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quant-sentiment-ai/claude-equity-research

🔌 Claude Code Plugin for institutional-grade equity research. Install with /plugin marketplace add. Generates professional buy/sell recommendations with comprehensive fundamental analysis, technical indicators, and risk assessment. Educational use only - not financial advice.

  • View repository ↗
  • Python
  • ★ 759
  • commit 15dfe2b863e9
  • scored 2026-11-13
  • scoring prov-4+qual-2+sec-2

AI provenance

0%

explicit

Confidence
88%
Coverage
100%

Estimated share of AI-generated code. Explicit attribution is a lower bound.

Code quality

59/100

weak

Confidence
90%
Coverage
100%

Complexity, duplication, lint density, tests, CI, docs and review process.

Security

87/100

strong

Confidence
85%
Coverage
100%

Secrets, static analysis, vulnerable dependencies, CI and supply-chain posture.

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Scores

Pillar Score Signal Confidence Coverage
AI provenance (estimated AI-generated share) 0 explicit 88% 100%
Code quality 59 weak 90% 100%
Security 87 strong 85% 100%

AI provenance

Explicit AI attribution on 0% of first-party code added; an estimated 0% including velocity signals.

  • Explicitly attributed share (lower bound): 0.0%
  • Unattributed share in AI-consistent velocity bursts: 0.0%
  • Estimated AI-generated share: 0.0%
AI tool Attributed commits Lines added
Claude Code 7 0

Current source lines last changed by AI-attributed commits: 0 of 186.

Too few lines in one group to compare finding densities.

Month Lines added Attributed to AI
2026-08 186 0%

Evidence

  • Claude Code: trailer 'Co-Authored-By: Claude' (5a8495a50172)
  • Claude Code: trailer 'Co-Authored-By: Claude' (a64cc7a62d5b)
  • Claude Code: trailer 'Co-Authored-By: Claude' (01998cc49268)
  • Claude Code: trailer 'Co-Authored-By: Claude' (f67dad324935)
  • Claude Code: trailer 'Co-Authored-By: Claude' (42623b60364e)
  • Claude Code: trailer 'Co-Authored-By: Claude' (a0d6096ffe43)
  • Claude Code: trailer 'Co-Authored-By: Claude' (ac78e1bbc59d)

AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.

Code quality

Code quality is weak: test-to-source ratio 0.00, no CI test run detected; 0% of function code in complex or long functions; 0% duplicated lines. Weakest area: testing.

Area Score
Complexity (lizard) 100
Duplication 100
Code smells (Opengrep) 37
Tests and CI 15
Documentation 65
Type safety 40
Process (Scorecard review/maintenance) n/a
  • Functions: 7; mean complexity 3.6, 90th percentile 7; 0 complex and 0 long functions
  • Duplicated lines: 0.0% of 140 compared
  • Tests: 0 test lines for 186 source lines (ratio 0.00); CI test commands: none detected; 0% of recent source commits also change tests
  • Docs missing or thin: contributing, changelog

Security

Security posture is strong: 1 high-severity workflow issues. Weakest area: posture.

Area Score
Secrets (gitleaks, tree + history) 100
Static analysis (Opengrep) 100
Vulnerable dependencies (OSV) n/a
CI workflows (zizmor) 80
Posture (policy, updates, signing, lockfiles) 40
OpenSSF Scorecard n/a
  • Posture checks passed: security policy, lockfiles; missing: dependency updates, signed commits

Top findings

Severity Finding Location
high zizmor/unpinned-uses: unpinned action reference [confidence high] .github/workflows/star-history.yml:31
medium zizmor/artipacked: credential persistence through GitHub Actions artifacts [confidence low] .github/workflows/star-history.yml:31

Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.

Repository profile

  • Source lines: 186; test lines: 0
Language Lines
Python 186

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