dd_checks ยท diff
git:20260906.f9cef19 to git:20260912.cdc9992
13 added, 5 removed. Audit A to A.
---
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/`.
+ `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. Each invocation reruns classification and
- assembles the final table, reusing eligible chapter audits. The final report
- records DD, batch-audit and structured-output configuration, but is not selected
- through a final-output cache lookup or manual-output lookup.
+ 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)