medium 23 Low 62 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 96 9-layer 72 Crowd 0 Layer: Software 41 Security 8 Quality 116 Api 1 Frontend 1 Cicd 1

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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 89 findings

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).

12 matching findings on this repo

medium [SEC134] AI scaffold leftover — Lorem ipsum / example.com / John Doe in code: L… skills/webapp-testing/examples/static_html_automa…:21
medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … skills/pptx/scripts/office/unpack.py:87
medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … skills/docx/scripts/office/validators/redlining.py:56
medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … skills/docx/scripts/office/unpack.py:87
medium [COMP001] High cognitive complexity: Function `_consolidate_text` has cognitive… skills/docx/scripts/office/helpers/merge_runs.py:178
medium [SEC136] AI-typical over-broad exception handler swallowing all errors: Catch-a… skills/docx/scripts/accept_changes.py:49
medium Bare except continues silently skills/pptx/scripts/office/validators/redlining.py:65
medium Bare except continues silently skills/pptx/scripts/office/validators/pptx.py:195
medium Bare except continues silently skills/pptx/scripts/office/unpack.py:78
medium Bare except continues silently skills/pptx/scripts/thumbnail.py:246
medium Bare except continues silently skills/pptx/scripts/thumbnail.py:90
medium Bare except continues silently skills/docx/scripts/office/helpers/simplify_redli…:43

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Legacy markers 3 findings

What it is: TODO, FIXME, XXX, HACK comments. Often indicate a known-broken path the author meant to fix.

Why it matters: Each marker is an unfinished thought. Production code shouldn't ship with debt that's documented but not tracked.

How AI causes it: AI mirrors the style of the codebase, so existing TODOs propagate into new code.

Fix approach: Convert each into a ticket. Delete the comment when the ticket lands. Use a pre-commit hook to block new TODOs without an issue link.

3 matching findings on this repo

low Legacy-named symbol `xml_copy` in skills/docx/scripts/office/validators/base.py…
low Legacy-named symbol `xml_copy` in skills/pptx/scripts/office/validators/base.py…
low Legacy-named symbol `xml_copy` in skills/xlsx/scripts/office/validators/base.py…

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Commented-out code 2 findings

What it is: Lines of source that were intentionally disabled but never deleted.

Why it matters: Git already remembers history — commented code rots, becomes wrong, and adds noise to diffs.

How AI causes it: AI sometimes comments out broken code instead of fixing it. Reviewers approve out of inertia.

Fix approach: Delete. Trust `git log`. If you really need to remember, save it in a notes file under `docs/`.

2 matching findings on this repo

info Commented-code block (5 lines) in skills/skill-creator/scripts/improve_descript…
info Commented-code block (8 lines) in skills/algorithmic-art/templates/generator_te…

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