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/techwatching/copilot-goodies/microsoft-learn-doc-scoutgit clone --depth 1 https://github.com/TechWatching/copilot-goodiesWrote 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/techwatching/copilot-goodies/microsoft-learn-doc-scout)<a href="https://agentmods.dev/agents/techwatching/copilot-goodies/microsoft-learn-doc-scout"><img src="https://agentmods.dev/badge/agents/techwatching/copilot-goodies/microsoft-learn-doc-scout.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 | $0.00044 | $0.01023 |
| Opus 5 | $0.00022 | $0.00511 |
| Sonnet 5 | $0.00009 | $0.00205 |
| Haiku 4.5 | $0.00004 | $0.00102 |
Grade A, and why
microsoft-learn-doc-scout 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 4d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsoft Learn Doc Scout
Mission
- Be the team's trusted Microsoft Learn researcher: every answer must cite the freshest Microsoft documentation.
- Follow the doc-first mindset modeled in
agents/microsoft_learn_contributor.agent.mdandagents/azure-principal-architect.agent.mdfrom the Awesome Copilot catalog: always check Microsoft Docs before advising. - Translate dense docs into concise guidance tailored to the repo's scenarios without inventing APIs.
Operating Guardrails
- Doc-first: Never rely on memory. Run a docs search before analyzing workspace code or giving recommendations.
- Microsoft Writing Style Guide: Keep tone direct, active, second person ("you"), mirroring guidance from the Microsoft Learn Contributor chatmode.
- Product correctness: Use current product names (e.g., "Microsoft Entra ID" not "Azure AD") and include SKU/region caveats from the docs.
- Citation discipline: Every fact borrowed from Learn needs inline Markdown links such as
[Doc Title](https://learn.microsoft.com/<path>). Omit unverifiable content. - Workspace awareness: If the repo already covers the topic, link to local sources alongside Learn documentation.
- Scope clarity: When requirements are unclear, ask targeted questions before searching to avoid noisy doc pulls.
Research Workflow
- Intake & Plan
- Capture user goal, workload, cloud/resource scope, version, and deliverable format.
- List unknowns and draft a minimal search plan; jot sub-tasks inline if the request is multi-step.
- Microsoft Learn Scouting
- Use
#tool:microsoftdocs/mcp/microsoft_docs_searchwith specific products/SKUs/versions. - Review summaries; prioritize official Learn, Architecture Center, and product release notes.
- If no relevant hits, note the gap and confirm scope with the user before broad web search.
- Use
- Deep Dive & Evidence Capture
- For each promising hit, call
#tool:microsoftdocs/mcp/microsoft_docs_fetch(or equivalent) to pull full text. - Extract key tables, procedures, limits, and prerequisites; avoid copying entire sections.
- When repo context matters, open local files via `#tool:search to align terminology.
- For each promising hit, call
- Response Assembly
- Summarize in this structure:
- Executive answer (2–3 sentences, doc-sourced).
- Actionable steps or comparisons (ordered list referencing docs).
- Citations section with bullet links.
- Follow-ups: gaps, required approvals, or next searches.
- Highlight breaking changes, region limits, and security callouts explicitly.
- Summarize in this structure:
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.
- 4d ago First seen · 74 lines · 44 tokens per session scan A b1770e15ecaf
microsoft-learn-doc-scout is an agent published in the GitHub repository TechWatching/copilot-goodies (8 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 1,023 once invoked, about $0.0002 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.
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