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.
npx skills add Rockielab/rockie-codex --skill diligence-deckgit clone --depth 1 https://github.com/Rockielab/rockie-codexWrote 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.
[](https://agentmods.dev/skills/rockielab/rockie-codex/diligence-deck)<a href="https://agentmods.dev/skills/rockielab/rockie-codex/diligence-deck"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-codex/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.
<a href="https://agentmods.dev/skills/rockielab/rockie-codex/diligence-deck"><img src="https://agentmods.dev/badge/skills/rockielab/rockie-codex/diligence-deck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once 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 |
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 9d ago.
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.
This is a copy
100% identical to diligence-deck — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
What ships with it
26 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- .gitignore 185 B
- examples/dataroom-fixture/notebook_read.json 665 B
- examples/dataroom-fixture/source_read.source:cim01.json 1.8 KB
- examples/dataroom-fixture/source_read.source:contract01.json 2.1 KB
- examples/dataroom-fixture/source_read.source:cust01.json 1.9 KB
- examples/dataroom-fixture/source_read.source:fin01.json 2.2 KB
- examples/dataroom-fixture/source_read.source:q01.json 1.2 KB
- examples/sample-dataroom/cim-blurb.md 1.6 KB
- examples/sample-dataroom/contract-snippet.md 1.8 KB
- examples/sample-dataroom/customer-list.txt 1.6 KB
- examples/sample-dataroom/financial-summary.txt 1.9 KB
- examples/sample-dataroom/financials/Q-summary.txt 957 B
- examples/sample-findings.json 15 KB
- prompts/partner-critic.md 5.5 KB
- references/dd-methodology.md 6.5 KB
- references/declarative-titles.md 2.7 KB
- references/evidence-discipline.md 6.1 KB
- references/financial-quality.md 4.2 KB
- references/legal-checklist.md 4.2 KB
- references/partner-critic-rubric.md 3.9 KB
- scripts/battleground.py 43 KB runs code
- scripts/critic_loop.py 19 KB runs code
- scripts/fetch_dataroom.sh 10 KB runs code
- scripts/ingest_dataroom.sh 8.7 KB runs code
- scripts/reconcile.py 19 KB runs code
- scripts/render_deck.py 20 KB runs code
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.
- 9d ago First seen · 502 lines · 65 tokens per session scan A f0f96e2eece1
diligence-deck is a skill published in the GitHub repository Rockielab/rockie-codex (20 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. It is 100% identical to diligence-deck, differing in 0 lines, and is treated as a copy.
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