Individual skill
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 CoT, reasoning type, ReAct vs CoT vs PoT, which dspy module, predict vs chain of thought.
skills/agentsop-module-shape-selection/SKILL.md
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README.mddoc · 2,109 bytesSKILL.mdcore · 17,685 bytesSKILL.mdcore · 17,685 bytesSKILL.mdcore · 17,685 bytesintermediate/operation_candidates.jsonasset · 6,039 bytesreferences/R1-source-evidence.mddoc · 4,264 bytes
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# 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]]`.
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