agentsop-dspy
Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use r
npx skills add agentsope/SkillAlchemy --skill agentsop-dspy --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DSPy SOP — Programming, Not Prompting > *"DSPy isn't a prompt-optimization agent framework. It's the LLM compiler for the shortest, cleanest code."* > — Eito Miyamura [eito.substack.com/p/dspy-the-most-misunderstood-agent] > > *"Prompts are effectively the weights of an LLM application."* > — Core philosophy [arxiv.org/abs/2310.03714] --- ## 1. 何时激活 (When to activate) Activate this skill when **any** of the following triggers are present in the user's intent or codebase: | Trigger | Signal | |---|---| | Imports / mentions | `import dspy`, `dspy.Signature`, `dspy.ChainOfThought`, `dspy.ReAct`, `Predict`, `MIPROv2`, `BootstrapFewShot`, `GEPA`, `teleprompter`, `compile(` on an LM program | | Tasks | "auto-tune this prompt", "I want to swap GPT-4 for a smaller model without re-engineering prompts", "I have 50/200/1000 labeled examples — optimize this", "compile a pipeline for our metric", "distill GPT-4 into Llama-3-8B" | | Symptoms | Hand-written prompts grow past ~50 lines; brittleness on model swap; the team manually tunes few-shot examples; a metric exists but isn't being used to drive prompt design | | Cross-skill bridges | LangGraph node calls an LLM and needs better prompts →
- 1. 何时激活 (When to activate)
- 2. 核心心智模型 (Core mental model)
- 3. SOP 工作流 (SOP workflow)
- Stage 1 — Programming (no optimizer yet)
- Stage 2 — Evaluation (no optimizer yet)
- Stage 3 — Optimization (compile)
- When to iterate back
- 4. 操作模型 (Trigger / Action / Output / Evidence)
- 4.1 Choose the optimizer
- 4.2 Module selection
- 4.3 Metric design
- 4.4 Cost guardrails
- 5. 困境决策案例 (Dilemma cases — ≥3)
- Case A — "Optimizer cost vs gain: when is it worth compiling?"
What does the agentsop-dspy skill do?
Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use r
How do I install it?
Run `npx skills add agentsope/SkillAlchemy --skill agentsop-dspy --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From agentsope/SkillAlchemy, a repository with 255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.