Agent skill

Tsh Task Extracting

by TheSoftwareHouse in TheSoftwareHouse/copilot-collections

Identify and structure epics and user stories from workshop materials (cleaned transcripts, Figma designs, codebase analysis, and other documents).

MITAuto-check passedProduct & Project Management

Install Tsh Task Extracting

skills CLI
$ npx skills add TheSoftwareHouse/copilot-collections --skill tsh-task-extracting -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install TheSoftwareHouse/copilot-collections tsh-task-extracting --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/TheSoftwareHouse/copilot-collections.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/tsh-task-extracting .claude/skills/tsh-task-extracting && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
tsh-task-extracting
GitHub stars
284
Token cost
~2.5k tokens
SKILL.md length
1,215 words
Files
3
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

Identify and structure epics and user stories from workshop materials (cleaned transcripts, Figma designs, codebase analysis, and other documents).

  • Tasks that involve User stories
  • SKILL.md covers What This Skill Produces, What This Skill Does NOT Produce, Task Extraction Process and Connected Skills
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Task breakdown

What it does

Tsh Task Extracting is an agent skill from TheSoftwareHouse/copilot-collections. Identify and structure epics and user stories from workshop materials (cleaned transcripts, Figma designs, codebase analysis, and other documents). Produces a business-oriented task breakdown with dependencies, assumptions, and open questions.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `extracted-tasks.example.md` and `intent-brief.example.md`).

It sits in Product & Project Management, covering User stories, Task breakdown and Codebase onboarding. It works with Figma. The repository describes itself as: Opinionated AI-enabled workflows for product engineering. The licence is MIT.

When your agent uses it

  • Tasks that involve User stories
  • Tasks that involve Task breakdown
  • Tasks that involve Codebase onboarding

Example prompts

  • “/tsh-task-extracting”

What it can do on your machine

Read from SKILL.md and the folder at commit 2fbe51e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Tsh Task Extracting loads about 2.5k tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 1,215 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TheSoftwareHouse/copilot-collections at commit 2fbe51e, republished under its MIT licence (© TheSoftwareHouse). 1,215 words, ~2,474 tokens.

Download SKILL.mdSave it as .claude/skills/tsh-task-extracting/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
tsh-task-extracting
description
Identify and structure epics and user stories from workshop materials (cleaned transcripts, Figma designs, codebase analysis, and other documents). Produces a business-oriented task breakdown with dependencies, assumptions, and open questions.
user-invocable
false

Task Extraction

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.

What This Skill Produces

  • Intent brief: A short business-first scope brief that is approved before extraction begins
  • Epics: High-level work streams with business descriptions and success criteria
  • User Stories: Discrete deliverables in "As a… I want… So that…" format with source traceability and scenario-based acceptance criteria
  • Dependencies: Relationships between epics and stories
  • Assumptions & Open Questions: Gaps that need stakeholder input

What This Skill Does NOT Produce

  • Technical architecture or implementation details (use tsh-architect agent for that)
  • Detailed acceptance criteria from a QA perspective (use tsh-context-engineer agent for that)
  • Story point estimates (left for team estimation sessions)
  • Sprint or release planning

Task Extraction Process

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 extraction
  • Cleaned transcript (cleaned-transcript.md): Primary source — review all discussion topics, decisions, action items, and open questions
  • Figma/FigJam designs: If available, analyze screens, flows, and annotations for functional requirements
  • Existing codebase: Use tsh-codebase-analysing skill to understand what already exists and what needs to be built
  • Other documents: Confluence pages, shared documents, email threads, or any other reference materials provided by the user
  • PDF documents: If available, read PDF files using the pdf-reader tool to extract requirements, process descriptions, business rules, or any other relevant content provided by the client
  • Project baseline (specifications/projects/<project-name>/task-baseline.md, if present): Review for continuity, overlap, and existing backlog context

Create 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:

  • Goal
  • In scope
  • Out of scope
  • Key stakeholders / actors
  • Likely epic candidates
  • Baseline overlap or existing backlog notes
  • Open questions

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:

  • Look for natural groupings of related functionality
  • Each epic should represent a cohesive business capability (e.g., "User Authentication", "Payment Processing", "Reporting Dashboard")
  • Epics should be independent enough to be delivered and validated separately where possible
  • Aim for 3-10 epics per workshop (if you find more, some may be too granular; if fewer, some may be too broad)

For each epic, draft:

  • A clear, business-oriented title
  • A 2-3 sentence business description explaining the value
  • High-level success criteria (what "done" looks like from a business perspective)

Step 5: Break down epics into user stories

For each epic, identify the individual user stories that compose it:

  • Each story should represent a single, deliverable piece of user-facing functionality
  • Stories should be small enough to be completed in a single sprint (as a guideline)
  • Look for stories in: feature descriptions, user workflows, business rules, data requirements, integration points
  • Include non-functional stories where explicitly discussed (e.g., "As an admin, I want audit logging so that compliance requirements are met")

Step 6: Write business-oriented descriptions with source traceability

For each user story, write:

  • Title: Short, descriptive, action-oriented (e.g., "User can reset password via email link")
  • User story: "As a [role], I want [capability] so that [benefit]"
  • Source: A concise traceability field that points back to the workshop material(s), baseline entry, or context summary used to derive the story
  • Acceptance criteria: Checklist of verifiable business conditions, written primarily as concise GIVEN / WHEN / THEN scenarios
  • High-level technical notes: Brief notes ONLY where the workshop discussion explicitly mentioned technical considerations (e.g., "Discussed using SSO integration", "Needs to support 10k concurrent users"). Do NOT invent technical details
  • Priority suggestion: Based on discussion emphasis and dependencies (Critical / High / Medium / Low)
  • Additional acceptance checks: Optional non-scenario checks when a GIVEN / WHEN / THEN statement is not sufficient
Show full SKILL.md (459 more words)Show less

Important: 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:

  • Blocked by: Story A cannot start until Story B is complete
  • Related to: Stories that share context but don't block each other
  • Epic dependencies: When one epic must be delivered before another can begin

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:

  • Assumptions: Decisions you made based on interpretation of the materials where the intent was not 100% clear. Label each assumption clearly so stakeholders can confirm or correct it.
  • Out of scope: Items that were explicitly excluded during the workshop, or that you identified as beyond the current discussion scope. This prevents scope creep and sets clear boundaries.

Step 9: Flag ambiguities and ask clarifying questions

Review all extracted tasks and identify:

  • Stories where you are not confident about the scope or intent
  • Conflicting information between different materials (e.g., transcript says one thing, Figma shows another)
  • Missing information that would significantly affect the task breakdown

Use askQuestions to clarify these items with the user. Ask exactly one question per askQuestions call. 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 askQuestions call 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.

Connected Skills

  • tsh-transcript-processing - provides the cleaned transcript used as primary input
  • tsh-codebase-analysing - for understanding existing system context when analyzing scope
  • tsh-task-analysing - for business-context exploration, baseline comparison, and intent brief drafting before extraction

© TheSoftwareHouse, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in .github/skills/tsh-task-extracting of TheSoftwareHouse/copilot-collections.

  • SKILL.md
  • extracted-tasks.example.md
  • intent-brief.example.md

Open the folder on GitHubat commit 2fbe51e

Compare with similar skills

Tsh Task Extracting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Tsh Task Extracting compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Tsh Task Extracting this skillTheSoftwareHouse/copilot-collections284—~2.5kAutomated safety check: PassMIT
Project Planneradrianpuiu/claude-skills-marketplace1001 repos~6kAutomated safety check: PassNone
Spec Workflowhashgraph-online/awesome-codex-plugins1.2k—~2.1kAutomated safety check: PassApache-2.0
Pm Planningavelikiy/great_cto103—~1.3kAutomated safety check: PassMIT
Analyze And Planadobe/skills195—~1.5kAutomated safety check: PassApache-2.0
Backlog Builderopen-mercato/skills221—~3kAutomated safety check: NotesMIT

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Works with

Questions about Tsh Task Extracting

What does Tsh Task Extracting do?

Identify and structure epics and user stories from workshop materials (cleaned transcripts, Figma designs, codebase analysis, and other documents). Tsh Task Extracting is an agent skill from TheSoftwareHouse/copilot-collections. Identify and structure epics and user stories from workshop materials (cleaned transcripts, Figma designs, codebase analysis, and other documents).

When should I use Tsh Task Extracting?

Tsh Task Extracting fits situations like: tasks that involve User stories; tasks that involve Task breakdown; tasks that involve Codebase onboarding.

How do I install Tsh Task Extracting in Claude Code?

Run `npx skills add TheSoftwareHouse/copilot-collections --skill tsh-task-extracting -a claude-code`. Or copy the skill folder (.github/skills/tsh-task-extracting in TheSoftwareHouse/copilot-collections) into .claude/skills/tsh-task-extracting in your project. Claude Code loads it when a task matches its description.

How do I install Tsh Task Extracting in Codex?

Run `npx skills add TheSoftwareHouse/copilot-collections --skill tsh-task-extracting -a codex`. Or copy the skill folder (.github/skills/tsh-task-extracting in TheSoftwareHouse/copilot-collections) into .agents/skills/tsh-task-extracting in your project. Codex loads it when a task matches its description.

Can I use Tsh Task Extracting in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TheSoftwareHouse/copilot-collections --skill tsh-task-extracting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tsh-task-extracting, .gemini/skills/tsh-task-extracting, .github/skills/tsh-task-extracting and .opencode/skills/tsh-task-extracting in your project.

What does Tsh Task Extracting need to run?

SKILL.md names no scripts, command-line tools or credentials: Tsh Task Extracting is instructions for the agent only.

Does Tsh Task Extracting access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Tsh Task Extracting safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Tsh Task Extracting use?

Tsh Task Extracting is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Tsh Task Extracting use?

About 2.5k tokens (SKILL.md is roughly 9.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Tsh Task Extracting?

Skills that share tags, products or a category with Tsh Task Extracting: Project Planner (adrianpuiu/claude-skills-marketplace, 100 stars), Spec Workflow (hashgraph-online/awesome-codex-plugins, 1.2k stars), Pm Planning (avelikiy/great_cto, 103 stars) and Analyze And Plan (adobe/skills, 195 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Tsh Task Extracting?

TheSoftwareHouse (a GitHub organization) maintains it in TheSoftwareHouse/copilot-collections, which has 284 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on October 5, 2026.

Source: TheSoftwareHouse/copilot-collections on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.