Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
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.
npx skills add gooseworks-ai/goose-skills --skill people-company-search-fibergit clone --depth 1 https://github.com/gooseworks-ai/goose-skillsWrote 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/skills/gooseworks-ai/goose-skills/people-company-search-fiber)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/people-company-search-fiber"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/people-company-search-fiber/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/skills/gooseworks-ai/goose-skills/people-company-search-fiber"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/people-company-search-fiber.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 26 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 28 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 29 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 32 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 32 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium Data Exfiltration · line 72 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00020 | $0.04977 |
| Opus 5 | $0.00010 | $0.02488 |
| Sonnet 5 | $0.00004 | $0.00995 |
| Haiku 4.5 | $0.00002 | $0.00498 |
Grade A, and why
people-company-search-fiber scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \ How it starts
The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fiber AI - People & Company Intelligence
Cost Reference
| Endpoint | Cost | Notes |
|---|---|---|
/v1/natural-language-search/profiles |
$0.02/record (pageSize × $0.02) | Default $0.50 if no pageSize |
/v1/people-search |
$0.02/record (pageSize × $0.02) | Default $0.50 if no pageSize |
/v1/natural-language-search/companies |
Varies | |
/v1/kitchen-sink/person |
Varies | Single lookup |
/v1/validate-email/single |
~$0.02 |
Cheaper alternative for people search: Apollo mixed_people/search costs $0.01 flat per call regardless of result count. Use $GOOSEWORKS_API_BASE/v1/proxy/apollo/mixed_people/search when possible.
Tip: Use the dedicated proxy route $GOOSEWORKS_API_BASE/v1/proxy/fiber/... instead of the generic orthogonal proxy for cleaner billing tracking.
Setup
Read your credentials from ~/.gooseworks/credentials.json:
export GOOSEWORKS_API_KEY=$(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"
Comprehensive search and enrichment for people, companies, investors, and jobs.
Capabilities
- Search profiles from text: Takes free-form text (e
- Search companies from text: Takes free-form text (e
- Find person by email: Do a reverse lookup: given an email address, find someone's LinkedIn profile and personal details
- Live fetch LinkedIn profile: Returns an enriched profile with details for a given LinkedIn profile identifier
- Validate a single email: Checks if a given email is likely to bounce using a waterfall of strategies
- Kitchen sink person lookup: Search for a person using a variety of parameters such as LinkedIn slug, LinkedIn URL, or their current company information
- Kitchen sink company lookup: Search for a company using a variety of parameters such as LinkedIn slug, LinkedIn URL, name, etc
- Investor search: Search for investors with flexible filtering capabilities
- Fetch LinkedIn profile posts: Fetches recent posts from a LinkedIn profile
- Live fetch LinkedIn company: Returns an enriched company with details for a given LinkedIn company identifier
- People search: Search for people using filters
- Fetch LinkedIn post comments: Fetches paginated comments for a LinkedIn post
- Company search: Search for companies using filters
- Convert text into company search filters: Takes free-form text (e
- Convert text into profile search filters: Takes free-form text (e
- Job postings search: Search for job postings with flexible filtering capabilities
- Fetch LinkedIn post reactions: Fetches paginated reactions of a specific type for a LinkedIn post
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 366 lines · 20 tokens per session scan A 51a38f82a735
people-company-search-fiber is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 20 tokens to every session and 4,977 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
excalidraw-ai
Create professional Excalidraw diagrams by generating JSON directly. This skill provides the Excalidraw JSON schema reference and professional icon libraries for AI agents to autonomously create diagrams without templates.
error-handling
Python error handling patterns for FastAPI, Pydantic, and asyncio. Follows "Let it crash" philosophy - raise exceptions, catch at boundaries. Covers HTTPException, global exception handlers, validation errors, background task failures. Use when: (1) Designing API error responses, (2) Handling RequestValidationError…
linting
Python linting with Ruff - an extremely fast linter written in Rust. Use when: (1) Standardizing code quality, (2) Fixing style warnings, (3) Enforcing rules in CI, (4) Replacing flake8/isort/pyupgrade/autoflake, (5) Configuring lint rules and suppressions.
logfire
Structured observability with Pydantic Logfire and OpenTelemetry. Use when: (1) Adding traces/logs to Python APIs, (2) Instrumenting FastAPI, HTTPX, SQLAlchemy, or LLMs, (3) Setting up service metadata, (4) Configuring sampling or scrubbing sensitive data, (5) Testing observability code.
commit-message
Analyze git changes and generate conventional commit messages. Supports batch commits for multiple unrelated changes. Use when: (1) Creating git commits, (2) Reviewing staged changes, (3) Splitting large changesets into logical commits.
python-backend
Python backend development expertise for FastAPI, security patterns, database operations, Upstash integrations, and code quality. Use when: (1) Building REST APIs with FastAPI, (2) Implementing JWT/OAuth2 authentication, (3) Setting up SQLAlchemy/async databases, (4) Integrating Redis/Upstash caching, (5) Refactoring…