diligence-deck

diligence-deck is a skill for Claude Code from Rockielab/rockie-claude. It costs 65 tokens per session (6,921 once invoked), scanned A, original, Apache-2.0.

A workflow for examining a company before an acquisition, using documents in a private data room and relevant web research. It produces structured findings and an investment-committee deck, with every claim tied to a quoted source.

In plain words
What is it for?
Use it to ingest and reconcile data-room documents, research the target company, produce cited due-diligence findings, render a presentation, and run skeptical review cycles.
Why use it?
It reduces unsupported statements, invented numbers, and hard-to-check conclusions in acquisition reviews. An investment committee is the group that decides whether an investment should proceed.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the rockie-claude plugin — 29 skills, 1 MCP server shipped together

Good fit Use it to ingest and reconcile data-room documents, research the target company, produce cited due-diligence findings, render a presentation, and run skeptical review cycles.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rockielab/rockie-claude/diligence-deck
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Rockielab/rockie-claude --skill diligence-deck
Clone the repo
git clone --depth 1 https://github.com/Rockielab/rockie-claude

Made for: Claude Code.

Or install rockie-claude, the plugin that ships this one along with the rest of its 29 skills, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for diligence-deck

README.md
[![agentmods](https://agentmods.dev/badge/skills/rockielab/rockie-claude/diligence-deck/github.svg)](https://agentmods.dev/skills/rockielab/rockie-claude/diligence-deck)
Your own site
<a href="https://agentmods.dev/skills/rockielab/rockie-claude/diligence-deck"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/diligence-deck/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for diligence-deck

Your own site · 80×15
<a href="https://agentmods.dev/skills/rockielab/rockie-claude/diligence-deck"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-claude/diligence-deck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,921 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00065 $0.06921
Opus 5 $0.00032 $0.03460
Sonnet 5 $0.00013 $0.01384
Haiku 4.5 $0.00006 $0.00692

Measured 11d ago against content hash f0f96e2eece1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

diligence-deck scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/battleground.py, scripts/critic_loop.py, scripts/fetch_dataroom.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

project-harness/skills/diligence-deck/SKILL.md · 502 lines

How it starts

The opening of the file, as written. The whole thing — 502 lines — stays where its author put it; the contents beside it link to each section on GitHub.

diligence-deck — acquisition due-diligence findings for Atlas

Explicit goal

You are a senior consultant at a top-tier firm (EY-Parthenon / L.E.K. / Bain DD practice) producing acquisition due-diligence findings for an investment committee (IC). Your output must survive review by a skeptical senior partner who flags vague claims, unsupported numbers, paraphrased citations, hedges, and asymmetric depth as slop and sends it back. Every finding maps to a verbatim quote from a named data-room document or a web citation. Nothing is invented. Nothing is paraphrased into a number.

This skill covers A1 + A2 + A3 + A5: (A5 fetch) -> intake -> ingest -> reconcile -> research -> structured findings -> deck -> adversarial critic loop -> (A5 emit). A1 produces findings.json + findings.md; A2 ("Deck rendering") renders that typed contract into a partner-grade slide deck; A3 ("Adversarial critic loop") runs a fresh senior-partner critic until the run passes twice; A5 ("Connectors") wires the running Rockie lab's uploaded sources in as the data room and ships the deck back out as a downloadable artifact. One later slice extends it — do not attempt its work here:

  • A1b — swap the built-in research step for a bake-off-selected deep-research engine.

Pipeline

[1] INTAKE     — deal inputs (company, sector, ask price, thesis, prior
                 knowledge). Save to deal_inputs.md.
[2] INGEST     — scripts/ingest_dataroom.sh <dataroom> -> manifest.json
[3.5] RECONCILE — scripts/reconcile.py <manifest> -> reconcile.json.
                 Cross-document contradiction scan + missing-but-expected
                 fields. Runs BEFORE synthesis. Its deltas are first-class
                 inputs the findings MUST address.
[3] RESEARCH   — TWO separated sub-steps, never collapsed:
                 [3a] RETRIEVE: read manifest docs + web search; collect
                      verbatim evidence (quote + source) into an evidence
                      pool. The retriever does NOT write findings.
                 [3b] SYNTHESIZE: write each section ONLY from the evidence
                      pool. The synthesizer may not introduce a fact or a
                      number that is not already in the pool with a quote.
[4] FINDINGS   — emit findings.json (typed contract for A2) + findings.md.

Read the full file on GitHub · 502 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 502 lines · 65 tokens per session scan A f0f96e2eece1

Subscribe to this mod's changes

diligence-deck is a skill published in the GitHub repository Rockielab/rockie-claude (21 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 65 tokens to every session and 6,921 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.