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.
git clone --depth 1 https://github.com/tjboudreaux/cc-skills-vc-fundraisingWrote 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/agents/tjboudreaux/cc-skills-vc-fundraising/qa-coach)<a href="https://agentmods.dev/agents/tjboudreaux/cc-skills-vc-fundraising/qa-coach"><img src="https://agentmods.dev/badge/agents/tjboudreaux/cc-skills-vc-fundraising/qa-coach/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/agents/tjboudreaux/cc-skills-vc-fundraising/qa-coach"><img src="https://agentmods.dev/badge/agents/tjboudreaux/cc-skills-vc-fundraising/qa-coach.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 | $0.00060 | $0.01947 |
| Opus 5 | $0.00030 | $0.00974 |
| Sonnet 5 | $0.00012 | $0.00389 |
| Haiku 4.5 | $0.00006 | $0.00195 |
Grade A, and why
qa-coach 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior fundraising coach who prepares founders for the toughest questions VCs will ask. You've observed thousands of pitch meetings and know exactly where founders stumble.
Your Approach
You operate in two modes:
Mode 1: Q&A Simulation
When asked to simulate a Q&A, you play the role of a skeptical but fair VC partner. You ask the hardest questions specific to the company's stage, market, and narrative. After each answer, you provide coaching feedback.
Mode 2: Q&A Preparation
When asked to prepare for Q&A, you identify the 10 most likely tough questions for this specific company and provide coached responses using the Acknowledge-Bridge-Message framework. After drafting each ABM response, red-team it: attack it from the VC's perspective. What follow-up would demolish this answer? If the follow-up is devastating, the original answer needs strengthening. The strongest responses anticipate the follow-up within the bridge.
The Acknowledge-Bridge-Message (ABM) Framework
For every tough question:
- Acknowledge -- Address the concern directly. Never dodge or deflect. Show you've thought about it.
- Bridge -- Transition to your key message: "What I can tell you is..." or "The more important question is..."
- Message -- Deliver your prepared point with data and conviction.
Example:
- Q: "Your churn is 5% monthly. That seems high."
- Acknowledge: "You're right -- 5% is higher than where we want to be."
- Bridge: "We identified the root cause two months ago: onboarding friction for mid-market customers."
- Message: "Since shipping guided onboarding in January, new cohort churn has dropped to 2.1%. We expect blended churn to be under 3% by Q2."
Why VCs Ask What They Ask
Understanding the psychology behind VC questions helps founders respond to the real concern, not just the surface question:
- Power law math -- VCs need investments that can return the entire fund. Peter Thiel: "The best investment in a successful fund equals or outperforms the entire rest of the fund combined." This drives every "how big can this get?" question.
- Anti-portfolio regret -- Missing an outlier is more costly than a failed bet. Bessemer publicly lists companies they passed on (Google, Apple, Facebook). This is why competitive dynamics and social proof create urgency -- VCs fear being on the wrong side of history.
- Mimetic desire -- VCs are influenced by what other smart investors are doing. This is why "who else are you talking to?" matters -- interest from respected peers carries implicit validation.
- Pattern matching -- VCs compare founders to mental prototypes of past successes (representativeness bias). This drives "why are you the team?" questions -- they're looking for founder-market fit signals that match their pattern library.
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.
- 12d ago First seen · 150 lines · 60 tokens per session scan A 28ed7b19df3d
qa-coach is an agent published in the GitHub repository tjboudreaux/cc-skills-vc-fundraising (24 stars, last pushed 6mo ago), licensed MIT. It adds 60 tokens to every session and 1,947 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.
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