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name: agentsop-module-shape-selection version: 0.1.0 description: >- 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.

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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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 decision
Tool appears	A function/API/search/calculator is now available to the step

Do NOT activate when:

• The work is a one-shot prompt — just call the LM; shape ceremony has no payoff.
• You are choosing the optimizer / teleprompter (MIPROv2, GEPA, BootstrapFewShot) — that is the [[agentsop-dspy]] workflow, a later stage. Shape comes first, optimizer second.
• The step is non-LM control flow (a if, a DB read, a deterministic transform).

Shape selection is upstream of optimization. You pick the shape in Stage 1 (Programming) of the dspy SOP, before any metric or compile [dspy.ai/learn/].

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2. 核心心智模型 (Core mental model)

Reasoning shape is chosen by task structure, not by defaulting to CoT.

The lib skill shows four modules side by side and a "Best Practices" note that says "Start with Predict, add ChainOfThought if needed" [~/.claude/skills/dspy Best Practices §1]. In practice that collapses into a CoT-everywhere reflex, because "if needed" is never operationalized. This overlay operationalizes it.

A module's shape is the control-flow contract between the LM and your code:

                 does the answer need        is there a real
                 intermediate reasoning?      tool to call?
                         │                          │
   simple lookup ── no ──┤                          │
   /classify   ─────────►│ Predict                  │
                         │                          │
   analytic /   ── yes ──┤── no tool ──────────────►│ ChainOfThought
   judgement            │                          │
                         │                          │
   needs to act ─────────┼── yes, real tool ───────►│ ReAct(tools=[...])
   /look things up      │                          │
                         │                          │
   math / counting ──────┴── deterministic compute ►  ProgramOfThought
   / strict parsing                                   (code grounds answer)

Three shifts the agent must internalize:

1. The default is a probe, not a destination. "Default to CoT" means "when unsure, CoT is the safe baseline" — not "always ship CoT." Every CoT you ship that a Predict would have matched is pure token tax [dspy.ai/learn/programming/modules/].

2. Shape is structural, optimizer is statistical. Shape = which control flow (this overlay). Optimizer = which demos/instructions get baked in ([[agentsop-dspy]] §4). A wrong shape cannot be fixed by a better optimizer — MIPROv2 on the wrong shape just optimizes the wrong thing [dspy.ai/learn/optimization/overview/].

3. Each shape has a cost signature. Predict ≈ 1 call, no reasoning tokens. CoT ≈ 1 call + a reasoning/rationale field (more output tokens). ReAct ≈ N calls (a tool loop). PoT ≈ 1 LM call + code execution. Shape choice is a cost choice.

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3. SOP (Classify → Pick → Measure)

A three-step gate, run per LM-calling step (not per pipeline):

Step 1 — Classify the task structure

Answer two yes/no questions about the step's output:

• Q1: Does a correct answer require visible intermediate reasoning? (Multi-hop inference, judgement, "why", trade-off weighing → yes. Lookup, label, format-conversion → no.)
• Q2: Does producing the answer require acting — calling a tool, fetching data, or running deterministic computation? (Search/API/DB → tool. Arithmetic/counting/strict-parse → computation. Neither → no.)

Step 2 — Pick the shape from the selection card (§4)

Map the (Q1, Q2) answers straight onto the card. Do not negotiate with the reflex.

Step 3 — Measure whether the shape earns its cost

A shape is only justified if it beats the cheaper shape below it. Before shipping anything heavier than Predict:

1. Run the candidate shape and the next-cheaper shape on 5–10 hand-picked examples.
2. Diff outputs with dspy.inspect_history(n=3) [dspy.ai/learn/programming/modules/].
3. Keep the heavier shape only if it changes answers for the better. If CoT and Predict produce the same labels on a classify task, ship Predict.
4. Record the call in intermediate/operation_candidates.json so the next node reuses the reasoning instead of re-deriving it.

Exit criterion: the chosen shape produces plausible outputs on 5+ examples AND no cheaper shape matches it. Then — and only then — proceed to metric + optimizer ([[agentsop-dspy]]).

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4. 操作模型 (Selection card)

4.1 The four shapes

Task structure	Shape	DSPy module	Cost signature	Evidence
Lookup / classify / extract / format-convert (no reasoning needed)	Predict	dspy.Predict(Sig)	1 call, no reasoning tokens — lowest overhead	[dspy.ai/learn/programming/modules/], lib Predict §2
Analytic / judgement / multi-hop inference (reasoning helps, no tool)	Chain of Thought	dspy.ChainOfThought(Sig)	1 call + reasoning/rationale field — adds output tokens	[dspy.ai/learn/programming/modules/], lib ChainOfThought §2
Tool-use: search / API / DB / retrieval / calculator	ReAct	dspy.ReAct(Sig, tools=[...])	N calls — a think→act→observe loop	[dspy.ai/learn/programming/modules/], lib ReAct §2
Math / counting / unit conversion / strict parsing	Program of Thought	dspy.ProgramOfThought(Sig)	1 LM call → generated code → executed; answer grounded in execution	[dspy.ai/learn/programming/modules/], lib ProgramOfThought §2

4.2 Cost note — CoT is not free

ChainOfThought adds a generated reasoning field to every call. On a high-volume classify step (e.g. routing 100k tickets/day), that reasoning field is pure cost with zero accuracy gain if the labels don't change. The lib skill's "add CoT if needed" [lib Best Practices §1] is correct but under-specified: needed means "Step 3 measured a lift." Default to CoT when unsure; ship Predict when measured equal.

4.3 Tie-breakers and escalation

Situation	Action	Why
Math task but you trust the LM's mental arithmetic	Still prefer PoT	Code execution removes arithmetic hallucination [dspy.ai/learn/programming/modules/]
Reasoning helps AND a tool exists	ReAct (it does CoT inside the loop)	ReAct subsumes CoT when tools are present
Hard analytic case, single CoT is unstable	dspy.MultiChainComparison / dspy.majority over N CoT samples	Vote across samples — escalation, not a base shape [dspy-sop §4.2]
"Tool" is actually a pure Python function with no I/O	Inline the function; use CoT or Predict, not ReAct	A ReAct loop with a trivial deterministic helper is wasted calls (Case B)

4.4 Selection card as a one-liner

no reasoning, no tool        → Predict
reasoning, no tool           → ChainOfThought
any real tool / action       → ReAct(tools=[...])
math / count / strict parse  → ProgramOfThought

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5. 困境决策案例 (Dilemma cases)

Case A — "CoT on a simple classify wastes tokens"

困境: A pipeline routes incoming support tickets into 6 categories. The engineer's reflex was dspy.ChainOfThought("ticket -> category") because "reasoning is always safer." Volume is 100k tickets/day. Is the reasoning field earning its cost?

约束:

• The output is one of 6 fixed labels — a closed-set classification.
• Every CoT call emits a reasoning field (extra output tokens) × 100k/day.
• Accuracy target already met by a simpler shape in spot-checks (unverified).

决策步骤 (Step 3 of the SOP, made concrete):

1. Classify (Step 1): Q1 "needs visible reasoning?" → no (closed-set label). Q2 "needs a tool/compute?" → no. The card says Predict.
2. The reflex said CoT. Run both on 10 hand-picked tickets, diff with dspy.inspect_history(n=3) [dspy.ai/learn/programming/modules/].
3. If the 6 labels come out identical, the reasoning field changed nothing — it is pure token tax at 100k/day. Ship Predict.
4. If CoT flips 1–2 ambiguous edge cases correctly, keep CoT only for those — or move ambiguity handling to a second, cheap Predict triage stage.

结果: On closed-set classification, Predict typically matches CoT. The CoT-everywhere reflex would have shipped a per-call reasoning surcharge for no accuracy.

可提取的操作: A closed-set classify/lookup step defaults to Predict. Promote to CoT only after Step 3 measures a label change — never on reflex.

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Case B — "ReAct without tools is just CoT (with extra failure modes)"

困境: An engineer wants an "agentic" answer step and writes dspy.ReAct("question -> answer", tools=[]) — or with a single trivial helper that does no real I/O. Is this actually agentic?

约束:

• ReAct's value is the think → act → observe loop over real tools (search, API, DB) [dspy.ai/learn/programming/modules/, lib ReAct §2].
• With no real tool, the loop has nothing to observe; it degenerates to reasoning — i.e. CoT — but pays for loop overhead and added parsing/failure surface.

决策步骤:

1. Classify (Step 1): Q2 "needs a real tool/action?" → no (empty or trivial tools). The card routes away from ReAct.
2. Recognize that ReAct with no real tools is just CoT — the reasoning happens, but the action/observation steps are dead weight that can hang or mis-parse.
3. If reasoning genuinely helps → use dspy.ChainOfThought directly. If it doesn't → dspy.Predict.
4. Add dspy.ReAct(tools=[...]) back only when a real external capability appears (web search, retrieval, calculator API). Then ReAct subsumes CoT inside its loop.

结果: Replacing tool-less ReAct with CoT removes loop overhead and a class of tool-parsing failures while preserving the reasoning. No capability is lost because none existed.

可提取的操作: ReAct earns its loop only when at least one real, I/O-bearing tool exists. Tool-less ReAct → downgrade to CoT (or Predict).

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Case C — "Math step: trust CoT's arithmetic or ground it with PoT?"

困境: A step computes "15% of 240, then subtract the 3-item average." The reflex is ChainOfThought because it "shows the math." Is shown arithmetic correct arithmetic?

约束:

• LMs hallucinate arithmetic even when the reasoning prose looks right.
• ProgramOfThought generates and executes code, grounding the number in a real computation [dspy.ai/learn/programming/modules/, lib ProgramOfThought §2].

决策步骤:

1. Classify (Step 1): Q2 "needs deterministic computation?" → yes (math). Card → ProgramOfThought, not CoT.
2. Use dspy.ProgramOfThought("question -> answer"); it emits answer = 240*0.15 - ... and runs it [lib ProgramOfThought §2].
3. Reserve CoT for the framing ("which numbers matter") only if that itself is ambiguous — then compose: CoT to extract operands → PoT to compute.

结果: PoT removes arithmetic hallucination at the cost of one code execution. CoT on the same step ships numbers that look derived but may be wrong.

可提取的操作: Any step whose answer is a computed number/count/parse defaults to PoT. CoT's prose is not a substitute for executed code.

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6. 反模式与边界 (Anti-patterns & boundaries)

Anti-patterns

1. The CoT-everywhere reflex. Reaching for dspy.ChainOfThought on every step "to be safe." Safe ≠ free; the reasoning field is a per-call token tax. CoT is the default when unsure, not the default always (Case A) [dspy.ai/learn/programming/modules/].
2. ReAct with no real tools. A ReAct(tools=[]) or a ReAct over a trivial pure function is just CoT plus loop overhead and extra failure modes (Case B).
3. CoT prose as a math guarantee. Trusting a reasoning field's arithmetic instead of executing it. Use PoT for computed answers (Case C).
4. Picking shape by feel after writing the prompt. Shape is an upfront structural decision; choosing it post-hoc means the prompt was written against the wrong contract.
5. Conflating shape with optimizer. "MIPROv2 will fix it" — an optimizer cannot repair a wrong shape; it optimizes whatever shape you gave it [dspy.ai/learn/optimization/overview/].
6. Predict on a genuinely analytic task. Under-powering to save tokens when the task needs multi-hop reasoning — the mirror failure of the CoT reflex.
7. One shape for the whole pipeline. Each LM-calling step gets its own classify→pick; a retrieve→reason→format pipeline may be ReAct→CoT→Predict.

Boundaries (where this overlay stops)

• Optimizer / teleprompter choice (BootstrapFewShot, MIPROv2, GEPA, finetune): not here — see [[agentsop-dspy]] §4. Shape first, optimizer second.
• Signature design (field names, types, desc=): the lib skill [[dspy]] Core Concepts §1. Shape assumes the signature exists.
• Metric design and compile-cost guardrails: [[agentsop-dspy]] §4.3–4.4.
• One-shot / no-pipeline prompting: out of scope; just call the LM.
• Token-level output constraints (force-valid-JSON): Outlines/Guidance, orthogonal to shape ([[agentsop-dspy]] §7).

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7. 跨框架对照 (Cross-framework correspondence)

The shape decision is framework-independent; only the spelling changes.

Reasoning shape	DSPy module	LangChain equivalent	Raw-prompting equivalent
Predict (lookup/classify, no reasoning)	dspy.Predict(Sig)	LLMChain / direct model.invoke with a plain template	Single prompt, "answer directly" — no scratchpad
Chain of Thought (analytic, no tool)	dspy.ChainOfThought(Sig)	LLMChain with a "think step by step" prompt; no agent	"Let's think step by step…" then answer
ReAct (tool-use loop)	dspy.ReAct(Sig, tools=[...])	create_react_agent / AgentType.ZERO_SHOT_REACT_DESCRIPTION + tools	Manual Thought/Action/Observation loop you parse yourself
Program of Thought (math/parse via code)	dspy.ProgramOfThought(Sig)	PythonREPLTool agent / LLMMathChain	"Write Python to compute the answer," then exec

Reading the table: the task-structure question (reasoning? tool? compute?) is the invariant. DSPy makes the choice a one-line module swap with a stable signature; LangChain makes it an agent-type/chain choice; raw prompting makes it a scratchpad-format choice you hand-maintain. The selection rubric in §3–§4 is the same in all three columns — only the binding to code differs. This is why the overlay lives above [[dspy]]: the rubric transfers even when you leave DSPy.

Bridge to the rest of the stack: once the shape is chosen here, hand off to [[agentsop-dspy]] for metric + optimizer + compile, and to [[dspy]] for the module API, signature syntax, and LM-provider wiring.

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Source map

• DSPy module semantics & "default to ChainOfThought" line: [dspy.ai/learn/programming/modules/]
• Local source dspy-sop SKILL.md §3 (Stage 1 module pick), §4.2 (module selection table)
• Local lib skill ~/.claude/skills/dspy/SKILL.md §Core Concepts 2 (Predict/CoT/ReAct/PoT examples), Best Practices §1 ("start simple, iterate")
• Full evidence trace: references/R1-source-evidence.md
• Extracted operations: intermediate/operation_candidates.json