Reviewing a counterparty draft
Assesses how proposed clauses compare with relevant market practice. Highlights departures that merit attention and provides fallback wording to support a focused negotiation.
— Legal & Contracts
Clause Benchmarking
Researches the market-standard position for each clause type in your jurisdiction and sector, then grades every clause as at, above or below market with fallback wording proposed.
- Legal & Contracts
- Traceable reasoning
- Runs anywhere
1 day → ~15 minutes
Including the external research pass
No coding required
- Input
- Agreement text
- Output
- Clause-by-clause market benchmark table
- Runs in
- Skillsize · ChatGPT · Claude · Copilot
- Export
- SKILL.md · MCP
- Time saved
- ~1 day per run
— USE CASES
What people use Clause Benchmarking for
Advising on unfamiliar markets
Grounds the review in research specific to the stated jurisdiction, sector and contract type. Gives advisers a more relevant reference point than a generic view of standard terms.
Prioritising contract negotiation points
Distinguishes clauses that align with market practice from those that depart materially. Provides a benchmark assessment and proposed alternatives for provisions worth negotiating.
Assessing standard contract terms
Tests an existing agreement against researched market positions. Identifies provisions that may warrant revision and supplies fallback language for consideration.
What it works from
- Agreement text
- Applicable jurisdiction
- Relevant sector
- Contract type
What you get back
- Clause-by-clause market benchmark table
- Researched market positions scoped to jurisdiction, sector and contract type
- At-market, above-market or below-market clause assessments
- Descriptions of deviations from market practice reference points for negotiation priorities
— HOW IT BEHAVES
How Clause Benchmarking produces its result
The mechanics behind this specific template — what it reads, what it calculates, and where a human stays in the loop.
Live research on the contract portfolio
Current external sources on the contract portfolio are researched during the run rather than recalled from training data, and every source travels with the output.
Composed as work product
Findings on the contract portfolio are written up as a document that reads like professional output, with each claim tied back to a clause.
Contracts and agreements broken into reviewable clauses
Long material is split into individual clauses, so each one is assessed on its own merits instead of buried in a document-level verdict.
Delivered as a working table
The clauses land as a clean table you can sort, filter or drop straight into the deliverable.
Why this is expensive by hand
Contract reviews often flag legal risks without showing whether a provision reflects market practice or warrants negotiation. In practice it shows up as reviewing a counterparty draft: assesses how proposed clauses compare with relevant market practice. Highlights departures that merit attention and provides fallback wording to support a focused negotiation. The value sits in the rigour, not the typing — yet the rigour is exactly what gets traded away when there is only 1 day of capacity for it.
How this Skill produces it
Here the same job runs as a Skill. Your material goes in; clause-by-clause market benchmark table comes out, alongside researched market positions scoped to jurisdiction, sector and contract type. What sits between input and output is the codified method: thresholds, sequencing and the points where a human confirms a call — all of it visible and editable in the Skill. In effect, 1 day of senior time compresses into ~15 minutes — and the output is comparable across clients, quarters and colleagues instead of shaped by whoever ran it.
Who it's for
- In-house legal and contract managers
- Commercial and procurement leads reviewing terms
- Operations teams handling contract volume
- Consultants advising on contractual risk
Run it in Skillsize — or export it anywhere
Clause Benchmarking exports as a structured SKILL.md file and is MCP-ready, so the same method runs in ChatGPT, Claude, Copilot or your own AI products. Adapt it to your methodology, and the intelligence stays yours — not locked to one vendor.
ChatGPTClaudeCopilotAI Products (MCP)
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