investor-call-prep
Prepare for investor calls by pulling upcoming meetings from Google Calendar, deeply researching each investor and their firm (website scraping, portfolio analysis, thesis extraction), checking for competitor conflicts, and outputting an honest prep sheet with compatibility assessments. Use when asked to prep for investor meetings, fundraising calls, VC meetings, or demo day.
npx skills add gooseworks-ai/goose-skills --skill investor-call-prep --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Investor Call Prep ## Setup Read your credentials from ~/.gooseworks/credentials.json: ```bash export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])") export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))") ``` If ~/.gooseworks/credentials.json does not exist, tell the user to run: `npx gooseworks login` All endpoints use Bearer auth: `-H "Authorization: Bearer $GOOSEWORKS_API_KEY"` Pull investor meetings from Google Calendar, deep-research each firm (scrape their website, analyze portfolio, extract thesis), and output an honest prep sheet that says which investors are a real fit and which aren't. **Read-only calendar access. Never creates, modifies, or deletes events.** ## Input - **domain** (required) — user's company website (provided in the prompt, e.g. "prep my investor calls for orthogonal.com") - **competitors** (optional) — auto-detected if not provided Always export to Google Sheets at the end — it's free and takes seconds. ## Step 1: Pull Investor Meetings Pull from today through Demo Day (March 24,
- Setup
- Input
- Step 1: Pull Investor Meetings
- Filtering — be precise, not greedy
- Create the Google Sheet immediately after confirmation
- Step 2: Research the User's Company
- Step 2b: Reverse-lookup competitor investors (one-time, cheap)
- Step 3: Research Each Investor
- 3a. Apollo — investor profile from email
- 3b. Scrape the firm's website (most reliable source)
- 3c. Perplexity — thesis, portfolio, competitor check
- Step 4: Classify Before Compiling
- Surface ecosystem investments, not just competitor conflicts
- Step 5: Compile Prep Sheet
export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")
Adjust timeMin to today's date
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
Auto-detect competitors (skip if user provided)
Run one call per competitor (e.g. 5 competitors = 5 calls total)
Firm enrichment
Find key people
Main page — thesis, overview, portfolio
Portfolio page (try /portfolio, /companies, /investments — skip on 404)What does the investor-call-prep skill do?
Prepare for investor calls by pulling upcoming meetings from Google Calendar, deeply researching each investor and their firm (website scraping, portfolio analysis, thesis extraction), checking for competitor conflicts, and outputting an honest prep sheet with compatibility assessments. Use when asked to prep for investor meetings, fundraising calls, VC meetings, or demo day.
How do I install it?
Run `npx skills add gooseworks-ai/goose-skills --skill investor-call-prep --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.