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 workshop-nightly-pilotgit 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/workshop-nightly-pilot)<a href="https://agentmods.dev/skills/microsoft/aibast-agents-library/workshop-nightly-pilot"><img src="https://agentmods.dev/badge/skills/microsoft/aibast-agents-library/workshop-nightly-pilot/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/workshop-nightly-pilot"><img src="https://agentmods.dev/badge/skills/microsoft/aibast-agents-library/workshop-nightly-pilot.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 | $0.00069 | $0.02743 |
| Opus 5 | $0.00034 | $0.01372 |
| Sonnet 5 | $0.00014 | $0.00549 |
| Haiku 4.5 | $0.00007 | $0.00274 |
Grade A, and why
workshop-nightly-pilot 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 today.
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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workshop Nightly Pilot
This is the repeatable runbook for testing an AIBAST workshop like a student, against the real
product, not just reviewing existing screenshots. It was built by piloting
solutions/account-intelligence end-to-end (see PR #206 and issue #204), which found a real,
reproducible Copilot Studio bug (AI-02/AI-06 skipped knowledge retrieval on the first turn of a
fresh chat, then passed on retry). Every subsequent solution should get the same treatment.
Why this exists: annotation-box QA (fixing misaligned green highlight boxes on existing
screenshots) is a different, smaller job than this one. That pass was done across all 51 other
solutions in PRs #207-#216 and only touched already-reusable screenshots. It never rebuilt an
agent, never re-ran a demo prompt, and never caught a live product regression. This skill is what
actually re-validates the product, not just the pictures of it.
Instructions for Copilot
Execute these steps autonomously. Pause only where marked ⏸.
Step 0: Load rotation state
Read solutions/_shared/pilot-rotation-state.json. It tracks, per solution: status
(pending / piloted / no_checkpoints_file / blocked), reshoot_required_count at last
scan, last_piloted date, environment_agent_name used, and issues_filed.
Pick the next batch to work (default default_batch_size_per_run from the state file, currently
2) using this order:
status == "pending", highestreshoot_required_countfirst (biggest gaps get fixed sooner).- Skip
status == "no_checkpoints_file"entries -- flag them in your report but do not invent a checkpoints file from scratch; that's a separate authoring task, not a pilot. - Once every
pendingentry has been piloted, reset the whole rotation: set everypilotedentry back topending(a fresh nightly regression pass), bump afull_cycles_completedcounter at the top level, and start again from #1. This is what makes it a nightly loop instead of a one-time backlog burn-down.
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.
- today First seen · 174 lines · 69 tokens per session scan A bfc5263794c3
workshop-nightly-pilot is a skill published in the GitHub repository microsoft/aibast-agents-library (7 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 2,743 once invoked, about $0.0003 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 2026-09-15.
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