用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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npx skills add https://github.com/aiFabricoCom/fabrico-collections-codex --skill fabrico-task-extracting命令会保持在同一行。复制前请横向滚动并检查完整内容。
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Audit AWS cost optimization and tagging compliance.
Audit GCP cost optimization and labeling compliance.
Process discovery materials into Jira-ready epics and user stories, or iterate on an existing backlog.
基于 SOC 职业分类
正在显示 SKILL.md
| name | fabrico-task-extracting |
| description | Identify and structure epics and user stories from workshop materials. |
This skill helps you identify discrete pieces of work (epics and user stories) from discovery workshop materials and structure them into a clear, business-oriented task breakdown. The output is intended for stakeholder review and eventual Jira creation — it is NOT a technical specification or implementation plan.
fabrico-architect subagent for that)fabrico-context-engineer subagent for that)Use the checklist below and track your progress:
Extraction progress:
- [ ] Step 1: Gather and review all input materials
- [ ] Step 2: Draft the intent brief
- [ ] Step 3: Review Gate 0 with the user and approve the intent brief
- [ ] Step 4: Identify high-level work streams (epics)
- [ ] Step 5: Break down epics into user stories
- [ ] Step 6: Write business-oriented descriptions with source traceability
- [ ] Step 7: Map dependencies between tasks
- [ ] Step 8: Identify assumptions and out-of-scope items
- [ ] Step 9: Flag ambiguities and ask clarifying questions
- [ ] Step 10: Present task list for user validation
- [ ] Step 11: Save the intent brief and extracted tasks documents
Step 1: Gather and review all input materials
Collect and thoroughly review all available workshop materials:
workshop-context-summary.md (if available): Explore Mode summary that captures business context, likely epics, and ambiguities before extractioncleaned-transcript.md): Primary source — review all discussion topics, decisions, action items, and open questionsfabrico-codebase-analysing skill to understand what already exists and what needs to be builtspecifications/projects/<project-name>/task-baseline.md, if present): Review for continuity, overlap, and existing backlog contextCreate a mental model of the full scope discussed during the workshop before proceeding to extraction.
Step 2: Draft the intent brief
Before extracting tasks, draft an intent-brief.md that captures the business intent of the workshop. Keep it concise and focused on scope decisions, not backlog detail.
Include at least:
If the user requested Explore Mode, this draft may be accompanied by a workshop-context-summary.md, but it still must not become a backlog artifact.
Step 3: Review Gate 0 with the user and approve the intent brief
Present the intent brief to the user for validation before backlog extraction begins. Ask whether the scope, exclusions, and candidate epics are correct. Iterate until the user approves.
This is Gate 0. Do not proceed to extraction until the user approves the intent brief.
Step 4: Identify high-level work streams (epics)
From the gathered materials, identify distinct work streams that represent major deliverables or feature areas:
For each epic, draft:
Step 5: Break down epics into user stories
For each epic, identify the individual user stories that compose it:
Step 6: Write business-oriented descriptions with source traceability
For each user story, write:
GIVEN / WHEN / THEN scenariosGIVEN / WHEN / THEN statement is not sufficientImportant: Keep descriptions in business language. Avoid implementation jargon. The goal is for any stakeholder to understand what will be delivered without technical knowledge.
Step 7: Map dependencies between tasks
Identify relationships between epics and stories:
Use clear notation (e.g., "Story 1.2 is blocked by Story 1.1") in the dependencies section.
Step 8: Identify assumptions and out-of-scope items
Document:
Step 9: Flag ambiguities and ask clarifying questions
Review all extracted tasks and identify:
Ask the user directly to clarify these items with the user. Ask exactly one question per focused question. Each question must clearly identify the specific epic or story it relates to — include the story identifier and title in the question header and context (e.g., "[Epic: User Auth > Story 1.2: User can log in] The transcript mentions SSO but the Figma shows email/password only. Which scope is correct?"). This ensures each popup is self-contained and the user can focus on one decision at a time.
Step 10: Present task list for user validation
Present each story to the user individually for validation using one focused question per story. Each question should include the story's full context: parent epic title, story title, and a brief summary of the acceptance criteria. Ask: "Is this story correct? Should it be split, merged, modified, or removed?"
After presenting all stories, ask one final workflow-level question: "Did I miss any tasks that should be added?"
Iterate based on feedback until the user approves the task list.
This is Review Gate 1 — the user must approve the task list before proceeding to Jira formatting.
Step 11: Save the intent brief and extracted tasks documents
Generate the final outputs following the ./intent-brief.example.md and ./extracted-tasks.example.md templates.
Save intent-brief.md and extracted-tasks.md to specifications/<workshop-name>/.
The extracted tasks document must preserve source traceability in each story and keep acceptance criteria in business-friendly scenario form.
fabrico-transcript-processing - provides the cleaned transcript used as primary inputfabrico-codebase-analysing - for understanding existing system context when analyzing scopefabrico-task-analysing - for business-context exploration, baseline comparison, and intent brief drafting before extraction