D4Vinci/Scrapling

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97 of your 136 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Scan timing: clone 1.59s · analysis 51.53s · 4.1 MB · GitHub API rate-limit (preflight)

https://github.com/D4Vinci/Scrapling · scanned 2026-06-04 04:11 UTC (1 day, 6 hours ago) · 10 languages

265 findings (131 legacy + 134 scanner) 76th percentile · Python · medium (20-100K LoC) Scanner says 85 (lower by 8)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 day, 6 hours ago · v2 · 198 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`	75.0	0.15	11.25
`security_score`	49.7	0.25	12.43
`testing_score`	97.0	0.20	19.40
`documentation_score`	96.0	0.15	14.40
`practices_score`	99.0	0.15	14.85
`code_quality`	46.9	0.10	4.69
Overall		1.00	77.0

Severity distribution — click a segment to filter

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Severity: Critical 9 High 85 Medium 27 Low 61 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 131 9-layer 67 Crowd 0 Layer: Software 58 Cicd 22 Security 3 Quality 110 Api 1 Hardware 3 Frontend 1

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 82 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 Agent control bridge may listen on a network interface without visible auth scrapling/cli.py:153
medium Bare except continues silently scrapling/spiders/engine.py:210
medium Bare except continues silently scrapling/core/ai.py:307
medium Bare except continues silently scrapling/core/shell.py:363
medium Bare except continues silently tests/core/test_storage_core.py:213
medium Bare except continues silently cleanup.py:29
medium Bare except continues silently cleanup.py:37
low [COMP001] High cognitive complexity: Function `__str__` has cognitive complexit… scrapling/core/translator.py:36
low [COMP001] High cognitive complexity: Function `clean` has cognitive complexity … cleanup.py:6
low Duplicated implementation block across source files scrapling/fetchers/stealth_chrome.py:22
low Duplicated implementation block across source files scrapling/fetchers/stealth_chrome.py:20
low Duplicated implementation block across source files scrapling/engines/_browsers/_stealth.py:19

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Legacy markers 1 finding

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.

1 matching finding on this repo

low Legacy-named symbol `test_proxies_property_returns_copy` in tests/fetchers/test…

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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 scrapling/parser.py:254
info Commented-code block (5 lines) in scrapling/core/mixins.py:40

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Config drift 2 findings

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.

2 matching findings on this repo

high Phantom test coverage: test_start_requests_raises_when_nothing_configured tests/spiders/test_sitemap.py:173
low File has no detected symbols: scrapling/engines/_browsers/_config_tools.py

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