Agent skill

Investor Call Prep

by gooseworks-ai in gooseworks-ai/goose-skills

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…

MITAuto-check passedBusiness, Finance & HR

Install Investor Call Prep

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills investor-call-prep --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/research/capabilities/investor-call-prep .claude/skills/investor-call-prep && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
investor-call-prep
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.8k tokens
SKILL.md length
1,176 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Works in 6 steps: Pull Investor Meetings → Research the User's Company → Research Each Investor → …
  • Asked to prep for investor meetings
  • SKILL.md covers Setup, Input, Step 1: Pull Investor Meetings and Step 2: Research the User's…, plus 5 more sections
  • Calls curl, python3 and npx; reaches api.gooseworks.ai; needs GOOSEWORKS_API_KEY

What it does

Investor Call Prep is an agent skill from gooseworks-ai/goose-skills. 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.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Business, Finance & HR, covering Fundraising and pitch decks, Sales call preparation and Web scraping. It works with Google Calendar. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Asked to prep for investor meetings
  • Fundraising calls

Example prompts

  • “/investor-call-prep”

Requirements

  • Python 3
  • Node.js
  • A credential in GOOSEWORKS_API_KEY

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Pull Investor Meetings
  2. Research the User's Company
  3. Research Each Investor
  4. Classify Before Compiling
  5. Compile Prep Sheet
  6. Google Sheets Export

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • python3
    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.gooseworks.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GOOSEWORKS_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Investor Call Prep loads about 3.8k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 1,176 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,176 words, ~3,769 tokens.

Download SKILL.mdSave it as .claude/skills/investor-call-prep/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
investor-call-prep
description
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.
source
orthogonal

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, 2026). All W26 companies are fundraising now through Demo Day. Make multiple calls if needed to avoid truncation — e.g. split into week 1 and week 2.

bash
# Adjust timeMin to today's date
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"google-calendar","path":"/list-events"}'
  "calendarId": "primary",
  "timeMin": "{today}T00:00:00Z",
  "timeMax": "{midpoint}T23:59:59Z",
  "maxResults": 100,
  "singleEvents": true,
  "orderBy": "startTime"
}'

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"google-calendar","path":"/list-events"}'
  "calendarId": "primary",
  "timeMin": "{midpoint+1}T00:00:00Z",
  "timeMax": "2026-03-24T23:59:59Z",
  "maxResults": 100,
  "singleEvents": true,
  "orderBy": "startTime"
}'
Filtering — be precise, not greedy

The keyword approach catches false positives (personal meetings, mock pitches, etc.). Use this priority order:

  1. Strong signal (auto-include): Title starts with "Investors between" — this is the standard Cal.com booking format for investor meetings.
  2. Medium signal (auto-include): Attendee email domain is a known VC domain (e.g. @moonfire.com, @a16z.com, @accel.com) OR the event description contains VC firm names.
  3. Weak signal (requires confirmation): Title contains keywords like invest, vc, fund, capital, ventures, angel, seed, series — BUT does NOT match pattern #1. These need manual review.
  4. Exclude: Events with "mock" or "practice" in title/description (these are rehearsals, not real meetings). Also exclude batch/group events with no attendees (e.g. "Fundraising Open Mic", "Demo Day") — these are YC events, not 1:1 investor calls.

Extract: title, date/time, attendee emails (non-company = investor contacts), description (often has investor names/emails even when attendee list doesn't).

Present filtered list to user for confirmation before proceeding.

Create the Google Sheet immediately after confirmation

Before starting any research, create the spreadsheet and populate it with all confirmed investor rows (date/time, firm name, investor name, firm website — leave research columns blank). Share the link with the user so they can watch results fill in live as each investor is researched. This is much better UX than waiting for all research to complete.

Step 2: Research the User's Company

Ask the user to describe their company in 1-2 sentences rather than relying on Perplexity, which often confuses companies with similar names (e.g. orthogonal.com vs orthogonal.io). The user's own description is always more accurate than a web search for early-stage startups.

Then auto-detect competitors:

bash
# Auto-detect competitors (skip if user provided)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"perplexity","path":"/chat/completions"}'
  "model": "sonar",
  "messages": [{"role": "user", "content": "Top 5-10 competitors of {company_name} ({domain})? {user_provided_description}. Company names and domains only."}]
}'

Verify the competitor list with the user before proceeding. Perplexity often returns enterprise incumbents (MuleSoft, Workato) rather than actual startup competitors. The user knows their competitive landscape better.

Save the company description and confirmed competitor list — use them for every investor assessment.

Step 2b: Reverse-lookup competitor investors (one-time, cheap)

Instead of asking each investor "have you invested in X?" (unreliable), do a single reverse lookup for each competitor. This is 1 Perplexity call per competitor — not per investor.

bash
# Run one call per competitor (e.g. 5 competitors = 5 calls total)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"perplexity","path":"/chat/completions"}'
  "model": "sonar",
  "messages": [{"role": "user", "content": "Who are the investors in {competitor_name} ({competitor_domain})? List all known venture capital firms and angel investors who have invested in them, with round details if available."}]
}'

Build a lookup table: {investor_firm -> [competitors they backed]}. Cross-reference this against the meeting list. This catches conflicts that per-investor Perplexity queries miss, at a fraction of the cost.

Step 3: Research Each Investor

Run ALL of these in parallel per investor. Every source adds unique data.

3a. Apollo — investor profile from email
bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"apollo","path":"/api/v1/people/match"}'
  "email": "{investor_email}",
  "reveal_personal_emails": true
}'

No attendee email? Don't stop. Parse firm name from event title, then:

bash
# Firm enrichment
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"apollo","path":"/api/v1/organizations/enrich","query":{"domain":"{firm_domain}"}}'

# Find key people
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"apollo","path":"/api/v1/mixed_people/search"}'
  "q_organization_domains": "{firm_domain}",
  "person_titles": ["Partner", "Principal", "Managing Director", "GP", "General Partner", "Investor"],
  "page": 1,
  "per_page": 10
}'
3b. Scrape the firm's website (most reliable source)

VC websites are the ground truth. Perplexity and Apollo often have gaps for smaller firms.

bash
# Main page — thesis, overview, portfolio
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://{firm_website}",
  "user_prompt": "Extract ALL information: investment thesis, fund size, check size, stage focus, sector focus, geographic focus, every portfolio company listed, team members with titles and LinkedIn URLs, contact info."
}'

# Portfolio page (try /portfolio, /companies, /investments — skip on 404)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://{firm_website}/portfolio",
  "user_prompt": "Extract every portfolio company: name, sector, funding stage, description, website URL."
}'

# Team page (try /team, /people, /about — skip on 404)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://{firm_website}/team",
  "user_prompt": "Extract every team member: full name, title, LinkedIn URL, bio summary, background."
}'
3c. Perplexity — thesis, portfolio, competitor check

Critical: use context-rich prompts. Include location, GP names, aliases. "Tell me about e2vc" gets nothing. "Tell me about e2vc, formerly 500 Emerging Europe, based in Turkey" gets rich results.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"perplexity","path":"/chat/completions"}'
  "model": "sonar",
  "messages": [{"role": "user", "content": "Tell me about {firm_name}, a venture capital firm{location_context}{gp_context}{alias_context}. I am raising for {company_name}, {company_description}. Answer:
1. Investment thesis and typical check size?
2. Notable portfolio companies in {company_sectors}?
3. Have they invested in any of these competitors: {competitor_list}?
4. What stage?
5. Recent investments or news?
6. Key partners and backgrounds?
7. Would {company_name} be a good fit for them? Why or why not?"}]
}'

Step 4: Classify Before Compiling

Before writing up the prep sheet, classify each meeting into one of these categories based on research:

  1. VC Fund — traditional venture capital firm (GP, Partner, Principal, Associate)
  2. Angel — individual investor (current/former founder, operator, or executive investing personally)
  3. NOT an investor — flag prominently. This includes:
    • Founders of other startups (potential BD/partnership, not fundraise)
    • Researchers/academics with no investing track record
    • Operators at companies (not investing personally)
    • Mock pitch / practice sessions

For non-investors, still include them in the sheet but mark the Compatibility column as "NOT AN INVESTOR" and explain what the meeting likely is (BD, partnership, mock pitch, etc.). This prevents the user from wasting prep time on a fundraise pitch when the meeting is something else.

Show full SKILL.md (438 more words)Show less
Surface ecosystem investments, not just competitor conflicts

When an investor has portfolio companies that are adjacent to the user's space (not direct competitors but in the same ecosystem), surface these as Ecosystem Signals rather than ignoring them. These are actually positive — they show the investor understands the space.

Examples:

  • An investor backed CrewAI (AI agent framework) → they understand agents need API access → good hook for Orthogonal
  • An investor backed Arcade.dev (AI tooling) → adjacent, not a conflict → shows thesis alignment
  • An investor backed Langbase (AI agents) → ecosystem overlap → talking point

Only flag as Competitor Conflict if the portfolio company is a direct competitor (same product, same customer, same use case). Adjacent/ecosystem companies go in the Talking Points column as conversation hooks.

Step 5: Compile Prep Sheet

Cross-reference all sources. When they conflict, prefer: website > Apollo > Perplexity.

Output format per meeting:
## {Firm Name} — {Date/Time}

**Investor:** {Name}, {Title}
**LinkedIn:** {linkedin_url}
**Firm:** {firm_name} | {firm_linkedin_url} | {firm_website}

**Thesis:** {specific, not generic}
**Stage:** {seed, Series A, etc.} | **Check Size:** {range} | **Fund Size:** {if known}
**Geographic Focus:** {regions}

**Portfolio ({count}):** {most relevant to user's space}
**Competitor Conflicts:** {names} or None found

**Compatibility: {verdict}**
{honest, company-specific assessment}

**Talking Points:**
1. {angle from portfolio overlap}
2. {angle from partner's background}
3. {angle from thesis alignment}
Compatibility — Be Honest and Specific

Every rating must reference the user's specific company, product, and sector. Generic assessments are useless.

Strong Fit — Thesis covers user's sector AND stage. Adjacent portfolio companies (not competitors). Partner has relevant domain expertise.

"Strong fit — Revo invests in B2B SaaS + AI from Turkey/CEE at seed-Series A ($500K-$5M). Their marketplace portfolio companies are adjacent. Melis's M&A background means she gets platform economics."

Moderate Fit — Partial overlap. Be specific about what's missing.

"Moderate fit — right stage but portfolio leans fintech/industrial tech, no developer tools. You'll need to educate them on the API marketplace space."

Weak Fit — Wrong thesis, stage, geography, or has funded a competitor. Don't sugarcoat.

"Weak fit — consumer apps focus, Series B+ checks. No dev tools portfolio. May not be worth your limited pre-demo-day time."

Competitor Conflict — Flag prominently.

"They backed Composio — a direct competitor. Ask early whether this creates a conflict."

Step 6: Google Sheets Export

The spreadsheet was already created in Step 1. Update each row as research completes — use curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"google-sheets","path":"/update-values"}'

Important: Always use curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \ -H "Content-Type: application/json" \ -d '{"api":"google-sheets","path":"for"}'

Tips

  • Parallelize everything: Apollo, Scrapegraph, Perplexity, Fiber are independent — run all in parallel per investor, and process investors simultaneously.
  • Website > all other sources: Firm websites are ground truth. Always scrape.
  • No email? Parse the firm name from the event title → derive domain → Apollo org enrich + people search + website scrape.
  • Context in Perplexity prompts: Include location, GP names, "formerly known as" — massively improves results for smaller firms.
  • Be brutal on fit: User has limited time. Say which meetings to prioritize and which to skip.
  • Firm domain from email: investor@somefirm.com → domain is somefirm.com.
  • Multiple attendees: Run Apollo on each. Most senior person = decision-maker.

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/research/capabilities/investor-call-prep of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

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Works with

Questions about Investor Call Prep

What does Investor Call Prep 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…. Investor Call Prep is an agent skill from gooseworks-ai/goose-skills. 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.

When should I use Investor Call Prep?

Investor Call Prep fits situations like: asked to prep for investor meetings; fundraising calls.

How do I install Investor Call Prep in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a claude-code`. Or copy the skill folder (skills/research/capabilities/investor-call-prep in gooseworks-ai/goose-skills) into .claude/skills/investor-call-prep in your project. Claude Code loads it when a task matches its description.

How do I install Investor Call Prep in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a codex`. Or copy the skill folder (skills/research/capabilities/investor-call-prep in gooseworks-ai/goose-skills) into .agents/skills/investor-call-prep in your project. Codex loads it when a task matches its description.

Can I use Investor Call Prep in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/investor-call-prep, .gemini/skills/investor-call-prep, .github/skills/investor-call-prep and .opencode/skills/investor-call-prep in your project.

What does Investor Call Prep need to run?

Going by SKILL.md and its folder, Investor Call Prep needs the command-line tools its instructions call (curl, python3 and npx) and credentials named GOOSEWORKS_API_KEY. Our summary lists: Python 3; Node.js; A credential in GOOSEWORKS_API_KEY.

Does Investor Call Prep access the network?

SKILL.md names 1 domain. In commands or code: api.gooseworks.ai; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Investor Call Prep safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Investor Call Prep use?

Investor Call Prep is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Investor Call Prep use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Investor Call Prep?

Skills that share tags, products or a category with Investor Call Prep: Apify Buying Signal Detection (apify/awesome-skills, 266 stars), Earnings Analysis (Wind-Alice/AliceMarket, 134 stars), Equity Research Core (byteseek/Mira, 275 stars) and Tradingview (gauss314/skills, 248 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Investor Call Prep?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.