dd-verification

A process for checking claims in pitch decks or founder statements against multiple independent sources. It classifies each claim as verified, partly verified, unverified, or contradicted.

In plain words
What is it for?
Checking claims about market size, business traction, founders, and competitors during investment or company due diligence.
Why use it?
It helps separate evidence-backed statements from claims that still need proof. This reduces the risk of relying only on a company's own presentation.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/abilityai/trinity/dd-verification
Any agent
npx skills add Abilityai/trinity --skill dd-verification
Clone the repo
git clone --depth 1 https://github.com/Abilityai/trinity

Made for: Claude Code, Codex.

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 582 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00035 $0.00582
Opus 5 $0.00017 $0.00291
Sonnet 5 $0.00007 $0.00116
Haiku 4.5 $0.00003 $0.00058

Measured 3d ago against content hash 992824df201a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dd-verification 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 3d 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.

docs/demos/vc-due-diligence/skills/dd-verification/SKILL.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Claim Verification Methodology

When analyzing claims from pitch decks, founder statements, or company materials, follow this rigorous verification process.

Verification Levels

Assign one of these confidence levels to every claim:

Level Definition Requirements
VERIFIED Confirmed via 2+ independent sources Multiple credible sources agree
PARTIALLY VERIFIED Some evidence supports, gaps remain One credible source + logical consistency
UNVERIFIED Cannot confirm with available data No independent sources found
CONTRADICTED Evidence conflicts with claim Sources disagree with stated claim

Verification Process

1. Identify the Claim

Extract the specific, testable assertion:

  • ❌ "We're growing fast" (vague)
  • ✅ "We grew 300% YoY in 2024" (specific, testable)

2. Find Independent Sources

Never rely solely on company-provided materials. Seek:

  • Public filings (SEC, state records)
  • Third-party data providers (Crunchbase, PitchBook, LinkedIn)
  • Press coverage (with dates)
  • Customer reviews and testimonials
  • Industry reports from analysts

3. Cross-Reference

Compare across sources:

  • Do numbers align within reasonable variance (±10%)?
  • Are timelines consistent?
  • Do different sources tell the same story?

4. Document Everything

For each verified claim, record:

{
  "claim": "The specific claim text",
  "confidence": "VERIFIED | PARTIALLY VERIFIED | UNVERIFIED | CONTRADICTED",
  "sources": [
    {"name": "Source name", "url": "URL if available", "date": "YYYY-MM-DD"}
  ],
  "notes": "Any caveats or context"
}

Red Flags

Watch for these warning signs:

  • Round numbers without context ("$10M ARR exactly")
  • Claims that can't be independently verified
  • Metrics that don't match industry standards
  • Vague timeframes ("recently", "soon")
  • Comparisons without clear methodology

Output Format

Include a verification summary in your analysis:

{
  "verification_summary": {
    "total_claims_analyzed": 15,
    "verified": 8,
    "partially_verified": 4,
    "unverified": 2,
    "contradicted": 1
  },
  "key_concerns": [
    "Market size claim contradicted by industry reports",
    "Founder employment dates don't match LinkedIn"
  ]
}

Read the full file on GitHub · 83 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. 3d ago First seen · 83 lines · 35 tokens per session scan A 992824df201a

Subscribe to this mod's changes

dd-verification is a skill published in the GitHub repository Abilityai/trinity (496 stars, last pushed 5d ago), licensed Apache-2.0. It adds 35 tokens to every session and 582 once invoked, about $0.0002 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.

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