Best use case
term-comparator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Compares term sheets against market standards, identifies outliers
Teams using term-comparator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/term-comparator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How term-comparator Compares
| Feature / Agent | term-comparator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Compares term sheets against market standards, identifies outliers
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Term Comparator ## Overview The Term Comparator skill analyzes term sheets against market standards and fund precedents to identify outliers and support negotiation strategy. It enables data-driven term negotiations and ensures consistency with market norms. ## Capabilities ### Market Standard Analysis - Compare terms against market benchmarks - Identify investor-favorable vs. founder-favorable terms - Track market term evolution over time - Analyze terms by stage and sector ### Precedent Comparison - Compare against fund's historical terms - Analyze co-investor standard terms - Review competitor term patterns - Build term precedent database ### Outlier Identification - Flag unusual or non-standard provisions - Identify terms outside normal ranges - Highlight aggressive or founder-unfriendly terms - Note missing standard protections ### Negotiation Support - Suggest negotiation priorities - Provide market data for negotiations - Identify trade-off opportunities - Model term package alternatives ## Usage ### Compare to Market ``` Input: Term sheet, market comparison parameters Process: Analyze against benchmarks Output: Market comparison report, variance analysis ``` ### Compare to Precedent ``` Input: Term sheet, precedent set Process: Compare against historical terms Output: Precedent comparison, consistency analysis ``` ### Identify Outliers ``` Input: Term sheet, outlier thresholds Process: Flag unusual terms Output: Outlier report, risk assessment ``` ### Support Negotiation ``` Input: Current terms, priorities Process: Analyze trade-offs, suggest approach Output: Negotiation strategy, talking points ``` ## Comparison Dimensions | Dimension | Market Range | Notes | |-----------|--------------|-------| | Liquidation Preference | 1x non-participating standard | Participating increasingly rare | | Anti-dilution | Broad-based weighted average | Full ratchet rare | | Board Composition | Investor minority or balanced | Depends on stage | | Protective Provisions | Standard set | Watch for unusual vetos | | Option Pool | 10-15% refresh | Check pre vs. post | ## Integration Points - **Term Sheet Drafting Process**: Validate generated terms - **Definitive Document Negotiation**: Support doc negotiations - **Term Sheet Generator**: Ensure generated terms are standard - **Term Sheet Negotiator (Agent)**: Support agent negotiations ## Market Data Sources - NVCA survey data - Fund precedent database - Co-investor term patterns - Legal firm market reports - Industry benchmarking services ## Best Practices 1. Maintain current market benchmark data 2. Consider stage and sector context 3. Weight precedent by recency 4. Distinguish must-haves from nice-to-haves 5. Consider full term package, not just individual terms
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