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 agentmods add skills/fiber-ai/fiber-ai-plugin/helpnpx skills add fiber-ai/fiber-ai-plugin --skill helpgit clone --depth 1 https://github.com/fiber-ai/fiber-ai-pluginWrote 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/fiber-ai/fiber-ai-plugin/help)<a href="https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/help"><img src="https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/help.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00032 | $0.01463 |
| Opus 5 | $0.00016 | $0.00732 |
| Sonnet 5 | $0.00006 | $0.00293 |
| Haiku 4.5 | $0.00003 | $0.00146 |
Grade A, and why
help scanned grade A with 0 findings 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 3d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fiber AI — Capabilities and Commands
What is Fiber AI?
Fiber AI provides B2B data enrichment APIs for finding companies, discovering prospects, and revealing verified contact information (work emails, phone numbers). It supports both direct API queries via MCP tools and programmatic access via TypeScript and Python SDKs.
Available Commands
General skills
| Command | What It Does |
|---|---|
/fiber:quickstart |
Guided first-run: verify key, search companies, reveal one contact |
/fiber:search "AI companies in NYC" |
Search for companies or people matching criteria |
/fiber:enrich "linkedin.com/in/someone" |
Reveal contact details (email, phone) for a person or company |
/fiber:audience "Series A fintech startups" |
Build a prospecting list with bulk search, enrichment, and export |
/fiber:sdk-ts "build a lead gen app" |
Get help writing TypeScript code with @fiberai/sdk |
/fiber:sdk-py "python enrichment script" |
Get help writing Python code with fiberai |
/fiber:setup |
Configure API key and verify MCP connection |
/fiber:help |
Show this help information |
Playbook skills (workflow-specific)
| Command | What It Does |
|---|---|
/fiber:find-similar-companies "Stripe" |
Find lookalike companies from a seed account |
/fiber:enrich-linkedin-csv |
Bulk enrich LinkedIn URLs with emails and phones |
/fiber:build-recruiting-audience "staff iOS engineers at Series B fintechs" |
Build a persistent, exportable recruiting candidate list |
/fiber:expand-from-email-list |
Reverse-resolve emails to LinkedIn profiles |
/fiber:enrich-github-handles |
Developer sourcing: GitHub handles to LinkedIn to contact details |
/fiber:find-and-enrich-by-role "VP Eng at fintech" |
Find people by role + company criteria and reveal contacts |
/fiber:track-signals "job-change alerts for these 200 accounts" |
Track job changes, hiring intent, social activity, and funding |
/fiber:benchmark-vs-competitor "100 LinkedIn URLs vs PDL" |
Run a reproducible, honest benchmark vs a competing provider |
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.
- 3d ago First seen · 101 lines · 32 tokens per session scan A 9f823df8590e
help is a skill published in the GitHub repository fiber-ai/fiber-ai-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 1,463 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…