--- name: dd_checks description: Assess a startup data room against industry-aware due-diligence checklists and produce one complete report. Use for comprehensive checklist review. --- # DD checks Assess the configured due-diligence chapters and combine their findings. ## Inputs and outputs The async `dd_checks(startup)` returns one final Markdown report in `list[InsightFile]`, named `dd-checks--.md`. Canonical chapter JSON audits remain internal. The report states the industry type, then lists the most important findings under `## Most important findings`, then the complete chapter tables. `most_important_findings(report)` returns that section for [dd_priorities](../dd_priorities/SKILL.md), or `None` when a report, such as a manual one, has none. ## Workflow and dependencies Prepare the startup through `ensure_startup_dataset` and synchronize before assessment. Classify industry with the configured JSON schema; select each chapter's matching variant or its general fallback. Product has biology, hardware and software variants plus a general fallback. Run selected checklists concurrently through [the shared audit engine](../standards_and_architecture/SKILL.md#checklist-audits). Every check receives the audit instructions plus a startup context: the industry type and that industry's text from `config/dd_checks/industry_focus/`, which says what matters most for such startups. The context holds only these configured values, because a changing text would invalidate every cached chapter audit. Each check returns an importance from 1 to 10 next to its status. Render the most important findings with `ranked_checks_to_markdown_table`: checks with at least the configured minimum importance, highest first, up to the configured maximum count (`settings.json`). Equal values keep checklist order. Render the complete chapter tables with `json_to_markdown_table`; there is no synthesis model call. Checklists, classification, industry texts and statuses belong to `config/dd_checks/`. `audit_response_schema.json` defines the fields, status enum and importance range; `audit_instructions.md` defines the evidence policy and the importance scale. The registry declares `startup-profile` as a prerequisite. Direct DD neither requests nor consumes that profile. After synchronization, select a manual or reusable final report through `InsightFile.find(selection="reusable")`, using DD and structured-output configuration and the indexed dataset revision. Return it before industry classification or chapter audits. On a cache miss, classify industry and assemble the final report, reusing eligible chapter audits. Chapter audits include the industry context, so they are reused only when the classification matches the one they were made with. Industry generation delegates technical checks to its schema and uses a business reviewer to require evidence for a selected industry. Result processing only maps the accepted classification or applies the existing general fallback. The standalone text parser explicitly uses the shared repair and validation functions followed by the same business reviewer. ## Side effects and failure behavior Preparation may import Dealum documents; synchronization and per-check retrieval may convert and index data. Model calls and insight saves occur; there is no outreach. Missing evidence is an assessment outcome, distinct from technical failure. Wait for all chapter outcomes. Any chapter failure raises before a new final Markdown report is saved; successful chapter JSON artifacts can remain saved and reusable. A partial final report is not returned. ## Usage ```bash conda run -n sictic-env python -m skills.harness '/dd_checks ""' ``` The direct CLI uses `--startup`. ## References - [Implementation](dd_checks.py) - [Configuration and checklists](../../config/dd_checks/) - [Shared audit contract](../standards_and_architecture/SKILL.md#checklist-audits)