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stdout-delegate-tutorial
Minimal 3-step chained example verifying the stdout-delegate pattern works end-to-end
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Minimal 3-step chained example verifying the stdout-delegate pattern works end-to-end
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Build or improve agent SKILL.md files by scaffolding, iterating with worktrees, running evals, registering dependencies, and proposing splits; not for general code modules, ordinary docs, or plugin packaging.
Turn a skills repo into a multi-platform plugin (Claude Code, Codex, Cursor, Gemini, OpenCode). Use when the user wants their skills installable via `/plugin install`, loadable as a Gemini extension, or discoverable via symlink on Codex/OpenCode. Also audits existing plugins for missing manifests or adapters.
Commit the current diff with a clean conventional message — the bot picks the prefix, writes the subject, and runs `git commit`. Use when the user says "bommit", "commit this", or "just commit" without dictating the message. bommit = bot + commit.
| name | stdout-delegate-tutorial |
| description | Minimal 3-step chained example verifying the stdout-delegate pattern works end-to-end |
| version | 0.2.0 |
A 3-step chained Mode D demo. Each step emits one __LLM_DELEGATE__ directive that writes
its result to a tmp file; the next step reads that file to build on the previous answer.
python3 tutorial.py --step 1
Emits:
__LLM_DELEGATE__: {"prompt": "Name one obscure programming language. Reply with the name only.", "out": "tmp/tutorial/lang.txt"}
Write the LLM response into tmp/tutorial/lang.txt.
python3 tutorial.py --step 2
Reads tmp/tutorial/lang.txt, then emits:
__LLM_DELEGATE__: {"prompt": "Give one surprising fact about <lang> in one sentence.", "out": "tmp/tutorial/fact.txt"}
Write the LLM response into tmp/tutorial/fact.txt.
python3 tutorial.py --step 3
Reads tmp/tutorial/lang.txt and tmp/tutorial/fact.txt, then emits:
__LLM_DELEGATE__: {"prompt": "Given this fact about <lang>: '<fact>' — should a beginner learn it? One sentence.", "out": "tmp/tutorial/verdict.txt"}
Write the LLM response into tmp/tutorial/verdict.txt.
python3 tutorial.py --step 4
No directive — just prints the collected results:
=== Chain complete ===
Language : <lang>
Fact : <fact>
Verdict : <verdict>
If you see all three fields populated, the 3-step chain is working end-to-end.