agentmods.dev

setup

A setup guide for connecting Fiber AI tools and installing or checking their SDKs. API keys are secret values that authorize requests to a service.

In plain words
What is it for?
It is for configuring the FIBER_API_KEY environment variable, verifying the Fiber AI connection, and checking SDK setup.
Why use it?
It helps identify missing credentials, authentication problems, and unavailable MCP connections before you try to use Fiber AI.

Skill for Claude CodeCodex

Part of the fiber plugin — 16 skills, 4 commands, 7 agents, 3 MCP servers shipped together

2 2mo ago A scan 31 tokens original MIT
View the source file fiber-ai/fiber-ai-plugin
Install

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.

agentmods
npx agentmods add skills/fiber-ai/fiber-ai-plugin/setup
Any agent
npx skills add fiber-ai/fiber-ai-plugin --skill setup
Clone the repo
git clone --depth 1 https://github.com/fiber-ai/fiber-ai-plugin

Made for: Claude Code, Codex.

Or install fiber, the plugin that ships this one along with the rest of its 16 skills, 4 commands, 7 agents, 3 MCP servers.

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 850 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00031 $0.00850
Opus 5 $0.00015 $0.00425
Sonnet 5 $0.00006 $0.00170
Haiku 4.5 $0.00003 $0.00085

Measured 2d ago against content hash f91ae2e1babe, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

setup 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 2d 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.

skills/setup/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Fiber AI Setup

Configure Fiber AI for the current environment.

When to Use

  • User says "setup fiber", "install fiber", "configure fiber"
  • MCP tools are not responding or returning authentication errors
  • User is missing an API key or needs to verify their configuration
  • First time using Fiber AI in this environment

Setup Steps

1. Check API Key

Check if the FIBER_API_KEY environment variable is set in the current shell.

  • If set: verify it works by calling the credits endpoint via Core MCP: call_operation("getOrgCredits", {"query": {"apiKey": "..."}}) If the call succeeds, show "Fiber AI is connected" with the credit balance.
  • If not set: instruct the user to:
    1. Get their API key from https://fiber.ai/app/api
    2. Set it as an environment variable: 
      export FIBER_API_KEY=sk_live_...
    3. For persistence, add the export to their shell profile (~/.zshrc or ~/.bashrc)

2. Verify MCP Connection

After the API key is configured:

  • Try calling a Core MCP tool like list_all_endpoints to verify connectivity
  • If successful: confirm "Fiber AI MCP is connected and working."
  • If unsuccessful: check that the editor supports HTTP (Streamable HTTP) MCP transport and that network access to mcp.fiber.ai is not blocked

3. SDK Installation (Optional)

Only if the user intends to write application code (not just use MCP tools):

  • TypeScript: npm install @fiberai/sdk
  • Python: pip install fiberai

4. Quick Verification

Suggest the user run a quick test to confirm everything works:

Try running /fiber:search "technology companies in San Francisco" to verify your setup.

Authentication Notes

  • For POST/PATCH/PUT API endpoints: apiKey is passed in the request body
  • For GET API endpoints: apiKey is passed in the query string
  • Check https://api.fiber.ai/docs/ for the exact authentication format per endpoint

Troubleshooting

  • MCP not connecting: verify the editor supports HTTP (Streamable HTTP) MCP transport
  • Authentication errors (401): API key is invalid or expired — regenerate at https://fiber.ai/app/api
  • Insufficient credits (402): top up at https://www.fiber.ai/app/subscription
  • Rate limit errors (429): wait a moment and retry
  • Network errors: ensure outbound HTTPS to mcp.fiber.ai is allowed by firewall or proxy

Read the full file on GitHub · 79 lines

Changes

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.

  1. 2d ago First seen · 79 lines · 31 tokens per session scan A f91ae2e1babe

Subscribe to this mod's changes

setup is a skill published in the GitHub repository fiber-ai/fiber-ai-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 850 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.

Building agents
Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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.

obra/superpowers · 37 tokens

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.

microsoft/vscode · 62 tokens

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.

microsoft/vscode · 51 tokens

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.

microsoft/vscode · 53 tokens

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…

microsoft/vscode · 71 tokens