Borrowing it
Nothing to install: this file belongs to rajbos/ai-engineering-fluency. 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/rajbos/ai-engineering-fluency/main/.github/prompts/sync-automatic-tools.prompt.mdgit clone --depth 1 https://github.com/rajbos/ai-engineering-fluencyWrote 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/commands/rajbos/ai-engineering-fluency/sync-automatic-tools)<a href="https://agentmods.dev/commands/rajbos/ai-engineering-fluency/sync-automatic-tools"><img src="https://agentmods.dev/badge/commands/rajbos/ai-engineering-fluency/sync-automatic-tools/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/commands/rajbos/ai-engineering-fluency/sync-automatic-tools"><img src="https://agentmods.dev/badge/commands/rajbos/ai-engineering-fluency/sync-automatic-tools.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 | $0.00016 | $0.01606 |
| Opus 5.5 | $0.00006 | $0.00642 |
| Sonnet 5.5 | $0.00003 | $0.00321 |
| Haiku 4.5 | $0.00002 | $0.00161 |
Grade A, and why
sync-automatic-tools 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 yesterday.
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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Automatic Tool Classification
Audit src/automaticTools.json against src/toolNames.json to ensure every tool is classified correctly. The classification directly affects fluency/maturity scoring in the extension — tools in automaticTools.json are considered automatic (Copilot invokes them on its own) and are excluded from the user's intentional tool usage score. Tools that are not in that list are counted as intentional (the user deliberately enables or configures them).
Classification Rules
Apply the rules below in priority order. When a tool matches multiple rules, the first matching rule wins.
Rule 1 — MCP tools are ALWAYS intentional (never automatic)
Any tool whose ID starts with mcp_, mcp., or mcp__ is a user-configured external MCP server tool. These must never appear in automaticTools.json.
- Examples:
mcp_github_search_code,mcp_io_github_git_create_pull_request,mcp__workspace__web_fetch - Reason: the user must explicitly install and configure an MCP server; Copilot does not add them autonomously.
Rule 2 — Write / modify / execute tools are intentional
Tools that write to disk, run commands, install packages, or otherwise have side effects outside the AI context window are intentional:
| Category | Examples |
|---|---|
| File editing | edit_file, edit_files, copilot_editFiles, copilot_writeFile, copilot_applyPatch, copilot_replaceString, copilot_multiReplaceString, insert_edit_into_file, replace_string_in_file, multi_replace_string_in_file, write, edit, multiedit, apply_patch |
| File creation/deletion | create_file, create_directory, remove_file, copilot_createFile, copilot_createDirectory |
| Terminal execution | run_in_terminal, run_build, run_command_in_terminal, copilot_runInTerminal, run_vscode_command, copilot_runVscodeCommand, kill_terminal, run_task, create_and_run_task, copilot_createAndRunTask |
| Test / notebook execution | runTests, copilot_runTests1, run_notebook_cell, copilot_runNotebookCell, copilot_editNotebook |
| Package / environment management | install_extension, copilot_installExtension, install_python_packages, create_virtual_environment, configure_python_environment |
| Symbol refactoring | vscode_renameSymbol |
| Browser interaction | click_element, navigate_page, open_browser_page, type_in_page, run_playwright_code |
| Todo writing | todowrite, TodoWrite, manage_todo_list |
| Web fetching | fetch_webpage, copilot_fetchWebPage, webfetch, websearch, WebSearch, vscode-websearchforcopilot_webSearch |
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.
- yesterday First seen · 85 lines · 16 tokens per session scan A c002492f1587
sync-automatic-tools is a command published in the GitHub repository rajbos/ai-engineering-fluency (119 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 1,606 once invoked, about $0.0001 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 2026-10-26.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.