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feat(cookbook): forensic-qoe — pre-LOI forensic QoE for private-company targets#199

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feat(cookbook): forensic-qoe — pre-LOI forensic QoE for private-company targets#199
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ololand-ai:cookbook/forensic-qoe

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@aniebyl aniebyl commented May 15, 2026

Summary

Adds a managed-agent cookbook that runs a pre-LOI Quality-of-Earnings forensic screen on private-company acquisition targets. Same workflow as the OloLand Cowork plugin ololand-forensic-qoe, exposed as a headless cookbook for enterprise teams running diligence inside their own orchestration layer (Temporal / Airflow / Guidewire).

The cookbook drives the OloLand MCP server, which exposes deterministic financial engines (Beneish M-Score, Benford's Law, EBITDA bridge, revenue-quality deep dive, working-capital analysis, journal-entry testing, lapping detection) plus a 246-category risk taxonomy and a cross-document reconciler with source-hierarchy discipline (CPA-audited > tax > management > AI-extracted).

Output: a 1-page IC-defensible PDF (./out/forensic-screen-<deal>.pdf) plus a structured JSON receipt with every adjustment cited to a specific source document, page, and section.

Positioning: stage-1 pre-LOI screen, not a Big-4 QoE replacement. Big-4 forensic QoE runs $150K-$500K and 4-8 weeks. This cookbook runs the same seven-primitive battery in ~72h and produces the IC-defensible artifact that decides whether to commit Big-4 spend.

Why this belongs in the repo

The existing 10 cookbooks cover analyst-workflow and operations (pitch, research, earnings, modeling, GL recon, KYC, valuation review, close, statement audit, meeting prep). Forensic QoE is the underwriting-defensibility surface — different JTBD (IC red-pen, not analyst draft), different buyer (investment committee), different artifact (IC-defensible PDF with enforced citation discipline). It complements the existing cookbooks rather than duplicating any of them.

Security & three-tier isolation

Tier Touches untrusted docs? Tools Connectors
document-reader Yes Read, Grep only OloLand upload_deal_document only
forensic-runner / Orchestrator No (reads through OloLand engines) Read, Grep, Glob, Agent OloLand (analyze + verify + read)
report-writer (Write-holder) No Read, Write, Edit OloLand generate_forensic_screen_pdf + record_materialized_risks only

document-reader returns length-capped, schema-validated JSON. Document contents are routed through OloLand's ingestion pipeline (Qdrant embeddings + cross-doc reconciler) rather than read by the orchestrator turn — prompt-injection inside a source PDF cannot reach the forensic-runner or the report-writer. If the cross-document reconciler raises a gap on a required metric (revenue / EBITDA / net debt / total debt), the cookbook halts before PDF generation — forensic QoE on top of an unresolved source-document disagreement is not defensible.

Validation

  • All 4 YAML manifests + steering-examples.json parse cleanly.
  • scripts/deploy-managed-agent.sh forensic-qoe --dry-run resolves the full manifest (orchestrator + 3 subagents) and posts a clean payload to POST /v1/agents without errors.

Test plan

  • Provision an OloLand agent key at app.ololand.ai/settings/api-keys, set OLOLAND_MCP_URL=https://api.ololand.ai/mcp plus an Authorization: Bearer olo_agent_sk_* header, and run scripts/deploy-managed-agent.sh forensic-qoe against a sandbox deal.
  • Steer with one of the events in steering-examples.json and verify the PDF + JSON receipt land in ./out/.
  • Verify record_materialized_risks write-back lands in OloLand's deal record.
  • Confirm the cookbook halts cleanly when verify_sponsor_assumptions raises a reconciliation gap.

Strategic context

Anthropic ships the policy gradient (the 10 finance agents + JV + MCP connector grid); OloLand ships the verifier stack (deterministic financial engines + risk taxonomy + cross-document reconciliation + persistent deal record). This cookbook is the wedge that lets the JV's enterprise customers invoke OloLand's IC-defensible forensic screen from inside an Anthropic-managed agent run — without leaving the Claude Platform.

If the maintainers prefer this live under plugins/partner-built/ololand/ rather than alongside the first-party cookbooks, happy to move it — let us know.

🤖 Generated with Claude Code

…ny targets

Adds a managed-agent cookbook that runs a pre-LOI Quality-of-Earnings
forensic screen on private-company acquisition targets. Calls the
OloLand MCP server (https://api.ololand.ai/mcp), which exposes the
seven-primitive forensic battery (Beneish M-Score, Benford's Law,
EBITDA bridge, revenue-quality deep dive, working-capital analysis,
journal-entry testing, lapping detection) plus a 246-category risk
taxonomy and a cross-document reconciler with source hierarchy
(CPA-audited > tax > management > AI-extracted).

Output: a 1-page IC-defensible PDF (`./out/forensic-screen-<deal>.pdf`)
plus a structured JSON receipt with every adjustment cited to source.

Positioning: a stage-1 pre-LOI screen, not a Big-4 QoE replacement.
Big-4 forensic QoE runs $150K-$500K and 4-8 weeks; this cookbook runs
the same primitive battery in ~72h and produces the IC-defensible
artifact that decides whether to commit the Big-4 spend.

Security: three-tier isolation per the cookbook convention.
`document-reader` is the only worker that touches untrusted source
documents and returns length-capped, schema-validated JSON.
`forensic-runner` orchestrates the OloLand MCP calls without seeing
raw document contents. `report-writer` is the only leaf with Write,
producing the PDF and JSON receipt from the structured findings.
If the cross-document reconciler raises a gap on a required metric
(revenue, EBITDA, net debt, total debt), the cookbook halts before
PDF generation — forensic QoE on top of an unresolved discrepancy
is not defensible.

Same workflow as the OloLand Cowork plugin `ololand-forensic-qoe`
(github.com/ololand-ai/ololand-plugins) — this cookbook is the
headless / overnight automation surface for enterprise teams running
forensic screens inside their own orchestration layer (Temporal /
Airflow / Guidewire).

Validation:
- `scripts/deploy-managed-agent.sh forensic-qoe --dry-run` resolves
  the manifest and posts a clean payload to POST /v1/agents.
- All 4 YAML manifests and the steering-examples.json parse cleanly.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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