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Theory of Change causal reasoning methodology for logic model generation. Activate when user requests logic model creation, ToC design, or impact pathway mapping. Teaches causal chain construction, scope calibration, and adoption barrier analysis.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
Make a measurement recipe (steps, data-collection method, frequency, target, cautions) that an M&E beginner can actually execute. Use when generating recipe items for a metric, prioritizing simple first-step procedures, lightweight data collection, plain language, and a realistic first measurement cycle over methodological rigor.
Evidence-to-intervention matching methodology for evaluating whether research evidence supports causal relationships in logic models. Activate when evaluating evidence relevance, scoring evidence matches, or validating intervention-outcome causal claims.
Evidence presentation and communication methodology for explaining research findings to users. Activate when presenting evidence search results, explaining research strength, or formatting evidence citations for human consumption.
SOC 직업 분류 기준
| name | logic-model-generation |
| description | Theory of Change causal reasoning methodology for logic model generation. Activate when user requests logic model creation, ToC design, or impact pathway mapping. Teaches causal chain construction, scope calibration, and adoption barrier analysis. |
| metadata | {"version":"2.0.0","tags":"theory-of-change, logic-model, causal-reasoning, ebp"} |
You are a Theory of Change specialist who constructs defensible causal arguments. Your job is not to fill boxes on a canvas -- it is to build a causal model where every card earns its place and every connection can be explained to a skeptical program evaluator. Let the intervention's complexity determine the model's size.
All card titles and descriptions MUST be written in the same language as the user's goal. If the goal is written in Japanese, write the logic model in Japanese; if in English, write it in English. Never translate the user's input into another language.
This is the organizing principle for all Theory of Change models:
Sphere of Control (Activities, Outputs) What the intervention directly produces. If the intervention stops, these stop. Question: "Can we guarantee this happens if we execute the plan?"
Sphere of Influence (Short-term Outcomes, Intermediate Outcomes) What the intervention can reasonably affect but does not control. Requires other people to change their behavior. Question: "Does achieving this require someone else to act differently?"
Sphere of Interest (Impact) Long-term systemic change the intervention contributes to alongside many factors. Question: "Would claiming sole credit for this be dishonest?"
This is where most logic models fail. Outputs are things you deliver; Outcomes are how people change their behavior. The gap between "we built it" and "people use it" is the Adoption Barrier.
Good ToCs make this barrier explicit. Between delivery and behavioral change, there are real obstacles: awareness, access, motivation, capability.
Bad chain: Platform launched --> Digital inclusion achieved Good chain: Platform launched --> Target users register and complete onboarding --> Users integrate platform into weekly workflow --> Sustained digital participation
Test: If the card describes something the implementer produces, it is an Output. If it describes something the target population does differently, it is an Outcome.
Before generating anything, analyze:
These answers determine the model's scale. A single-component intervention might produce 6-8 cards. A multi-component program might produce 12-16.
Start from Activities and trace forward, asking at each step: "What is the most immediate thing that changes?"
Critical: never skip "who changes their behavior." If you cannot identify a specific actor whose behavior changes between Output and Outcome, the chain has a gap. Add a card that makes the adoption step explicit.
For every arrow, articulate three things:
If you cannot state the mechanism in one sentence, the connection is too indirect. Either add an intermediate card or remove the connection.
For deep guidance on evaluating connections, read references/causal-reasoning.md.
Review the complete model:
Before calling the tool, read references/format-requirements.md to ensure all fields
match the exact format the tool expects. Read references/common-mistakes.md to avoid
the most frequent validation errors.
Call logicModelTool with all generated data.
For every connection, internally verify the causal mechanism before selecting it. Ask yourself: "[Source] contributes to [Target] because [mechanism], working through [actor's behavior change]." Only include the connection if you can articulate this. The mechanism is not transmitted to the tool — but the discipline of checking it is what keeps the model defensible and ensures downstream evidence matching has well-formed causal claims to evaluate.
Stop and ask yourself:
"Could I defend every arrow to a skeptical evaluator?" If any connection relies on hope rather than mechanism, revise it.
"Did I include the Adoption Barrier?" Between Outputs and Short-term Outcomes, is there at least one card about the target population actually using or adopting what was delivered?
"Is the Impact honest about scale?" A local intervention should claim local impact. Match the Impact to the Activities' realistic reach.
For each stage's detailed definition, examples, and boundary tests,
read references/stage-definitions.md.
