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 commands/fiber-ai/fiber-ai-plugin/fiber-audiencegit 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/commands/fiber-ai/fiber-ai-plugin/fiber-audience)<a href="https://agentmods.dev/commands/fiber-ai/fiber-ai-plugin/fiber-audience"><img src="https://agentmods.dev/badge/commands/fiber-ai/fiber-ai-plugin/fiber-audience.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 5.1 | $0.00021 | $0.00354 |
| Opus 5 | $0.00010 | $0.00177 |
| Sonnet 5 | $0.00004 | $0.00071 |
| Haiku 4.5 | $0.00002 | $0.00035 |
Grade A, and why
fiber-audience 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 6d 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.
What it actually says
Walk the user through the Fiber AI audience workflow for bulk operations.
All audience operations use the Core MCP via call_operation. Call get_endpoint_details_full for each operationId before constructing parameters.
The 8-step lifecycle (do not skip steps):
- Create:
call_operation("createAudience", {"body": {"apiKey": "...", "name": "..."}})— save the audience ID - Set filters:
call_operation("updateAudienceSearchParams", {"body": {...}, "path": {"audienceId": "..."}})— usescompanySearchParamsandprospectSearchParams - Build:
call_operation("buildAudience", ...)— charges credits, CONFIRM with user first - Poll build status: check audience status until
NORMAL(poll every 10-30s) - Estimate cost:
call_operation("estimateEnrichmentCost", ...)— free, show estimate to user - Enrich:
call_operation("triggerEnrichment", ...)— charges credits, CONFIRM with user first - Poll enrichment:
call_operation("getEnrichmentStatus", {"query": {"apiKey": "..."}, "path": {"audienceId": "..."}})— poll every 30s until complete - Export:
call_operation("exportCompanies", ...)orcall_operation("exportProspects", ...)
NEVER skip the cost estimate. NEVER proceed without user confirmation on credit-charging steps.
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.
- 6d ago First seen · 22 lines · 21 tokens per session scan A fee758588a2a
fiber-audience is a command published in the GitHub repository fiber-ai/fiber-ai-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 354 once invoked, about $0.0001 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.
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