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 fiber-ai/fiber-ai-plugin --skill benchmark-vs-competitorgit 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/benchmark-vs-competitor)<a href="https://agentmods.dev/skills/fiber-ai/fiber-ai-plugin/benchmark-vs-competitor"><img src="https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/benchmark-vs-competitor/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/fiber-ai/fiber-ai-plugin/benchmark-vs-competitor"><img src="https://agentmods.dev/badge/skills/fiber-ai/fiber-ai-plugin/benchmark-vs-competitor.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00123 | $0.04004 |
| Opus 5.5 | $0.00049 | $0.01602 |
| Sonnet 5.5 | $0.00025 | $0.00801 |
| Haiku 4.5 | $0.00012 | $0.00400 |
Grade A, and why
benchmark-vs-competitor 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 2mo 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 — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fiber AI: Benchmark vs Competitor
Run a pre-registered, reproducible benchmark between Fiber AI and one competing data provider on the user's sample. The benchmark is vendor-agnostic: Fiber provides the reference implementation; the user brings the competitor's API credentials and endpoint.
Non-negotiable principle: this skill reports honest numbers, including metrics where Fiber underperforms. Do not suppress losses. Do not cherry-pick the sample. Fiber's credibility with evaluators is the moat; one dishonest benchmark destroys it.
When to use
- User is evaluating Fiber against an incumbent (PDL, Apollo, Clearbit, Coresignal, ZoomInfo, or similar)
- User says "benchmark", "bake-off", "test data quality", "compare providers", "run 100 samples"
- User is a CEO / head-of-GTM / head-of-data deciding between vendors
- The user has API credentials (or can get trial access) to the competitor
Do not use when
- User does not have competitor credentials and is unwilling to sign up - surface the blocker
- Sample size < 50 - not statistically meaningful; refuse or gently suggest the user bring more rows
- User wants a pure Fiber evaluation without competitor comparison - use
/fiber:enrich-linkedin-csvor/fiber:find-and-enrich-by-rolefor direct test runs - User wants marketing collateral, not an honest benchmark - refuse
Recommended evaluation dimensions (prioritized)
When the user has not specified what to test, guide the benchmark toward these dimensions. They are ordered by how reliably they differentiate providers in real-world agent workflows.
Dimension 1: Default response completeness
Enrich the same well-known profile (e.g., Bill Gates https://www.linkedin.com/in/williamhgates) on both providers with NO field selector or field-group parameter. Compare: number of top-level keys, total response size in bytes, and whether profile-classification fields (tags, flags, inferred location, tenure data) are present without extra configuration.
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.
- 2mo ago First seen · 246 lines · 123 tokens per session scan A 761368ced137
benchmark-vs-competitor is a skill published in the GitHub repository fiber-ai/fiber-ai-plugin (2 stars, last pushed 18d ago), licensed MIT. It adds 123 tokens to every session and 4,004 once invoked, about $0.0005 per session on Opus 5.5. 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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