بنقرة واحدة
analyze-dataroom
Use when a PE professional needs a fast diligence pass over a
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Use when a PE professional needs a fast diligence pass over a
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when the user wants a recorded video walkthrough of the Decision- Optimization Diagnostic (DX) upload UI — drop CSVs, watch the pipeline run, see the report rendered. Drives the local pe-mcp-web app via Playwright with caption overlays at each step, records to .webm, and prints a one-line ffmpeg command to convert to MP4. Reproducible, scripted, no manual screen capture required.
Use when a PE shop or portco needs to audit a deployed AI-agent fleet — inventory every registered agent, flag zombies (idle too long), runaway-cost agents (modeled monthly spend over a threshold), and misaligned agents (eval rubric fail), and produce a board-defendable pruning recommendation list with annual savings if the prunes land. Tackles the 40%-of-agentic- projects-cancelled-by-2027 risk Gartner is forecasting, and the agent-sprawl problem mega-funds (Vista, Thoma Bravo) face once they deploy AI agents at portco scale. Pure deterministic — no LLM call inside the tool, modeled telemetry stamped as modeled.
Use when an operating partner wants to surface cross-portco
Use when an LP has sent the GP an AI-section DDQ (Due-Diligence Questionnaire) — typically the new ILPA v2.0 (Q1 2026) AI governance / data / risk sections — and the GP needs a first-draft response packet built deterministically from the fund's existing AI-evidence artifacts in finance_output/, with every answer citing its source and a cross-answer consistency layer flagging contradictions before the LP does.
Use when a portco is post-close and the operating partner needs
Use when a PE professional needs an EU AI Act (Regulation 2024/1689)
| name | analyze-dataroom |
| description | Use when a PE professional needs a fast diligence pass over a |
You do not fabricate flags. Every entry traces to a regex match against a specific section + paragraph. The eight extractors are deterministic; running the tool twice on the same document produces identical output.
| Flag family | Signal | Severity rule |
|---|---|---|
customer_concentration | "X% of revenues / customers / portfolio" with X ≥ 10 | high if ≥25%, medium if ≥15%, else low |
going_concern | "substantial doubt about the company's ability to continue as a going concern" | always high |
material_weakness | "material weakness in internal control" (with negation skip) | high in Item 9A, medium elsewhere |
goodwill_impairment | "goodwill impairment charge" (with negation skip) | always high |
auditor_change | "dismissed / changed independent registered public accountant" | medium |
related_party | "related-party transactions" (in Item 13 or 1A) | low |
restatement | "restatement of prior-period / previously issued financials" | always high |
severe_risk_factor | Item 1A paragraphs with ≥3 hits of severity language ("material", "substantial", "adverse") | medium (3-4 hits), high (≥5 hits) |
cim_analyze(
ticker: str | None = None, # e.g. "SHC", "BOWL", "DNUT"
local_html_path: str | None = None,
form: str = "10-K", # or "S-1", "10-Q", "8-K", etc.
output_filename: str | None = None,
) -> dict
Returns:
{
"report_path": "/abs/path/to/cim_redflags_<base>.html",
"json_path": "/abs/path/to/cim_redflags_<base>.json",
"n_flags": int,
"by_severity": {"high": int, "medium": int, "low": int},
"by_type": {<flag_type>: int, ...},
"source_url": str, # SEC EDGAR URL or file:// path
"company_name": str,
}
form type, downloads the
primary HTML document, and analyzes. This requires network.ticker=....local_html_path=....Default to "10-K". Other useful forms:
"10-Q" for quarterly (less risk-factor depth, more MD&A freshness)"S-1" for IPO prospectus (closer to a CIM in audience + structure)"8-K" for material event disclosures"S-4" for M&A registration statementscim_analyze(ticker="SHC", form="10-K")
Report back:
| Failure | Diagnosis | Fix |
|---|---|---|
Ticker not found in SEC EDGAR registry | Bad ticker or company is foreign-listed only | Try the company's full name, or fetch by CIK directly |
No 10-K filing found in recent submissions | Company hasn't filed a 10-K (e.g. just IPO'd, only S-1 exists) | Try form="S-1" or form="10-Q" |
No standard 10-K/S-1 sections found | Wrong document type or malformed HTML | Confirm the URL — should be the primary .htm doc, not an exhibit |
| 0 flags surfaced | Either a very clean filing OR the parser missed sections | Manually inspect a few Item 1A paragraphs — if rich, re-check the parser |
When you finish, return to the user:
{n} flags · {high} high · {medium} medium · {low} low.Do not paste the full flag list back into the chat. The HTML is the artifact; the chat output is the pointer.
User: "Run /analyze-dataroom on SHC"
Agent calls cim_analyze(ticker="SHC", form="10-K").
Agent replies:
Diligence red-flag report rendered:
finance_output/cim_redflags_SHC.html· Sotera Health Co (CIK 1822479) · 10-K filed 2026-02-2449 flags · 16 high · 32 medium · 1 low Top finding: 7 material-weakness disclosures across Items 1A and 9A — a high rate worth confirming with the auditor's remediation timing.
Source: https://www.sec.gov/Archives/edgar/data/1822479/.../shc-20251231.htm