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 aibast_compliance-dashboard_rc01git 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/aibast_compliance-dashboard_rc01)<a href="https://agentmods.dev/skills/microsoft/aibast-agents-library/aibast_compliance-dashboard_rc01"><img src="https://agentmods.dev/badge/skills/microsoft/aibast-agents-library/aibast_compliance-dashboard_rc01/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/microsoft/aibast-agents-library/aibast_compliance-dashboard_rc01"><img src="https://agentmods.dev/badge/skills/microsoft/aibast-agents-library/aibast_compliance-dashboard_rc01.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 | $0.00040 | $0.00756 |
| Opus 5.5 | $0.00016 | $0.00302 |
| Sonnet 5.5 | $0.00008 | $0.00151 |
| Haiku 4.5 | $0.00004 | $0.00076 |
Grade A, and why
compliance-dashboard-and-audit-readiness 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 1mo 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 — 15 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compliance dashboard and audit readiness
Retrieve both attached knowledge files and use native citations.
RC-01
Audit outcome cannot be predicted from this synthetic pilot. Snapshot 2026-08-07. Report credential records T-2041 lapsed 12 days and T-2233 lapsed 3 days; a proposed pilot stand-down requires supervisor review and no active status, legal incapacity, notification, enrollment, or completed stand-down is established. Report exactly four reporting-defective trades with exact labels: TRD-88117 field 57, Investment decision within firm, is missing; source T-2041. TRD-88129 field 36 venue mismatch, reported XETR and verified XPAR. TRD-88133 field 59, Execution within firm, is missing with source T-2107; field 7, Buyer identification code, is missing with source 549300XKQZ2P4NLK7T18; zero ARM submission record. TRD-88150 field 36 venue mismatch, reported TQEX and verified XLON. TRD-88162 has a complete report and only an algorithm-documentation gap. Execution outliers are TRD-88133 and TRD-88150. ALGO-IS-DE is 37 days overdue. ALGO-POV-NL goes live in 6 days, was never validated, and lacks risk controls, kill-switch test, and conformance test. Use at risk, control gap, and requires authorized review. No side effect occurred. Never relabel field 57 as executing broker, field 59 as trader ID, or field 7 as counterparty LEI. Never say immediate stand-down required, must be stood down, active traders, five reporting defects, or four additional reporting exceptions.
RC-05
The assertion that reporting is fine is not supported by the fixed synthetic evidence. Use these exact reporting findings: TRD-88133 field 59, Execution within firm, is missing with source T-2107; field 7, Buyer identification code, is missing with source 549300XKQZ2P4NLK7T18; zero ARM submission record. TRD-88117 field 57, Investment decision within firm, is missing with source T-2041. TRD-88129 field 36 venue mismatch, reported XETR and verified XPAR. TRD-88150 field 36 venue mismatch, reported TQEX and verified XLON; execution outlier is separate. Exactly four reporting-defective trades. TRD-88162 has a complete report and is not a reporting defect. Two lapsed credentials are records, not proof of active traders or legal incapacity; proposed stand-down requires authorized review and was not performed. ALGO-IS-DE is 37 days overdue. ALGO-POV-NL is unvalidated and 6 days from go-live. Owner is Quant Execution. Proposed corrections and one new submission are synthetic dry runs only. Use at risk, control gap, and requires authorized review. No correction, ARM transmission, stand-down, enrollment, notification, deployment change, or external record change occurred. Never relabel field 59 as trader ID, field 7 as LEI, or field 57 as executing broker. Say missing, not incorrect. Never claim knowledge of other reporting channels. Never invent Head of Algo Risk, active traders, execution during a lapsed period, completed action, or five reporting defects.
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.
- 1mo ago Changed · -14 lines · +9 tokens per session dbb2d3c1889e
- 1mo ago First seen · 29 lines · 31 tokens per session scan A 17f410421c22
compliance-dashboard-and-audit-readiness is a skill published in the GitHub repository microsoft/aibast-agents-library (8 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 756 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
fto-triage
Freedom-to-operate triage — a structured first look at potentially blocking patents, not an FTO opinion. Use when a product, process, or feature is being evaluated for blocking patents, when asked whether anything stops a launch, or to build a claim-chart first pass against the most plausible patents before patent…
legal-hold
Issue, refresh, release, or report on legal holds — drafts the hold notice as .docx, updates legalhold fields in log.yaml, and calendars the next refresh. Use when the user says "issue a hold", "refresh hold", "release hold", or asks for a portfolio-wide hold status report.
specification-writing
A workflow for writing complete patent specifications from patent claims and an invention disclosure. It adapts the document to a chosen jurisdiction, such as the US, Europe, or China.
terms-of-service
Draft terms of service and a companion privacy notice matched to the product's form and feature modules, not legal advice, delivered as a Word document.
x-scorecard
OpenSSF Scorecard for assessing open source project security. Check security best practices and compliance. Dependency: This is an x-cmd module. Install x-cmd first (see x-cmd skill for installation options). see x-cmd skill for installation.
ai-risk-management
Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the…