agentsop-module-shape-selection
ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. The local `dspy` skill lists the modules but never surfaces the *selection criterion*: reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM control flow. Search keywords: chain of thought vs ReAct, when to use
npx skills add agentsope/SkillAlchemy --skill agentsop-module-shape-selection --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.
# M2 — Module-Shape Selection (CoT / ReAct / PoT / Predict) > *"Pick the lowest-power Module that works. Default to ChainOfThought."* > — DSPy docs [dspy.ai/learn/programming/modules/] > > This overlay sharpens that line into a **rubric**: the default is not a law. The > shape is a function of the *task structure*, and CoT is only one of four answers. This is an **enhancement overlay**. It assumes the [[dspy]] library skill is loaded (it provides `dspy.Predict`, `dspy.ChainOfThought`, `dspy.ReAct`, `dspy.ProgramOfThought` APIs and install). This file adds only the *decision* the lib skill leaves implicit. Cross-link: [[dspy]], and the optimizer SOP `[[agentsop-dspy]]`. --- ## 1. 何时激活 (When to activate) Activate the instant you are about to **add or wrap an LM-calling step**: | Trigger | Signal | |---|---| | New node | A LangGraph/CrewAI node body, or a `forward()` line, is about to call an LM | | New `dspy.<Module>(Sig)` | You are typing `dspy.ChainOfThought(...)` on reflex — stop and run the rubric | | Refactor | An existing `Predict` "feels weak" or a `ChainOfThought` "feels wasteful" | | Pipeline growth | A multi-stage program adds a stage; each stage needs its own shape decisio
- 1. 何时激活 (When to activate)
- 2. 核心心智模型 (Core mental model)
- 3. SOP (Classify → Pick → Measure)
- Step 1 — Classify the task structure
- Step 2 — Pick the shape from the selection card (§4)
- Step 3 — Measure whether the shape earns its cost
- 4. 操作模型 (Selection card)
- 4.1 The four shapes
- 4.2 Cost note — CoT is not free
- 4.3 Tie-breakers and escalation
- 4.4 Selection card as a one-liner
- 5. 困境决策案例 (Dilemma cases)
- Case A — "CoT on a simple classify wastes tokens"
- Case B — "ReAct without tools is just CoT (with extra failure modes)"
What does the agentsop-module-shape-selection skill do?
ENHANCE overlay on [[dspy]] — the upfront rubric for choosing a reasoning SHAPE (Predict / ChainOfThought / ReAct / ProgramOfThought) BEFORE you write a prompt or pick an optimizer. The local `dspy` skill lists the modules but never surfaces the *selection criterion*: reasoning shape is chosen by task structure, not by reflexively defaulting to CoT. Activate every time a new LM-calling node/step is added to a pipeline. Do NOT activate for one-shot prompts, optimizer/teleprompter choice (that is the dspy SOP's job), or non-LM control flow. Search keywords: chain of thought vs ReAct, when to use
How do I install it?
Run `npx skills add agentsope/SkillAlchemy --skill agentsop-module-shape-selection --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.