Borrowing it
Nothing to install: this file belongs to cgfixit/AzureAI-CopilotStudio-PersonalAgent-Instructions. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/cgfixit/AzureAI-CopilotStudio-PersonalAgent-Instructions/main/.claude/skills/azureAI-optimize/SKILL.mdgit clone --depth 1 https://github.com/cgfixit/AzureAI-CopilotStudio-PersonalAgent-InstructionsWrote 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/cgfixit/azureai-copilotstudio-personalagent-instructions/azureai-optimize)<a href="https://agentmods.dev/skills/cgfixit/azureai-copilotstudio-personalagent-instructions/azureai-optimize"><img src="https://agentmods.dev/badge/skills/cgfixit/azureai-copilotstudio-personalagent-instructions/azureai-optimize/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/cgfixit/azureai-copilotstudio-personalagent-instructions/azureai-optimize"><img src="https://agentmods.dev/badge/skills/cgfixit/azureai-copilotstudio-personalagent-instructions/azureai-optimize.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 | $0.00039 | $0.01737 |
| Opus 5 | $0.00019 | $0.00869 |
| Sonnet 5 | $0.00008 | $0.00347 |
| Haiku 4.5 | $0.00004 | $0.00174 |
Grade A, and why
azureAI-optimize 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 10d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimize the AzureAI-CopilotStudio-PersonalAgent-Instructions repository. This is a documentation/prompt-engineering library (no build system, no tests). Every deliverable is a Markdown file containing system instructions for enterprise AI personal agents.
Step 1: Run the analysis driver
bash .claude/skills/azureAI-optimize/analyze.sh .
Read the output carefully. It reports:
- Unfilled placeholders in
examples/(bugs — these should be filled) - Missing core sections per example (Purpose, Core Mission, Forbidden Actions, Escalation, Security, Source Hierarchy)
- Azure AI o3 reasoning protocol presence/absence per file
- README ↔ examples/ consistency (files missing from the README structure block)
- CI workflow gaps (markdown lint, link check, placeholder audit)
- Security hardening (action pinning, CODEOWNERS, branch protection)
- Domain coverage (existing vs suggested new domains)
Step 2: Pick an optimization category and execute
Based on the analysis output and the user's request, choose one or more of these categories. If the user didn't specify, pick the highest-impact items from the analysis.
A. Enhance examples for Azure AI Enterprise o3
The examples/Network&SecurityAgent.md is the reference implementation for o3 optimization — it includes a ## Reasoning Protocol (o3-Optimized) section with a structured pre-response checklist (QUERY TYPE → LAYER/DOMAIN → ENVIRONMENT ASSUMPTIONS → GROUNDING CHECK → VERSION STRICTNESS → FAILURE MODES → SELF-CRITIQUE → OUTPUT DECISION) and explicit confidence rules.
For each example flagged as ENHANCE in the analysis:
- Read
examples/Network&SecurityAgent.mdsections 2 (Reasoning Protocol) and 3 (Response Modes) as the pattern to follow. - Add a domain-appropriate
## Reasoning Protocol (o3-Optimized)section after Core Mission. Adapt the checklist dimensions to the domain (e.g., for PowerShell: EDITION → MODULE AVAILABILITY → COMPATIBILITY → ERROR HANDLING STRATEGY; for YARA: RULE TYPE → TARGET ARTIFACT → FP RISK → PERFORMANCE IMPACT). - Add explicit confidence-surfacing rules: state confidence with source, escalate below 70%.
- Add a
## Response Modestable mapping trigger phrases to output modes (Procedure, Quick Fact, Troubleshoot, etc.). - Preserve all existing content — these are additive enhancements.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 105 lines · 39 tokens per session scan A fb53d3f732f2
azureAI-optimize is a skill published in the GitHub repository cgfixit/AzureAI-CopilotStudio-PersonalAgent-Instructions (1 stars, last pushed 8d ago), licensed MIT. It adds 39 tokens to every session and 1,737 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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