ouroboros-test-plan-author
Skill3 starsChanged 6 months agoMITView on GitHub
Create intent-based Ouroboros desktop E2E Markdown test plans from a high-level goal, feature, module, bug, PRD, ticket, or UI area. Use when asked to generate, draft, author, improve, or expand files under test-plan/ for Agent Browser desktop testing.
In this repository18 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-browserSkill
- ai-sdkSkill
- electron-scaffoldSkill
- electronSkill
- grill-meSkill
- ou-features-auditSkill
- ou-full-code-reviewSkill
- ouroboros-intent-e2eSkill
- prd-to-ticketsSkill
- team-implement-ticketsSkill
- ai-sdkSkill
- grill-meSkill
- meta-thinkingSkill
- ou-full-code-reviewSkill
- prd-to-ticketsSkill
- refactor-agents-mdSkill
- team-implement-ticketsSkill
The file
We split this skill into the rules it gives an agent
12 rules. About 0 are common, 8 are distinctive, and 4 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈8
Distinctive · estimate
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 8
Use this skill to turn a high-level feature, module, bug, or goal into one or more executable Markdown charters under test-plan/.
- Very specific: line 14
Search relevant renderer components, stores, protocol types, existing E2E specs, and existing test-plan/*.md.
- Distinctive: line 15
Prefer current labels, UI names, domain language, and mock data patterns from the codebase.
- Very specific: line 19
Write the Markdown plan in test-plan/<kebab-case-name>.md.
- Distinctive: line 20
Keep the plan intent-based:
- Distinctive: line 22
Avoid brittle CSS selectors, implementation details, or Playwright-style commands.
- Distinctive: line 24
If the plan needs mock data, name the required scenario shape in plain language.
- Distinctive: line 25
Do not invent large fixture JSON unless the user explicitly asks for it.
- Very specific: line 69
Use p0 for release-blocking smoke flows.
- Distinctive: line 71
Prefer stable human-facing terms: accessible labels, menu names, panel titles, visible messages.
- Distinctive: line 73
Keep each plan short enough for an agent to execute in one focused run.
- Distinctive: line 77
After writing or updating a plan, report:
≈ “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
Ouroboros Test Plan Author
Use this skill to turn a high-level feature, module, bug, or goal into one or
more executable Markdown charters under test-plan/.
Workflow
- Ground the plan in the repo before writing:
- Search relevant renderer components, stores, protocol types, existing E2E specs, and existing
test-plan/*.md. - Prefer current labels, UI names, domain language, and mock data patterns from the codebase.
- Search relevant renderer components, stores, protocol types, existing E2E specs, and existing
- Decide plan granularity:
- One plan for one user-centered workflow.
- Split broad modules into multiple charters only when the workflows have different setup, risks, or pass/fail oracles.
- Write the Markdown plan in
test-plan/<kebab-case-name>.md. - Keep the plan intent-based:
- Describe what the user is trying to accomplish.
- Avoid brittle CSS selectors, implementation details, or Playwright-style commands.
- Include enough observable outcomes for an Agent Browser runner to judge pass/fail.
- If the plan needs mock data, name the required scenario shape in plain language.
- Do not invent large fixture JSON unless the user explicitly asks for it.
- State when missing mock data should produce
INCONCLUSIVE.
Required Plan Shape
# Title Case Plan Name
## Mission
One or two sentences describing the user workflow and why it matters.
## Priority / Tags
p0, smoke, desktop, feature-name
## Initial State
- Fresh Electron user data directory, unless persistence is the subject.
- Mock CLI scenario requirements.
- Any required workspace, dialog response, policy response, or seeded data.
## Intent Steps
1. User-centered action.
2. Observable action or navigation.
3. Verification step.
## Expected Outcomes
- Concrete visible UI state.
- Relevant persisted/logged behavior when observable through test logs.
- Recovery or closing behavior where appropriate.
- No uncaught renderer error.
## Evidence Required
- Screenshot names or moments worth capturing.
- Console and page error output.
- Any mock CLI log evidence worth checking.Quality Bar
- Use
p0for release-blocking smoke flows; usep1for important surfaces; usep2for edge cases and exploratory coverage. - Make each expected outcome externally observable from the running app, screenshots, logs, or console output.
- Prefer stable human-facing terms: accessible labels, menu names, panel titles, visible messages.
- Include negative or recovery checks for risky flows: cancellation, empty states, disabled actions, denied approvals, failed config, or restart.
- Keep each plan short enough for an agent to execute in one focused run.
Output
After writing or updating a plan, report:
- The file path.
- The workflow covered.
- Any mock scenario assumptions.
- The exact command to dry-run and execute it:
bun run test:intent:e2e -- test-plan/<name>.md --dry-runbun run test:intent:e2e -- test-plan/<name>.md
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.

