| name | contract-snapshot |
| description | Use when the user wants to compare the same handful of terms across N contracts side-by-side in a grid — what is the term, survival period, carveouts, and governing law in each of these 5 NDAs? Returns a row-per-document × column-per-question grid with citations per cell. Reference skill for the M3-C output_format - table mode; intended as a starting point for operators to fork and tune for their own contract types. |
| lq_ai | {"title":"Contract Snapshot","version":"1.0.0","author":"LegalQuants","tags":["contracts","due-diligence","tabular","snapshot"],"jurisdiction":"agnostic","trigger_examples":["compare the term length and governing law across these NDAs","show me a side-by-side of these 5 contracts on key terms","I want a grid of these contracts' material terms"],"output_format":"table","ensemble_verification":false,"minimum_inference_tier":2,"columns":[{"name":"Term","query":"What is the term length of this agreement? Quote the operative\nclause exactly and surface the duration in plain language\n(e.g., \"3 years from the Effective Date\"). If the agreement\nis open-ended or runs until termination, say so.\n"},{"name":"Survival","query":"What confidentiality or other obligations survive termination\nor expiration of this agreement? Quote the survival clause\nif one exists. If no survival period is specified, return\n\"not specified\" (do not infer a default).\n","ensemble_verification":true},{"name":"Carveouts","query":"What carveouts to the confidentiality obligation does this\nagreement permit (e.g., already-known information, independently\ndeveloped, required by law)? List each carveout as a bullet\nwith a quoted phrase from the agreement.\n"},{"name":"Governing Law","query":"What jurisdiction governs this agreement, and which courts\nhave venue for disputes? Quote the governing-law and\nforum-selection clauses verbatim.\n","minimum_inference_tier":3}]} |
Contract Snapshot
A reference skill for the M3-C output_format: table mode. Produces a side-by-side grid of the same questions across N contracts — the in-house lawyer's "compare clauses across N agreements" workflow. Each cell carries a citation back to the source document, and failed extractions render as not found rather than confidently-wrong text.
When this skill applies
Apply when the user wants to compare a small number of well-defined questions across a corpus of similar contracts:
- "What is the term, survival, and governing law across these 5 NDAs we're tracking?"
- "Pull out the payment terms, IP ownership, and termination triggers across these 10 MSAs."
- "For my Q3 portfolio review, I need a grid of these 30 vendor contracts' liability caps."
Do not apply this skill to:
- Single-document review — use the appropriate document-specific skill (
nda-review, msa-review-saas, etc.).
- Free-form chat against contracts — that's the regular Chat surface.
- Tasks where the questions aren't well-defined upfront — the column queries must be specific enough to extract from each row's source document; vague queries produce poor cells.
Inputs
The skill takes a set of documents (selected via the Tabular Review UI from a Knowledge Base, a Project, or a free file selection). The four columns above run as Citation Engine-grounded extractions against each document.
To adapt this skill for a different contract type (e.g., MSAs), fork the skill and rewrite the four column queries. Keep them short, specific, and quote-asking — the Citation Engine works best when the model is encouraged to quote rather than paraphrase.
Per-column overrides
This skill demonstrates the two per-column overrides M3-C1 supports:
ensemble_verification: true on the Survival column. Survival is the load-bearing economic term in confidentiality agreements (a 3-year confidentiality term with a 10-year survival is very different from one with no survival), so cells in this column run through Stage 4 of the Citation Engine cascade — three judges debating whether the cell value is faithful to its citation. Higher cost, higher confidence.minimum_inference_tier: 3 on the Governing Law column. The skill-level floor is Tier 2 (commercial inference). Governing-law extraction is the column most likely to surface counterintuitive answers (e.g., a contract drafted under California law but with a Delaware forum-selection clause); routing this column to Tier 3+ avoids the cheapest models' tendency to collapse the two into one answer.
Other columns inherit the skill-level ensemble_verification: false and minimum_inference_tier: 2 defaults — appropriate for the lower-stakes, more-extractive Term and Carveouts columns.
Output format and downstream surfaces
The grid renders in the Tabular Review UI (/lq-ai/tabular/) with sticky-first-row and sticky-first-column. Each cell shows the extracted value + a small confidence chip; click anywhere on the cell to open the existing M2-C2 citation drawer with the source document highlighted at the cited chunk.
From the result view, operators can:
- Export the grid as XLSX — each cell carries its citation as an Excel comment with a clickable link back to the deployment.
- Export as CSV — citations are flattened to sibling
{column_name}_citation_url columns.
- Run a bulk operation — e.g., "Redline the Survival column in all rows" runs the
nda-review skill against each row's source document with the survival value as context.
Disclaimer (per Decision F)
This skill is a starting point, not a vetted template. The four columns are appropriate for many NDAs but won't be right for every corpus. Before relying on the output of a Tabular Review run on this skill, the user-attorney should:
- Review the column queries — do they match the questions you actually want answered for this corpus?
- Spot-check at least one cell per column against the source document — does the extraction faithfully represent the source?
- Treat any
not found cell as a signal to investigate, not as definitive evidence that the clause is absent.
The output is a draft for an in-house lawyer to validate, not a final compliance artifact.
Fork and tune
This skill is intentionally minimal so operators can fork it as a starting point:
output_format: table
columns:
- name: Payment Terms
query: What are the payment terms (frequency, days-to-pay, late-fee provisions)?
- name: IP Ownership
query: Who owns IP created during the engagement (work-for-hire, license-back, joint)?
- name: Termination
query: List each termination right (for cause, for convenience, notice periods).
The Tabular UI accepts both saved skills (like this one) and ad-hoc column specs entered directly in the wizard's column step.
