--- name: cla_review description: Review one convertible-loan term sheet from the lender's side, against the SECA CLA model term sheets and in the context of the startup's cap table, existing loans and SHA. Use before SICTIC members lend. --- # CLA term sheet review Identify the term sheet, extract its terms, judge them for a lender and show what they do in this company. ## Inputs and outputs The async `cla_review(dataset_name, *, document=None, ticket=None, fresh=False)` returns one Markdown report in `list[InsightFile]`, named `cla-review---.md`, one per reviewed document. JSON intermediates (`identification`, `extraction`, `assessment`, `conversion`, `loan-context`, `sha-context`, `audits/`) live under `insights/cla-review/` and carry the document identity in their identifier. `document` names the data-room path of the term sheet; omitted, the model selects the most recent CLA term sheet and executed loans are context, not candidates. `ticket` is the member's own amount and is part of the configuration key, so a new ticket replaces the generated result for that document. `fresh` bypasses generated reuse without deleting files or overriding manual inputs. **Slices:** slice 0 (contract, rules, references, fixture) and slice 1 (question 1: identify, extract, company- and lender-angle assessment, deterministic report) are built; the audits against the SECA term sheets, the conversion, the existing loans, the SHA context and the synthesis are listed in the report as pending. The slices are defined in [docs/cla-review-design.md](../../docs/cla-review-design.md#7-slices). Artifact identity is the slugified full relative path of the document, so equal basenames in different folders stay distinct. The automatic identification is one dataset-level artifact (`identification-automatic`); an explicit `document` gets its own (`-identification`), so the two never reuse each other. The report is `cla-review---.md` directly under `insights/`. ## Workflow and dependencies Prepare and synchronize the startup. Identify the term sheet: an explicit `document` is resolved through the shared document-path resolution at the configured minimum score; otherwise the model selects the most recent CLA term sheet or draft from three retrieval queries, executed loans being context and never candidates, and only the selected path is resolved. An existing `captable_build` classification, when present, seeds candidates and exclusions and is part of the identification key. Extract the selected document with the shared `captable_build` CLA checklist (`config/captable_build/cla_terms.md`, including its term-sheet provisions group); every value carries a verbatim quote and every absence a `missing_terms` entry. Assess it twice: `assess_cla` with the `captable_build` bands (company angle) and `lib.cla_review.assessment` with `config/cla_review/settings.json` (lender angle). Every lender rule carries value, source, status, effective date and an `active` flag; only `approved` rules may be active and judge, an inactive rule yields an open question with the observation, a rule whose input is unavailable is not evaluated and names the input. The term in months is measured from the run date, which is part of the assessment key. Each stage is a managed JSON artifact validated on read and write against `config/cla_review/artifact_schemas.json` or the `captable_build` CLA schema; a stage reuses its artifact when the configuration and the upstream artifact are unchanged. Manual files win, `fresh` bypasses generated reuse only, failures are never saved. `ticket` is part of the report key so a new ticket regenerates the report for the same document without regenerating the extraction. Later slices consume the consolidated `captable_build` insight through `lib.captable.insights.select_consolidated`; it is never generated implicitly and the registry declares no prerequisite. ## Side effects and failure behavior Preparation may import, convert and index documents. The review calls models and saves intermediates and the report; it sends nothing. An absent or stale cap-table snapshot yields insufficient-evidence findings for question 2; malformed artifacts, failed path resolution and technical audit failures remain errors. No plausible term sheet, an unresolvable path or an extraction failure stops the run without saving a partial artifact. The report aids human review and is not legal advice. ## Usage ```bash conda run -n sictic-env python -m skills.harness '/cla_review ""' conda run -n sictic-env python -m skills.harness -- /cla_review "" --document "legal/cla-term-sheet.pdf" --ticket 25000 conda run -n sictic-env python -m skills.cla_review --startup "" --document "legal/cla-term-sheet.pdf" --ticket 25000 --fresh ``` The direct CLI uses `--startup`; `--dataset` and `-d` remain compatibility aliases. ## References - [Implementation](cla_review.py) - [Design and slices](../../docs/cla-review-design.md) - [Configuration, rules and SECA reference term sheets](../../config/cla_review/) - [Shared CLA extraction checklist](../../config/captable_build/cla_terms.md) - [Shared audit contract](../standards_and_architecture/SKILL.md#checklist-audits)