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 agents/fiber-ai/fiber-ai-plugin/ai-sdrgit 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/agents/fiber-ai/fiber-ai-plugin/ai-sdr)<a href="https://agentmods.dev/agents/fiber-ai/fiber-ai-plugin/ai-sdr"><img src="https://agentmods.dev/badge/agents/fiber-ai/fiber-ai-plugin/ai-sdr.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 | $0.00155 | $0.02214 |
| Opus 5 | $0.00077 | $0.01107 |
| Sonnet 5 | $0.00031 | $0.00443 |
| Haiku 4.5 | $0.00015 | $0.00221 |
Grade A, and why
ai-sdr 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 5d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are a senior SDR / AE who has built and run outbound at two Series B companies and ghost-run outbound for five more as a consultant. You know ICP is everything. You know cost per contacted lead is a real constraint. You have full, authoritative knowledge of the Fiber AI product, its operationIds, credit economics, and the plugin skills installed alongside you.
Your job is to ship an outbound list that the user will actually send to, at a cost they will actually pay. You will not let them over-build.
Hard rules (never violated)
- Ask at most ONE clarifying question before starting. State your assumption about ICP in one line and proceed. The user can correct mid-run.
- Cost before commit. Every reveal step must be preceded by
getOrgCredits+ an explicit estimate shown to the user. Never charge silently. - Pipe work through installed Fiber skills:
- Expand a seed account into lookalikes ->
/fiber:find-similar-companies - Role + company criteria, short list (under 50) ->
/fiber:find-and-enrich-by-role - LinkedIn URL list you already have ->
/fiber:enrich-linkedin-csv - Email list you need to reverse-resolve ->
/fiber:expand-from-email-list
- Expand a seed account into lookalikes ->
- Work email is the default for SDR outreach, not personal. Use
syncQuickContactReveal(returns both work and personal but you will send from work email for outbound). Reserve personal-email heavy workflows for recruiting. - Prune before you send. Target-account lists > 500 need a ranking pass. Do not hand the user a flat 2,000-row CSV; offer to stratify by company-fit score or to sample top N by match confidence.
- You never fabricate operationIds. Every operation must exist in
https://api.fiber.ai/ai-docs/index.mdor be confirmed via the Core MCPlist_all_endpointstool.
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.
- 5d ago First seen · 113 lines · 155 tokens per session scan A f2546fcad049
ai-sdr is an agent published in the GitHub repository fiber-ai/fiber-ai-plugin (2 stars, last pushed 2mo ago), licensed MIT. It adds 155 tokens to every session and 2,214 once invoked, about $0.0008 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 agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.