Repository Analysis

anthropics/claude-for-legal

A suite of plugins for legal workflows

13.5 Low AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of anthropics/claude-for-legal, a Python project with 8,759 GitHub stars. SynthScan v2.0 examined 49,436 lines of code across 298 source files, recording 390 pattern matches distributed across 7 syntactic categories. The overall adjusted score of 13.5 places this repository in the Low AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

13.5
Adjusted Score
13.5
Raw Score
100%
Time Factor
2026-07-07
Last Push
8.8K
Stars
Python
Language
49.4K
Lines of Code
298
Files
390
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 1MEDIUM 40LOW 349

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 390 distinct pattern matches across 7 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Structural Annotation Overuse336 hits · 540 pts
SeverityFileLineSnippetContext
LOWlaw-student/skills/exam-forecast/SKILL.md47### Step 1: IntakeCOMMENT
LOWlaw-student/skills/exam-forecast/SKILL.md58### Step 2: Read each past examCOMMENT
LOWlaw-student/skills/exam-forecast/SKILL.md70### Step 3: Cross-exam pattern analysisCOMMENT
LOWlaw-student/skills/exam-forecast/SKILL.md88### Step 4: Forecast for the upcoming examCOMMENT
LOWlaw-student/skills/exam-forecast/SKILL.md144### Step 5: Output locationCOMMENT
LOWlaw-student/skills/legal-writing/SKILL.md49### Step 1: Read the whole draftCOMMENT
LOWlaw-student/skills/legal-writing/SKILL.md53### Step 2: Identify the structural typeCOMMENT
LOWlaw-student/skills/legal-writing/SKILL.md62### Step 3: Structured feedback (no rewriting)COMMENT
LOWlaw-student/skills/legal-writing/SKILL.md125### Step 4: If the student asks you to rewriteCOMMENT
LOWlaw-student/skills/legal-writing/SKILL.md136### Step 5: Track patternsCOMMENT
LOWlaw-student/skills/irac-practice/SKILL.md51### Step 1: Establish what we're gradingCOMMENT
LOWlaw-student/skills/irac-practice/SKILL.md60### Step 2: Read the answer closelyCOMMENT
LOWlaw-student/skills/irac-practice/SKILL.md70### Step 3: Structured feedbackCOMMENT
LOWlaw-student/skills/irac-practice/SKILL.md142### Step 4: Track patternsCOMMENT
LOWlaw-student/skills/socratic-drill/SKILL.md40### Step 1: Pick the topicCOMMENT
LOWlaw-student/skills/socratic-drill/SKILL.md44### Step 2: AskCOMMENT
LOWlaw-student/skills/socratic-drill/SKILL.md50### Step 3: Listen and push backCOMMENT
LOWlaw-student/skills/socratic-drill/SKILL.md75### Step 4: Only after they get thereCOMMENT
LOWlaw-student/skills/outline-builder/SKILL.md80### Step 1: InputsCOMMENT
LOWlaw-student/skills/outline-builder/SKILL.md89### Step 2: StructureCOMMENT
LOWlaw-student/skills/outline-builder/SKILL.md95### Step 3: Build — scaffold first, content from sourcesCOMMENT
LOWlaw-student/skills/outline-builder/SKILL.md133### Step 4: GapsCOMMENT
LOWlaw-student/skills/study-plan/SKILL.md53### Step 1: What are we planning forCOMMENT
LOWlaw-student/skills/study-plan/SKILL.md65### Step 2: Inputs — one at a time, wait for eachCOMMENT
LOWlaw-student/skills/study-plan/SKILL.md109### Step 3: Build the scheduleCOMMENT
LOWlaw-student/skills/study-plan/SKILL.md127### Step 4: Write itCOMMENT
LOWlaw-student/skills/study-plan/SKILL.md189### Step 5: Confirm with the studentCOMMENT
LOWlaw-student/skills/cold-call-prep/SKILL.md49### Step 1: Identify the reading + professorCOMMENT
LOWlaw-student/skills/cold-call-prep/SKILL.md56### Step 2: Predict the questionsCOMMENT
LOWlaw-student/skills/cold-call-prep/SKILL.md91### Step 3: DrillCOMMENT
LOWlaw-student/skills/cold-call-prep/SKILL.md102### Step 4: Post-drill summaryCOMMENT
LOWemployment-legal/skills/termination-review/SKILL.md46### Step 1: The basic factsCOMMENT
LOWemployment-legal/skills/termination-review/SKILL.md57### Step 2: High-risk flag scanCOMMENT
LOWemployment-legal/skills/termination-review/SKILL.md116### Step 3: Jurisdiction-specific requirementsCOMMENT
LOWemployment-legal/skills/termination-review/SKILL.md139### Step 4: Severance and releaseCOMMENT
LOWemployment-legal/skills/termination-review/SKILL.md166### Step 5: Documentation checkCOMMENT
LOWemployment-legal/skills/wage-hour-qa/SKILL.md44### Step 1: JurisdictionCOMMENT
LOWemployment-legal/skills/wage-hour-qa/SKILL.md52### Step 2: Research the rule, then state itCOMMENT
LOWemployment-legal/skills/wage-hour-qa/SKILL.md166### Step 3: The flagCOMMENT
LOWemployment-legal/skills/handbook-updates/SKILL.md28### Step 1: Get the changeCOMMENT
LOWemployment-legal/skills/handbook-updates/SKILL.md34### Step 2: Diff against currentCOMMENT
LOWemployment-legal/skills/handbook-updates/SKILL.md43### Step 3: Find cross-referencesCOMMENT
LOWemployment-legal/skills/handbook-updates/SKILL.md53### Step 4: State supplement impactCOMMENT
LOWemployment-legal/skills/handbook-updates/SKILL.md61### Step 5: Promise checkCOMMENT
LOWemployment-legal/skills/policy-drafting/SKILL.md36### Step 1: Scope the policyCOMMENT
LOWemployment-legal/skills/policy-drafting/SKILL.md42### Step 2: Jurisdictional scanCOMMENT
LOWemployment-legal/skills/policy-drafting/SKILL.md60### Step 3: Draft the core policyCOMMENT
LOWemployment-legal/skills/policy-drafting/SKILL.md73### Step 4: State supplementsCOMMENT
LOWemployment-legal/skills/policy-drafting/SKILL.md88### Step 5: Cross-checkCOMMENT
LOWemployment-legal/skills/hiring-review/SKILL.md46### Step 1: JurisdictionCOMMENT
LOWemployment-legal/skills/hiring-review/SKILL.md58### Step 2: ClassificationCOMMENT
LOWemployment-legal/skills/hiring-review/SKILL.md76### Step 3: Restrictive covenantsCOMMENT
LOWemployment-legal/skills/hiring-review/SKILL.md104### Step 4: Jurisdiction-specific requirementsCOMMENT
LOWemployment-legal/skills/hiring-review/SKILL.md123### Step 5: Offer letter contentCOMMENT
LOWip-legal/skills/takedown/SKILL.md81### Step 1: Identify the copyrighted workCOMMENT
LOWip-legal/skills/takedown/SKILL.md92### Step 2: Identify the infringing material and its locationCOMMENT
LOWip-legal/skills/takedown/SKILL.md103### Step 3: Fair-use gateCOMMENT
LOWip-legal/skills/takedown/SKILL.md120### Step 4: Good-faith beliefCOMMENT
LOWip-legal/skills/takedown/SKILL.md133### Step 5: Accuracy and agent authorityCOMMENT
LOWip-legal/skills/takedown/SKILL.md141### Step 6: Draft the noticeCOMMENT
276 more matches not shown…
Modern AI Meta-Vocabulary34 hits · 90 pts
SeverityFileLineSnippetContext
MEDIUMREADME.md153scripts/ # deploy-managed-agent.sh · validate.py · orchestrate.py · lint-tool-scope.py · test-cookbooksCODE
MEDIUMREADME.md202**Start by connecting a research tool.** Everything else is better with one, and citations are unverified without one. SCODE
MEDIUMCONTRIBUTING.md14## Design principle: SKILL.md encodes the right behavior; CLAUDE.md guardrailsCOMMENT
MEDIUMCLAUDE.md23scripts/ # validate.py, lint-tool-scope.py, orchestrate.py,CODE
MEDIUMlaw-student/CLAUDE.md116## Shared guardrailsCOMMENT
MEDIUMlaw-student/skills/case-brief/SKILL.md64## The brief — scaffold, then the student fillsCOMMENT
MEDIUMlaw-student/skills/outline-builder/SKILL.md95### Step 3: Build — scaffold first, content from sourcesCOMMENT
MEDIUMemployment-legal/CLAUDE.md141## Shared guardrailsCOMMENT
MEDIUMip-legal/CLAUDE.md152## Shared guardrailsCOMMENT
MEDIUMcommercial-legal/CLAUDE.md317## Shared guardrailsCOMMENT
MEDIUMcommercial-legal/skills/nda-review/SKILL.md20Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUMcommercial-legal/skills/stakeholder-summary/SKILL.md21Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUM…mmercial-legal/skills/vendor-agreement-review/SKILL.md21Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUMproduct-legal/CLAUDE.md152## Shared guardrailsCOMMENT
MEDIUMproduct-legal/skills/launch-review/SKILL.md34Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUMproduct-legal/skills/is-this-a-problem/SKILL.md33Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUMlegal-builder-hub/CLAUDE.md93## Shared guardrailsCOMMENT
MEDIUMlitigation-legal/CLAUDE.md180## Shared guardrailsCOMMENT
MEDIUMlitigation-legal/CLAUDE.md368The canonical column maps to the cross-plugin severity floor described in `## Shared guardrails` below.CODE
MEDIUMlitigation-legal/skills/deposition-prep/SKILL.md30Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUMlitigation-legal/skills/claim-chart/SKILL.md239## Patent-mode guardrails (in addition to shared guardrails)COMMENT
MEDIUMlitigation-legal/skills/claim-chart/SKILL.md335## Civil-mode guardrails (in addition to shared guardrails)COMMENT
MEDIUMlitigation-legal/skills/claim-chart/SKILL.md441## Shared guardrails — checklistCOMMENT
MEDIUMcorporate-legal/CLAUDE.md146## Shared guardrailsCOMMENT
MEDIUMai-governance-legal/CLAUDE.md303## Shared guardrailsCOMMENT
MEDIUMprivacy-legal/CLAUDE.md236## Shared guardrailsCOMMENT
MEDIUMprivacy-legal/skills/use-case-triage/SKILL.md36Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUMprivacy-legal/skills/pia-generation/SKILL.md42Before producing output, check where it's going. If the user has named a destination (a channel, a distribution list, a CODE
MEDIUMprivacy-legal/skills/pia-generation/SKILL.md67**Carry severity from upstream as a floor** per the cross-skill severity floor rule in `~/.claude/plugins/config/claude-CODE
MEDIUMprivacy-legal/skills/dpa-review/SKILL.md63**Carry severity from the upstream output as a floor** per the cross-skill severity floor rule in `~/.claude/plugins/conCODE
MEDIUMlegal-clinic/CLAUDE.md239## Shared guardrailsCOMMENT
MEDIUMlegal-clinic/skills/client-intake/SKILL.md174*Every statutory, ordinance, regulatory, rule, or case citation in this section carries a provenance tag (see plugin CLACOMMENT
MEDIUMregulatory-legal/CLAUDE.md173## Shared guardrailsCOMMENT
MEDIUMregulatory-legal/skills/policy-redraft/SKILL.md31## Hard guardrails — read these firstCOMMENT
Decorative Section Separators6 hits · 18 pts
SeverityFileLineSnippetContext
MEDIUMlitigation-legal/matters/_log.yaml9# ──────────────────────────────────────────────────────────────────────────COMMENT
MEDIUMlitigation-legal/matters/_log.yaml11# ──────────────────────────────────────────────────────────────────────────COMMENT
MEDIUMlitigation-legal/matters/_log.yaml62# ──────────────────────────────────────────────────────────────────────────COMMENT
MEDIUMlegal-clinic/deadlines.yaml10# ──────────────────────────────────────────────────────────────────────────COMMENT
MEDIUMlegal-clinic/deadlines.yaml12# ──────────────────────────────────────────────────────────────────────────COMMENT
MEDIUMlegal-clinic/deadlines.yaml30# ──────────────────────────────────────────────────────────────────────────COMMENT
Over-Commented Block11 hits · 11 pts
SeverityFileLineSnippetContext
LOW…kills/renewal-tracker/references/renewal-register.yaml1# Renewal RegisterCOMMENT
LOW…kills/renewal-tracker/references/renewal-register.yaml21# business_owner: "jane@company.com"COMMENT
LOWlegal-builder-hub/references/allowlist-default.yaml1# Default allowlist for legal-builder-hubCOMMENT
LOWlitigation-legal/matters/_log.yaml1# Portfolio Ledger — litigation-legalCOMMENT
LOWlitigation-legal/matters/_log.yaml21# stage: string — free-text substage (e.g., "pleadings", "fact discovery")COMMENT
LOWlitigation-legal/matters/_log.yaml41# business_lead: string | nullCOMMENT
LOWscripts/deploy-managed-agent.sh1#!/usr/bin/env bashCOMMENT
LOW.github/workflows/cla.yaml21 if: github.event_name == 'pull_request_target' || contains(github.event.comment.body, 'recheck') || contains(gitCOMMENT
LOWlegal-clinic/deadlines.yaml1# Clinic deadline ledger — legal-clinicCOMMENT
LOWlegal-clinic/deadlines.yaml21# timezone: string (optional; default from CLAUDE.md jurisdiction)COMMENT
LOW…-legal/skills/gap-surfacer/references/gap-tracker.yaml1# Gap TrackerCOMMENT
AI Response Leakage1 hit · 5 pts
SeverityFileLineSnippetContext
HIGHai-governance-legal/skills/use-case-triage/SKILL.md148The APPROVED / CONDITIONAL / NOT APPROVED buckets, the red-line definitions, and the CONDITIONAL required-controls list CODE
Fake / Example Data1 hit · 1 pts
SeverityFileLineSnippetContext
LOWlitigation-legal/skills/matter-intake/SKILL.md251 counterparty: "Acme Corp"CODE
Unused Imports1 hit · 1 pts
SeverityFileLineSnippetContext
LOWscripts/lint-tool-scope.py20CODE