apply-oopsi-model
Skill4 starsChanged 3 months agoMITView on GitHub
WHEN/WHERE/WHO: [Scheduling: Business analysts, testers, or agents exploring user journeys or data workflows.] HOW: [Structural: Use this SKILL to work backward from the business Outcome, to tangible Outputs, to the Process, down to Scenarios and exact Inputs.] WHY: [Scheduling: Starting with inputs obscures the business goal. Starting with Outcome guarantees alignment.]
In this repository61 other files it gives its agents.
- activate-personalSkill
- analyze-with-tactics-questionnairesSkill
- apply-agile-testing-quadrantsSkill
- apply-architectural-tacticsSkill
- apply-attribute-driven-designSkill
- apply-fundamentals-of-data-visualizationSkill
- apply-screenplay-patternSkill
- apply-specification-by-exampleSkill
- blog-topic-discoverSkill
- build-agent-memory-systemsSkill
- build-business-objectives-modelSkill
- build-data-dictionarySkill
- build-feature-treeSkill
- comment-alive-test-driven-developmentSkill
- conduct-event-stormingSkill
- create-ecosystem-mapSkill
- create-living-documentationSkill
- create-skill-from-bookSkill
- CREATING-SKILL-TEMPLATESkill
- define-architectural-driversSkill
- define-bounded-contextsSkill
- define-ubiquitous-languageSkill
- design-agentic-screenplay-modelSkill
- design-agent-perception-layersSkill
- design-agent-planningSkill
- design-agent-reward-functionsSkill
- design-agents-using-patternsSkill
- design-architecture-viewpointsSkill
- design-autonomous-ai-interfacesSkill
- design-ddd-aggregatesSkill
- design-tool-use-sandboxingSkill
- document-architectural-decisionsSkill
- document-legacy-codebaseSkill
- doc-with-usage-exampleSkill
- draft-srs-documentSkill
- elicit-requirements-modelsSkill
- evolve-skill-with-wikiSkill
- extract-business-rulesSkill
- facilitate-example-mappingSkill
- facilitate-user-story-mappingSkill
- implement-agent-observabilitySkill
- implement-agent-reflectionSkill
- improve-existing-skillSkill
- improve-user-storySkill
- managing-ai-projectsSkill
- orchestrate-multi-agent-collaborationSkill
- prioritize-requirementsSkill
- review-c4-modelSkill
- review-data-engineeringSkill
- save-as-skillSkill
- scaffold-cmake-cxx-sanitize-gtest-envSkill
- skill-commonSkill
- task-commit-triggerSkill
- test-case-with-demoSkill
- test-case-with-readmeSkill
- test-driven-developmentSkill
- UnitTesting-convert2CaTDDSkill
- update-my-resumeSkill
- update-spec-contextSkill
- validate-requirements-criteriaSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 13
When this skill is activated, first load skill-common and apply the shared conventions it defines: canonical section order, frontmatter …
- Distinctive: line 47
Transfer Check: Verify the workflow still works for planning sessions, requirement reviews, and workflow stabilization conversations.
- Distinctive: line 48
Stop Rule: If the required outcome cannot be stated clearly, stop and ask before specifying scenarios or inputs.
- Distinctive: line 55
If the input text is a non-functional requirement (e.g., "System must support 1000 TPS"), stop and inform the user that OOPSI is designed …
- Distinctive: line 84
Verify that every Scenario maps to at least one Output.
- Distinctive: line 85
Verify that the Inputs provide enough data to execute the Process.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Apply OOPSI Model
Common Contract (Load First)
When this skill is activated, first load skill-common and apply the shared conventions it defines: canonical section order, frontmatter rules, anti-pattern guidance, and the Review In Mind loop in review-in-mind. This skill adds only domain-specific rules below.
Who
Business Analysts, Testers, Developers, and AI Agents. The agent uses this skill to deconstruct complex workflows systematically.
What
This skill implements the OOPSI model (Outcome, Outputs, Process, Scenarios, Inputs). It is a reverse-engineering approach to requirements discovery:
- Outcome: What is the high-level business goal?
- Outputs: What tangible artifacts or system state changes prove the outcome was achieved?
- Process: What steps must be taken to generate those outputs?
- Scenarios: What are the edge cases, business rules, or alternative flows in that process?
- Inputs: What exact data is required to trigger those scenarios?
When
Invoke this skill when exploring a new Epic, User Story, or complex data flow. Trigger phrases include: "use OOPSI," "work backward from the outcome," "what are the outputs of this feature," or "deconstruct this workflow."
Where
Applies to Backlog Refinement, Three Amigos sessions, and test planning.
Why
When teams start by defining inputs (e.g., "Given a user with ID 1234"), they often get bogged down in technical setup and lose sight of the business value. By starting at the Outcome, the team aligns on why they are doing the work, and the Inputs become merely the data required to prove the Outcome.
Inputs
- A User Story, Epic, or high-level feature description.
Output (Logical Evidence)
- A completed OOPSI framework document.
Optimization Readiness
- Failure Signals: The workflow starts from inputs instead of outcomes, scenarios do not map to outputs, or the business outcome becomes blurred by implementation details.
- Evidence To Collect: Outcome statements, output lists, process steps, scenario sets, and example inputs showing where the model clarified business behavior.
- Safe Mutation Boundaries: Refine framing prompts, outcome/output wording, scenario guidance, and input-definition cues without changing the core outcome-first modeling structure.
- Acceptance Criteria: Accept revisions only if each scenario maps to tangible outputs and the chosen inputs are sufficient to execute the process.
- Rejected Revision Handling: Record input-first framing, vague outcomes, and unsupported scenario expansions so they are not repeated.
- Transfer Check: Verify the workflow still works for planning sessions, requirement reviews, and workflow stabilization conversations.
- Stop Rule: If the required outcome cannot be stated clearly, stop and ask before specifying scenarios or inputs.
Constraints (Logical Boundaries)
- Outputs must be tangible and verifiable (e.g., "A confirmation email is sent," "Database record is updated"). "The user feels happy" is an Outcome, not an Output.
- Inputs should be specific data values required to execute the Scenarios.
One More Thing
If the input text is a non-functional requirement (e.g., "System must support 1000 TPS"), stop and inform the user that OOPSI is designed for functional workflows and data processing.
How (Structural Workflow)
Phase 1: Define Outcome and Outputs
- Identify the Outcome (the business goal of the feature).
- List the tangible Outputs that result from a successful (or failed) execution of this feature.
Phase 2: Map the Process
- Outline the Process—the high-level steps or workflow required to get from start to finish.
Phase 3: Identify Scenarios
- Brainstorm the Scenarios—what are the different paths through the process? What are the edge cases or business rules?
Phase 4: Specify Inputs
- For each scenario, define the exact Inputs (data tables, API payloads, or preconditions) needed to trigger that scenario.
Resources
- OOPSI Framework
Review In Mind (ReviewInMindGenie)
Execute the common review loop in review-in-mind before delivering.
Review lens for this skill:
- Does the chain run Outcome → Outputs → Process → Scenarios → Inputs without skipping levels?
- Are outputs observable and scenarios concrete enough to write tests?
- Are inputs complete and free of unstated assumptions?
Validation
- Verify that every Scenario maps to at least one Output.
- Verify that the Inputs provide enough data to execute the Process.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

