seed-coach
Skill51 starsChanged 11 days agoMITView on GitHub
Pitch Coach. Rehearses the investor call with you: drills the hard questions investors ask (market, competition, traction, unit economics, team, why now, risks, the ask), tailored to your own numbers from the competitor scout, the financial model, the market size and the investor you are meeting ("Rover has 515,771 ratings. Why will owners switch?"), grades your written answers (a number, specifics, no hedging, under a minute spoken, answer first) and writes a 20-minute plan for a booked first call. Reads seed/competitors.json, model.json, market.json, investors.json, pipeline.json and calls/. Use when the user says "prep me for the call", "practice my pitch", "what will investors ask", "drill me", "grade my answer", "mock investor", "call agenda", or after /seed-closer books a call.
In this repository10 other files it gives its agents.
- seed-closerSkill
- seed-dataroomSkill
- seed-deckSkill
- seed-investorsSkill
- seed-marketSkill
- seed-modelSkill
- seed-planSkill
- seed-scoutSkill
- seed-termsSkill
- seed-updatesSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 0 are distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 73
Run coach.py drill --n 10 with the user's words for customers and the supply side.
- Very specific: line 78
When a numbers question stumps them, show the "Your numbers" line and the file it came from.
- Very specific: line 81
When /seed-closer books a call, run coach.py agenda --investor ID --write and drill --investor ID, and walk through both the day before.
- Very specific: line 83
After the call, hand back to /seed-closer to log it and send the follow-up, and to /seed-dataroom if they ask for documents.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
seed-coach
The Pitch Coach gets you ready for the questions that sink first calls. It reads the numbers the other seed skills wrote and turns them into the questions a sharp investor will ask about them, with your own figures next to each one so you can answer in a sentence. Then it grades what you say against the things investors listen for, and lays out the 20 minutes of a booked call so you spend them on the story, not on small talk.
${CLAUDE_SKILL_DIR} below is this skill's folder (where this SKILL.md lives).
python3 "${CLAUDE_SKILL_DIR}/coach.py" drill --n 10 --customers owners --provider walker
python3 "${CLAUDE_SKILL_DIR}/coach.py" drill --investor maya-chen --category "the ask"
python3 "${CLAUDE_SKILL_DIR}/coach.py" drill --category "unit economics" --seed 3 --write
python3 "${CLAUDE_SKILL_DIR}/coach.py" grade --answer "15% flat, no owner fee. ..." --question ue-take
python3 "${CLAUDE_SKILL_DIR}/coach.py" grade --file answer.txt
python3 "${CLAUDE_SKILL_DIR}/coach.py" agenda --investor maya-chen --writeOther flags: --dir (default ./seed), --json, --minutes 10..60 for the
agenda, --seed N to shuffle within each category the same way every time.
What it reads and writes
| file | what |
questions.json (this folder) | the bank: 8 categories, generic questions plus tailored ones with {placeholders}, each with what the investor is testing and what a strong answer has |
seed/competitors.json | /seed-scout: the biggest rivals, their ratings, weak points and counts |
seed/model.json | /seed-model: take rate, contribution per order, LTV:CAC, breakeven month, peak burn, use of funds |
seed/market.json | /seed-market: TAM, SAM, SOM and the assumptions behind them |
seed/investors.json | /seed-investors: the investor's role, firm, last deal, check size, personal line |
seed/pipeline.json, seed/calls/<id>.ics | /seed-closer: whether the call is booked, and when (UTC shown in ET) |
seed/coach/drill.md, seed/coach/agenda-<id>.md | with --write |
How it works
- Drill. A tailored question is asked only when the files hold every
number it needs; otherwise it is skipped, never filled with a guess.
Tailored questions come first, one per category per round, then the
generic ones, so ten questions cover the ground.
--customersand--providerset the words ("owners", "walker pay"). - Grade, five checks of 20 points: 15 to 150 words (under 60 seconds at 150 words a minute), a number, no hedging words ("I think", "maybe", "probably"), at least two specifics (numbers, names) and no buzzwords ("disrupt", "world-class"), and the answer first (a first sentence of 25 words or fewer). Exit 0 only when all five pass.
- Agenda: seven blocks, 2, 3, 4, 4, 4, 2 and 1 minutes for 20 (scaled exactly for other lengths): hello and why them, the problem (the review evidence), the product, market and model, their questions (the three most likely), the ask, the next step.
Steps
- Run
coach.py drill --n 10with the user's words for customers and the supply side. Ask the questions one at a time, as the investor would. - Let the user answer in writing or out loud (transcribe it), then run
coach.py grade --answer "..." --question ID. Read the unchecked lines back and ask for a tighter version. Two or three rounds per question. - When a numbers question stumps them, show the "Your numbers" line and
the file it came from. If a number looks wrong, fix it in that skill
(
/seed-model,/seed-market), not in the answer. - When
/seed-closerbooks a call, runcoach.py agenda --investor ID --writeanddrill --investor ID, and walk through both the day before. - After the call, hand back to
/seed-closerto log it and send the follow-up, and to/seed-dataroomif they ask for documents.
Example: the reel
Leashly (made up) has a call with Maya Chen (fictional) of Northlight
Ventures on Thursday, November 5, 2026 at 10:00 AM ET. coach.py drill
--customers owners --provider walker opens with:
1. [market] Your SOM is 2.5% of a $960M SAM in 3 years. What gets you the first 1,000 customers?
2. [competition] Rover has 515,771 ratings. Why will owners switch?
3. [unit economics] What does your 15% take rate leave after walker pay?
Your numbers: Per walk: $25 price, you keep $3.75, $2.23 after costs.The answer "15% flat, no owner fee. We keep $3.75 of every $25 walk and
$2.23 after card fees and the backup walker. Rover keeps 20% of sitter
bookings and adds an owner fee." grades 100/100: 32 words, about 13
seconds. coach.py agenda --investor maya-chen opens with her personal line,
puts Rover's 106 of 341 and Wag!'s 81 of 324 in minutes 2 to 5, and ends on
the $1.5M post-money SAFE and a date for the next meeting.
What it never does
It never invents a number for an answer, never tells the user to overstate traction, and never promises that a good call means a check. Investors decide.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

