Ddq Red Flag Scanner logo

Ddq Red Flag Scanner

Community
latour-ai
ddq-red-flag-scanner

Scan a completed DDQ or RFP response for red flags — vague or evasive answers, internal inconsistencies, missing standard disclosures, and answers that contradict the manager's deck or letters. For allocators, fund-of-funds analysts, OCIOs, and ODD teams. Trigger on phrases like "scan this DDQ," "red flags in this DDQ," "review these RFP responses," "anything off in this questionnaire," "check this DDQ against the deck," "what's missing from this DDQ," or when the user uploads a completed due diligence questionnaire or RFP response. Do NOT use for a general first pass on a manager — use the hedge-fund-diligence skill. Do NOT use for ongoing quarterly review of an existing manager. Do NOT use for NDAs or contracts — use the pe-nda-review skill.

Overview

Publisherlatour-ai
Repositoryskills
Skill nameddq-red-flag-scanner
Stars
69
Forks
4
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by latour-ai on GitHub. Read the source before you install it.

Installation

Install the Ddq Red Flag Scanner AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/latour-ai/skills/tree/main/ddq-red-flag-scanner
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/latour-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/ddq-red-flag-scanner .claude/skills/ddq-red-flag-scanner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ddq Red Flag Scanner in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ddq Red Flag Scanner on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ddq Red Flag Scanner is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

DDQ Red-Flag Scanner

Scan a completed DDQ or RFP response the way a skeptical ODD analyst reads it: not for what it says, but for what it avoids saying. Output a severity-ranked findings list where every finding quotes the exact language (or names the exact missing disclosure), explains why it matters, and gives the follow-up question to ask.

Why this skill exists

DDQs are written by the manager's IR team and counsel, and they are very good at producing answers that are technically responsive and substantively empty. The tells are systematic: passive voice around compliance history, "from time to time" hedges, valuation policies that never mention level-3 assets, key-person questions answered with org charts. A human reviewer skimming 80 pages misses these; a systematic scan against a checklist of standard disclosures doesn't. This skill is the systematic scan — it ranks what it finds so the human spends time on what matters.

Inputs

Required:

  • A completed DDQ or RFP response (PDF, document, or pasted text)

Optional (enables the cross-document consistency check — the highest-value finding type):

  • The manager's pitch deck, investor letters, tear sheet, or website copy
  • Form ADV (or tell the skill to pull the public ADV via your AI's web search)
  • A prior version of the same DDQ — enables a "what changed" diff
  • The user's own DDQ standard or pet questions

Missing-input behavior: The DDQ alone is enough to run. If no comparison materials are provided, run the scan and note that the cross-document consistency section was skipped. Don't ask for anything unless the DDQ itself is unreadable or clearly partial — in that case ask once which sections exist.

How it works

  1. Map the document. List the sections present and immediately note standard sections that are absent (see the disclosure checklist in step 4).
  2. Evasiveness pass. Flag answers that are vague, deflecting, or non-responsive. Tells: passive voice where an actor matters ("errors are addressed promptly" — by whom?), answers that restate the question, "please refer to" loops that never land, qualifiers like "generally," "typically," "from time to time," "we are not aware of," answers conspicuously shorter than neighboring ones, and boilerplate where specifics were asked.
  3. Internal consistency pass. Cross-check the DDQ against itself: headcount vs. team bios, AUM in one section vs. another, stated strategy vs. described risk limits, service providers named inconsistently, dates that don't line up.
  4. Missing-disclosure checklist. Verify the document substantively addresses, at minimum: regulatory exams/actions and material litigation; personal trading and code-of-ethics policy; valuation policy including who values level-3/hard-to-value assets and who can override; key-person provisions and succession; conflicts of interest and related-party arrangements; side letters / preferential terms (MFN); fund expenses charged to the fund vs. the manager; counterparty and prime broker arrangements; business continuity and cybersecurity; compliance staffing and whether the CCO is dedicated; auditor identity and any auditor changes; errors-and-omissions history and material NAV restatements. Each item is answered / partially answered / absent.
  5. Cross-document consistency pass (if materials provided). Compare DDQ claims against the deck, letters, and ADV: performance characterizations, AUM, team size and tenure, capacity statements, strategy descriptions. Quote both documents wherever they disagree.
  6. Severity-rank everything and write the findings report.

Output format

Length follows the substance: cover what the task needs and leave out filler sections, restated summaries, and boilerplate.

# DDQ Red-Flag Scan — [Manager / Fund]
Document: [title, date/version] | Comparison materials: [list or "none"]
Sections covered: [list] | Standard sections absent: [list]

## Summary
[3–5 sentences: overall responsiveness, the 2–3 findings that matter most,
and whether the pattern suggests sloppiness or deliberate opacity.]

Findings: [N] HIGH / [N] MEDIUM / [N] LOW

## HIGH severity
### H1. [Short title]
- **Location:** [section / question number / page]
- **Exact language:** > "[verbatim quote]"  — or — **Missing:** [the disclosure that should be here]
- **Why it matters:** [1–3 sentences]
- **Possible benign explanation:** [1 sentence — there often is one]
- **Follow-up to ask:** "[specific question]"

[Repeat per finding; same structure for MEDIUM and LOW.]

## Cross-document inconsistencies
| Claim | DDQ says ([loc]) | Other doc says ([doc, loc]) |
|---|---|---|

## Disclosure checklist
| Standard disclosure | Status | Note |
|---|---|---|
[answered / partial / absent for each checklist item]

## Follow-up question list (consolidated)
[All follow-ups in one copy-paste-ready block, ordered by severity.]

Severity scale — HIGH: missing or evasive on compliance history, valuation of level-3 assets, conflicts, key-person, or a direct contradiction with other documents. MEDIUM: vague on material operational items, internal inconsistencies, boilerplate where specifics were asked. LOW: sloppiness, stale dates, minor gaps.

Guardrails

  • Quote exactly. Every finding about language must reproduce the language verbatim with its location. No paraphrased accusations.
  • An absence is a finding, not an allegation. "The DDQ does not disclose X" is correct; "the manager is hiding X" is not. Always offer the benign explanation where one plausibly exists.
  • Never fabricate document content, question numbers, or quotes. If text extraction from a PDF is uncertain, say so and flag the section for manual reading.
  • Fact vs. inference: the quote is fact; the interpretation is inference — keep them visually separate in every finding.
  • This is a document scan, not operational due diligence. It does not replace background checks, service-provider verification, or on-site ODD, and it is not an investment or redemption recommendation.
  • Confidentiality: DDQs are confidential. Remind the user to handle the scan output under the same restrictions as the source document.

Example prompt

"Attached: Crestline Partners' completed DDQ and their pitch deck. Scan the DDQ for red flags — especially valuation policy and compliance history — and check whether anything in it contradicts the deck. Rank findings by severity and give me the follow-up questions for the ODD call."

Frequently asked questions

What does the Ddq Red Flag Scanner AI skill do?

Scan a completed DDQ or RFP response for red flags — vague or evasive answers, internal inconsistencies, missing standard disclosures, and answers that contradict the manager's deck or letters. For allocators, fund-of-funds analysts, OCIOs, and ODD teams. Trigger on phrases like "scan this DDQ," "red flags in this DDQ," "review these RFP responses," "anything off in this questionnaire," "check this DDQ against the deck," "what's missing from this DDQ," or when the user uploads a completed due diligence questionnaire or RFP response. Do NOT use for a general first pass on a manager — use the...

Why use Ddq Red Flag Scanner on TypingMind?

Because you install it once and use it with any model. Ddq Red Flag Scanner is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ddq Red Flag Scanner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/latour-ai/skills/tree/main/ddq-red-flag-scanner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Ddq Red Flag Scanner?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ddq Red Flag Scanner?

As many as you like. As long as a model supports skills, you can use Ddq Red Flag Scanner with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ddq Red Flag Scanner AI skill free?

Yes. It is published on GitHub by latour-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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