swe-method
Skill0 starsChanged 6 months agoMITView on GitHub
Guide for working through a Jira story efficiently by separating context gathering, immutable evidence collection, codebase exploration, human-in-the-loop specification refinement, execution planning, and implementation. Use when turning a ticket into a reliable engineering workflow instead of jumping straight to code.
In this repository13 other files it gives its agents.
- change-consistencySkill
- engineer-agentic-aiSkill
- format-markdownSkill
- information-representation-designSkill
- living-artifactsSkill
- messaging-frameworkSkill
- python-testingSkill
- relevance-first-information-designSkill
- reviewable-branch-diffsSkill
- review-ready-codeSkill
- securitySkill
- software-designSkill
- thoughts-to-artifactSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
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 8
Use this workflow to work through a Jira story with enough context and validation to avoid speculative implementation.
- Distinctive: line 48
Use the agent's question flow to extract one decision at a time and store the clarified specification in a spec file or spec folder.
- Distinctive: line 67
Only after assumptions are validated should tests be written against those facts.
- Distinctive: line 72
Use the observed shape to define a test case.
≈ “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
SWE Method
Use this workflow to work through a Jira story with enough context and validation to avoid speculative implementation.
Step 1: Define Context
Bring together the resources you already know about up front.
Specification
- Jira ticket ID
Additional Context Index
- Slack thread URLs
- Session IDs from AI-tool interactions
- Meeting transcripts
- Personal notes
- Parent, related, and child tasks
- Existing branches and PRs
Step 2: Fetch Data Into An Immutable Layer
Gather the source material you will reason from and keep it separate from interpretation so later steps are grounded in facts rather than memory.
Step 3: Explore Codebase And Systems
Based on the specification and additional context, inspect the real repositories, branches, PRs, APIs, schemas, CLIs, and surrounding systems that the work depends on.
Step 4: Human-In-The-Loop Specification Refinement
Rules:
- The bigger the task, the harder it is to disambiguate everything up front.
- The longer the autonomous run, the larger the divergence risk from the engineer's intended outcome.
Sizing guidance:
- Tiny and repetitive tasks: the HIL step can often be skipped and decisions encoded in a skill.
- Small tasks: do a lightweight HIL planning step, then automate implementation.
- Larger tasks: break the work into smaller subtasks and refine each before execution.
Use the agent's question flow to extract one decision at a time and store the clarified specification in a spec file or spec folder.
Question types can include:
- Requirements that depend on stakeholders or teammates
- Design decisions the engineer can make or escalate
Step 5: Execution Plan
The specification is about what to build. The execution plan is about how to build it without hallucinating missing facts.
In brownfield systems, especially when databases, APIs, CLIs, or SDKs are involved, do not let the agent assume schemas or response shapes. Validate them first.
Execution plans may include HIL steps that expose the agent to:
- Real API responses
- Real DB schemas
- Real CLI or SDK behavior
Only after assumptions are validated should tests be written against those facts.
Example execution plan for a class change:
- Query the API or DB the class depends on.
- Use the observed shape to define a test case.
- Implement until the test passes.
- Refactor and simplify.
- Update docs.
Step 6: Implementation
Two workable paradigms:
- Implement a slice, run it end to end against the real system, troubleshoot until it behaves correctly, then add tests to lock it down.
- Validate real external behavior first, then do TDD using tests grounded in factual responses rather than imagined ones.
Alternate implementation with cleanup so code quality and conventions are applied continuously rather than deferred to the end.
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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.

