Apify Buying Signal Detection
apify/awesome-skills
Set up a recurring buying-signal detection pipeline that finds companies showing buying intent across three signal types — job postings (hiring for the persona), fundraising events (recent raises)…
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…
$ npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gooseworks-ai/goose-skills investor-call-prep --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "investor-call-prep" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/investor-call-prep into .claude/skills/investor-call-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investor-call-prep", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/investor-call-prepType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gooseworks-ai/goose-skills investor-call-prep --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/research/capabilities/investor-call-prep .agents/skills/investor-call-prep && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "investor-call-prep" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/investor-call-prep into .agents/skills/investor-call-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investor-call-prep", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gooseworks-ai/goose-skills investor-call-prep --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/research/capabilities/investor-call-prep .cursor/skills/investor-call-prep && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "investor-call-prep" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/investor-call-prep into .cursor/skills/investor-call-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investor-call-prep", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gooseworks-ai/goose-skills.git --path skills/research/capabilities/investor-call-prep--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gooseworks-ai/goose-skills investor-call-prep --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/research/capabilities/investor-call-prep .gemini/skills/investor-call-prep && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "investor-call-prep" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/investor-call-prep into .gemini/skills/investor-call-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investor-call-prep", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gooseworks-ai/goose-skills investor-call-prepInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/research/capabilities/investor-call-prep .github/skills/investor-call-prep && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "investor-call-prep" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/investor-call-prep into .github/skills/investor-call-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investor-call-prep", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gooseworks-ai/goose-skills --skill investor-call-prep -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gooseworks-ai/goose-skills investor-call-prep --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/research/capabilities/investor-call-prep .opencode/skills/investor-call-prep && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "investor-call-prep" agent skill from https://github.com/gooseworks-ai/goose-skills/tree/main/skills/research/capabilities/investor-call-prep into .opencode/skills/investor-call-prep/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investor-call-prep", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
investor-call-prepPrepare 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. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
curlpython3npxFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
api.gooseworks.aiFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
GOOSEWORKS_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,176 words, ~3,769 tokens.
.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.Read your credentials from ~/.gooseworks/credentials.json:
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.
Always export to Google Sheets at the end — it's free and takes seconds.
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.
# 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"
}'The keyword approach catches false positives (personal meetings, mock pitches, etc.). Use this priority order:
@moonfire.com, @a16z.com, @accel.com) OR the event description contains VC firm names.invest, vc, fund, capital, ventures, angel, seed, series — BUT does NOT match pattern #1. These need manual review.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.
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.
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:
# 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.
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.
# 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.
Run ALL of these in parallel per investor. Every source adds unique data.
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:
# 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
}'VC websites are the ground truth. Perplexity and Apollo often have gaps for smaller firms.
# 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."
}'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.
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?"}]
}'Before writing up the prep sheet, classify each meeting into one of these categories based on research:
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.
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:
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.
Cross-reference all sources. When they conflict, prefer: website > Apollo > Perplexity.
## {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}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."
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"}'
investor@somefirm.com → domain is somefirm.com.© 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
SKILL.md and 1 other file in skills/research/capabilities/investor-call-prep of gooseworks-ai/goose-skills.
Open the folder on GitHubat commit c650c6d
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.
Investor Call Prep next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Investor Call Prep this skillgooseworks-ai/goose-skills | 1.2k | 1 repos | ~3.8k | Automated safety check: Pass | MIT | |
| Apify Buying Signal Detectionapify/awesome-skills | 266 | — | ~5.1k | Automated safety check: Notes | Apache-2.0 | |
| Earnings AnalysisWind-Alice/AliceMarket | 134 | 3 repos | ~2.2k | Automated safety check: Pass | None | |
| Equity Research Corebyteseek/Mira | 275 | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Tradingviewgauss314/skills | 248 | — | ~4.7k | Automated safety check: Pass | MIT | |
| Xvary Stock Researchsickn33/agentic-awesome-skills | 47k | 2 repos | ~952 | Automated safety check: Pass | MIT |
apify/awesome-skills
Set up a recurring buying-signal detection pipeline that finds companies showing buying intent across three signal types — job postings (hiring for the persona), fundraising events (recent raises)…
Wind-Alice/AliceMarket
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage.
byteseek/Mira
Run Mira's core single-equity research workflow across fundamentals, financial quality, macro context, technical pricing, events, and thesis framing.
gauss314/skills
Datos de mercado de TradingView via APIs publicas internas sin auth: Scanner (~300 columnas con quote/indicadores tecnicos/financials/earnings/ratings/targets), Symbol Search v3 (ISIN/CUSIP/CIK)…
sickn33/agentic-awesome-skills
Thesis-driven equity analysis from public SEC EDGAR and market data; /analyze, /score, /compare workflows with bundled Python tools (Claude Code, Cursor, Codex).
Superior-Trade/superior-skills
A skill your agent uses when a Polymarket prediction-market thesis rests on an external event — CPI, Fed, elections, court rulings, ETF decisions — and needs market confirmation before committing.
gooseworks-ai/goose-skills
Scrape and search Reddit posts using Apify. An agent skill from gooseworks-ai/goose-skills.
gooseworks-ai/goose-skills
Generate or edit an image via any FAL image model (nano-banana edit, gpt-image, flux, ...), ROUTED THROUGH THE fal-proxy so it bills the Ads agent.
gooseworks-ai/goose-skills
Replace an existing video's opening with a supplied clip or free kinetic text hook while retaining and verifying every original body frame, audio, captions and ending.
gooseworks-ai/goose-skills
Scrape blog posts via RSS feeds (free, no API key) with Apify fallback for JS-heavy sites.
gooseworks-ai/goose-skills
Find leads by scraping engagers from a competitor's top LinkedIn posts.
gooseworks-ai/goose-skills
Assemble a ChatGPT chat-reveal video ad from a thread + timeline JSON — one continuous Playwright recording of a ChatGPT mobile chat (user types with the iOS keyboard up → taps send → keyboard…
Works with
Categories
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.
Investor Call Prep fits situations like: asked to prep for investor meetings; fundraising calls.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.