dd_checks git:20260912.cdc9992A

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---
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-<startup>-<model>.md`.
Canonical chapter JSON audits remain internal.

## 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).
Render validated results with `json_to_markdown_table`; there is no synthesis
model call. Checklists, classification and statuses belong to
`config/dd_checks/`. `audit_response_schema.json` defines the fields and status
enum; `audit_instructions.md` defines the evidence policy.

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 table, reusing eligible chapter audits.

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 "<STARTUP>"'
```

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)