feat(cookbook): forensic-qoe — pre-LOI forensic QoE for private-company targets#199
feat(cookbook): forensic-qoe — pre-LOI forensic QoE for private-company targets#199aniebyl wants to merge 1 commit into
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…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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Hey @ololand-ai — did a mechanical pre-review pass on this PR (not a maintainer, just trying to clear the queue so Anthropic FSI's substantive review-time is spent on the right things). Two items worth addressing before someone with merge rights picks this up:
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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
document-readerRead,Greponlyupload_deal_documentonlyforensic-runner/ OrchestratorRead,Grep,Glob,Agentreport-writer(Write-holder)Read,Write,Editgenerate_forensic_screen_pdf+record_materialized_risksonlydocument-readerreturns 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
scripts/deploy-managed-agent.sh forensic-qoe --dry-runresolves the full manifest (orchestrator + 3 subagents) and posts a clean payload toPOST /v1/agentswithout errors.Test plan
app.ololand.ai/settings/api-keys, setOLOLAND_MCP_URL=https://api.ololand.ai/mcpplus anAuthorization: Bearer olo_agent_sk_*header, and runscripts/deploy-managed-agent.sh forensic-qoeagainst a sandbox deal.steering-examples.jsonand verify the PDF + JSON receipt land in./out/.record_materialized_riskswrite-back lands in OloLand's deal record.verify_sponsor_assumptionsraises 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