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AC nil-contract-analysis-samir-patel

NIL (Name, Image, and Likeness) contract analysis for NCAA student-athletes from the athlete's perspective. Use when user says 'review this NIL contract', 'analyze this NIL deal', 'check this athlete agreement', 'review my NIL agreement', or uploads a PDF NIL contract for review. Identifies red flags, missing protections, and compliance issues. Produces a structured review memorandum with negotiation positions. Do NOT use for general contract review, employment agreements, non-NIL endorsements, or brand-side deal analysis.

lawve-ai/awesome-legal-skills Agent Skills author: lawve-ai 10 files body ≈ 3 270 tokens Open the sourcegithub.com↗ analyzed 75 min ago

NIL (Name, Image, and Likeness) contract analysis for NCAA student-athletes from the athlete's perspective.

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

AnalyzerMarketingSecuritySales and CRMtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
93
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (nil-contract-analysis-samir-patel) differs from the folder (nil-contract-reviewer-samir-patel)
    • 50When it triggers. No condition that starts the skill
    • 100Tools and files. No external tools needed
    • 100Steps. 62 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 3270 tokens
    • 100Running it twice. No mutating operations
    • low 10 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 528: enough signal without eating the budget
    • +4Structure: 29 headings
    • +3Step-by-step instructions: 62 items
    • +4Has examples (0 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.