obra/superpowers

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https://github.com/obra/superpowers.git · scanned 2026-05-15 23:05 UTC (2 weeks, 6 days ago) · 10 languages

11 findings (4 legacy + 7 scanner) 68th percentile · Javascript · tiny (<2K LoC) Scanner says 99 (lower by 22)

File as issue Image v2 schema

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 2 weeks, 6 days ago · v1 · 3 findings from 1 source. 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 2026-05-17-v4 calibration-aware

Component	Sub-score	Weight	Contribution
`structure_score`	75.0	0.15	11.25
`security_score`	98.9	0.25	24.73
`testing_score`	85.0	0.20	17.00
`documentation_score`	75.0	0.15	11.25
`practices_score`	40.0	0.15	6.00
`code_quality`	68.4	0.10	6.84
Overall		1.00	77.1

Calibrated penalty buckets (security_score): agent: 1.1

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 0 High 1 Medium 0 Low 1 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 3 9-layer 0 Crowd 0 Layer: Quality 3

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 2 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).

2 matching findings on this repo

medium Agent control bridge may listen on a network interface without visible auth skills/brainstorming/scripts/start-server.sh:12
low Multiple AI-agent scaffold marker files are present AGENTS.md:1

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

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