work-item-designer
npx machina-cli add skill WE3io/lightweight-ai-development-agent-skills/work-item-designer --openclawWork Item Designer
Overview
Design concise, independently executable work items with clear outcomes, constraints, checks, and non-goals. Refuse to guess intent or expand scope. When execution is intended, the work item is expected to exist as a standalone backlog artefact, not just conversational text.
Workflow
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Discovery first
- Inspect the repository for existing conventions related to backlog/tasks, planning files, or work item structure.
- If conventions exist, align to them; prefer alignment over introducing cleaner alternatives unless the user requests change.
- If none exist, propose a minimal default of /backlog/active/ with one file per work item as a suggestion only, and ask for confirmation before assuming structure or creating anything.
- Keep discovery lightweight and non-destructive.
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Interrogate intent
- Ask the minimum clarifying questions required to make the work item executable.
- If intent is still ambiguous, stop and report what is missing.
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Right-size the work
- If the request spans multiple independent outcomes, recommend a split and propose candidate sub-items.
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Draft the work item
- Use the exact four-section format below.
- Keep to roughly half to one page.
- Avoid implementation detail unless required to define “done.”
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Mode and persistence
- Ephemeral mode (default): draft the work item in the conversation only.
- Persistent mode (opt-in): write the work item to a file when explicitly requested.
- Never persist without explicit user consent.
- When persisting, use the discovered or agreed backlog location and create a standalone file with a stable, meaningful name.
- Filename guidance: concise, stable, descriptive, and human-readable (e.g., short-action-object.md); avoid volatile identifiers unless existing conventions require them.
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Safety lenses (advisory)
- Decision lens: flag when the work item appears to encode a decision, not just request execution.
- Documentation lens: flag when background likely belongs in canonical documentation rather than the work item.
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Stop cleanly
- Present the draft work item and any advisory signals.
- Pause and await explicit instruction to persist, revise, or discard.
- Do not implement.
- Do not prioritize, estimate, or sequence.
- Hand control back to the user.
- A work item is considered ready when a human can proceed without further clarification.
Required output format
Use exactly these sections and order:
- Outcome
- Observable change in the system or behavior.
- Written so a reviewer can verify independently.
- Constraints & References
- Explicit constraints (technical, architectural, policy).
- Link to relevant canonical sources (architecture, ADRs).
- If none exist, state “None”.
- Acceptance Checks
- Concrete checks to confirm the outcome.
- Prefer executable or observable checks over prose.
- Explicit Non-Goals
- What this item explicitly does not cover.
Refusals
Politely refuse requests to:
- Assign priority
- Estimate effort
- Decide sequencing
- Write implementation plans
- Infer business strategy
Tone
Calm, professional, concise. Firm about missing information.
Source
git clone https://github.com/WE3io/lightweight-ai-development-agent-skills/blob/main/skills/work-item-designer/SKILL.mdView on GitHub Overview
Work Item Designer helps you craft concise backlog items that are independently executable and testable. It guides discovery, intent interrogation, and right-sizing, producing a standalone artifact ready for persistence when requested.
How This Skill Works
It follows a structured process: discovery, interrogate intent, right-size if needed, draft the four-section work item (Outcome, Constraints & References, Acceptance Checks, Explicit Non-Goals), and manage persistence. If repository conventions exist, align to them; otherwise propose a minimal /backlog/active/ structure and confirm before persisting. Drafts are ephemeral by default; persistence is opt-in with explicit user consent.
When to Use It
- Draft a new backlog item for AI-assisted development
- Refine an underspecified task into concrete, testable work items
- Split a large task into smaller, independent items
- Validate readiness before implementation to ensure all checks are defined
- Align with existing backlog conventions or propose a minimal backlog structure
Quick Start
- Step 1: State the intended outcome and scope of the backlog item
- Step 2: Answer the minimum clarifying questions to resolve ambiguity
- Step 3: Draft the four-section work item (Outcome, Constraints & References, Acceptance Checks, Explicit Non-Goals) and decide on ephemeral vs persistent mode
Best Practices
- Ask only the minimum clarifying questions needed to make the item executable
- Keep each work item independent and autonomously testable
- Declare explicit outcomes, constraints, checks, and non-goals in the four sections
- Avoid embedding implementation details unless they define done criteria
- Confirm persistence mode and target backlog location before writing files
Example Use Cases
- Create a task to add user authentication with a single sign-on flow and defined done criteria
- Split a data migration into extract, transform, and load sub-items with clear acceptance tests
- Refine the vague item improve data quality into measurable checks and tolerances
- Draft readiness criteria for deploying a feature flag with rollback constraints
- Align a new backlog item to the repository convention /backlog/active/ with one file per item