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NO.: #7dc4b0e1
Topic: AGENTS & TOOLS
Source: Simon Willison
Published: 2026-05-11 18:48:57
Importance: ★ 5/10 — radar 50
Original: simonwillison.net/2026/May/11/llm-shebang/#atom-everything

Run `llm` Directly from a Script Shebang

Plain-text prompts and YAML templates can become runnable scripts with llm. Useful for tiny automations and tool-calling helpers when shell-level speed matters.

[ KEY POINTS ]

A shebang like #!/usr/bin/env -S llm -f turns an English text file into an executable prompt, lowering friction for one-off generators.
Adding -T enables tool calls inside the script, so time lookups or other helpers can run without wrapping everything in Python.
YAML templates can define Python functions and execute them via llm -t, pushing lightweight agent behavior into a single file.
The shown calc example chains multiply and add with --td debug output, making tool execution transparent enough for quick internal utilities.

Originalsimonwillison.net/2026/May/11/llm-shebang/#atom-everythingRead original →

// related

#0001
#0001Agents & tools GitHub Changelogyesterday
GitHub adds REST API auditing for `Copilot` cloud agent repo config
60radar
Repo-level agent settings can now be checked by API instead of manual UI review. Useful for keeping automation permissions visible before cloud agents touch production code.
- New endpoint: Get Copilot cloud agent configuration for a repository, currently in public preview.
- Best fit is policy drift checks across repos: scan whether agent access and configuration match your expected defaults.
- This is governance plumbing, not a coding-speed feature. Worth adopting if Copilot agents run on real repos.
Source: github.blog/changelog/2026-05-18-audit-repository-copiloRead original →
FIG-0011:1
60radar
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#0002
#0002Agents & tools GitHub Changelogyesterday
`Copilot Spaces API` is now generally available
70radar
Copilot SpacesGitHub Copilot feature — manages task-specific context spaces
Spaces can now be managed from your own apps via API. Useful for wiring repo context into internal tools or repeatable agent workflows.
- The API supports create, read, update, and delete for Spaces, so context setup no longer has to stay inside GitHub UI.
- Good fit for templates: bootstrap a project space per repo, customer, or feature branch and keep agent context consistent.
- This is more automation surface than end-user feature. Value depends on whether Copilot Spaces is already part of the coding workflow.
Source: github.blog/changelog/2026-05-18-copilot-spaces-api-now-Read original →
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70radar
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#0003
#0003Agents & tools r/ClaudeAIyesterday
11 Claude Habits That Compound Over Daily Use
50radar
The useful part is not prompt tricks, but persistent context: Projects, CLAUDE.md, styles, skills, and subagents. Worth turning into a default setup before long coding sessions.
- Put codebase context, style guides, and prior PRs into Projects once. Re-pasting the same background is pure context tax.
- A custom style like skeptical senior engineer changes review quality by forcing pushback instead of agreeable code comments.
- In Claude Code, CLAUDE.md carries more weight than session prompts. Around 80 lines of project context can remove repeated stack explanations.
- Use cheaper/faster models by task: Sonnet as default, Opus for architecture, Haiku for batch cleanup like tickets or emails.
- Subagents fit parallel chores: run tests, inspect files, or summarize docs while the main coding thread keeps moving.
Source: www.reddit.com/r/ClaudeAI/comments/1tgqnsl/11_claude_thiRead original →
50radar
PHOTO
FIG-0031:1

Run `llm` Directly from a Script Shebang

// related

GitHub adds REST API auditing for `Copilot` cloud agent repo config

`Copilot Spaces API` is now generally available

11 Claude Habits That Compound Over Daily Use