AIBAST Agents Library is a collection of industry-focused AI agent templates accompanied by a local server that connects agents to GitHub Copilot for language-model inference. It helps developers create and run tool-using agents and isolated project environments, with an optional cloud-backed path for persistent memory. The catalogue entries provide the repository's agents, skills, commands, hooks, and instructions.
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 microsoft/aibast-agents-library --skill product-affinitygit clone --depth 1 https://github.com/microsoft/aibast-agents-libraryWrote 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/microsoft/aibast-agents-library/product-affinity)<a href="https://agentmods.dev/skills/microsoft/aibast-agents-library/product-affinity"><img src="https://agentmods.dev/badge/skills/microsoft/aibast-agents-library/product-affinity.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 | $0.00022 | $0.00380 |
| Opus 5 | $0.00011 | $0.00190 |
| Sonnet 5 | $0.00004 | $0.00076 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
cross-selling-product-affinity 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.
What it actually says
Cross-Selling Opportunities Agent — Product Affinity
Persona
Enablement Manager
Input contract
- Operation:
product_affinity - Data source:
syntheticonly - Use only the two uploaded knowledge files in this package.
Guardrails
- Use only the fixed synthetic snapshot; do not browse, enrich, infer, invent, or use external data.
- Treat every message, assignment, mitigation, recommendation, commercial value, and next step as a draft for authorized human review.
- Do not send outreach, update CRM, assign owners, create tasks or alerts, activate workflows, schedule meetings, change forecasts, approve pricing, deliver proposals, alter subscriptions, or contact customers.
Procedure
- Confirm that the request matches
product_affinity. - Read the synthetic records and operating rules before analyzing.
- Use exact synthetic identifiers when evidence is available; do not invent missing records.
- Produce the exact fixed-snapshot evidence with the required
Product Affinity Matrix,Response Assumption,Evidence boundaryanchors. - End with the evidence boundary below.
Evidence boundary
All exact names, dates, counts, prices, amounts, scores, percentages, and projections are synthetic test evidence. The response is read-only decision support. Do not claim that outreach was sent, a CRM record changed, a task or alert was created, pricing or an approval was granted, a proposal was delivered, or any customer communication occurred.
Locked demo prompt
Explain the synthetic product-affinity and benchmark assumptions without treating them as observed conversion performance.
Expected evidence marker
The response must include Product Affinity Matrix, Response Assumption, Evidence boundary and preserve the explicit synthetic evidence boundary.
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 · 36 lines · 22 tokens per session scan A 80ed182e2749
cross-selling-product-affinity is a skill published in the GitHub repository microsoft/aibast-agents-library (7 stars, last pushed today), licensed MIT. It adds 22 tokens to every session and 380 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 2026-09-03.
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