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Tasks Generator

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luongnv89
tasks-generator

Generate sprint-based development tasks from a PRD. Use when asked to create tasks or break down requirements. Don't use for PRD/TAD authoring or task execution.

Overview

Publisherluongnv89
Repositoryskills
Skill nametasks-generator
Stars
124
Forks
18
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by luongnv89 on GitHub. Read the source before you install it.

Installation

Install the Tasks Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/luongnv89/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/tasks-generator .claude/skills/tasks-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tasks Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Tasks Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Tasks Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Tasks Generator

Transform PRD documents into structured, sprint-based development tasks with dependency analysis.

Environment Check

Before running this skill, verify:

  • The project has a PRD file (prd.md or similar)
  • You have write access to the project directory
  • The project is a git repository with a remote
  • You can run bash commands in the project directory

If any check fails, the skill will stop and ask for clarification.

Subagent Architecture

This skill uses a Staged Pipeline (E) + Parallel Workers (B) architecture:

Phase 1: Requirements Extraction
  ↓ (requirements-extractor agent)
Phase 2-3: Sprint Planning
  ↓ (sprint-planner agent)
Phase 4-5: Parallel Sprint Task Generation
  ├→ (sprint-worker agents, spawned in parallel — one per sprint)
  ├→ sprint 1 tasks
  ├→ sprint 2 tasks
  ├→ sprint 3 tasks
  └→ ...
Phase 6: Cross-Sprint Dependency Resolution
  ↓ (dependency-resolver agent)
Final Output: tasks.md with all tasks and dependencies

Agents:

  1. agents/requirements-extractor.md — Reads PRD + supporting docs, produces structured feature list
  2. agents/sprint-planner.md — Defines sprint scope (POC, MVP, full features), produces sprint plan
  3. agents/sprint-worker.md — Generates tasks for ONE sprint (runs in parallel, one per sprint)
  4. agents/dependency-resolver.md — Wires cross-sprint dependencies, produces final tasks.md

Key Insight: Per-sprint task generation is parallelizable. Large PRDs produce 30-80 tasks across 4+ sprints. Sprint tasks are not fully independent — Sprint 2 depends on Sprint 1 output — so the dependency-resolver does a final pass to wire cross-sprint relationships.

Repo Sync Before Edits (mandatory)

Before creating/updating/deleting files in an existing repository, sync the current branch with remote:

bash
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin
git pull --rebase origin "$branch"

If the working tree is not clean, stash first, sync, then restore:

bash
git stash push -u -m "pre-sync"
branch="$(git rev-parse --abbrev-ref HEAD)"
git fetch origin && git pull --rebase origin "$branch"
git stash pop

If origin is missing, pull is unavailable, or rebase/stash conflicts occur, stop and ask the user before continuing.

Modes (check before running)

  • Input resolution: $ARGUMENTS present → use it directly; empty → auto-pick (see Input below).
  • Repo state: dirty working tree → stash before sync (see Repo Sync); no origin → stop and ask.
  • Existing tasks.md: back it up before regenerating (see Pre-checks).
  • README update: only if the PRD lives inside an ideas repo (see README Maintenance) — skip otherwise.

Input

Preferred: PRD file path provided in $ARGUMENTS.

Auto-pick mode (if $ARGUMENTS is empty):

  1. Reuse the most recent project folder/path from this chat/session.
  2. If unavailable, use env var IDEAS_ROOT when present.
  3. Else check shared marker file ~/.config/ideas-root.txt.
  4. Backward compatibility fallback: ~/.openclaw/ideas-root.txt.
  5. If still unavailable, ask the user to provide the path or set IDEAS_ROOT.
  6. Use <project>/prd.md.
  7. If multiple candidates are plausible, ask user to choose.

Pre-checks

  1. Resolve PRD_PATH (from $ARGUMENTS or auto-pick mode) and verify it exists
  2. Check for existing tasks.md in the same directory - create backup if exists: tasks_backup_YYYY_MM_DD_HHMMSS.md
  3. Look for supporting docs in same directory: tad.md, ux_design.md, brand_kit.md

Workflow

Phase 1: Extract Requirements

From PRD, extract:

  • Core features and value proposition
  • User stories and personas
  • Functional requirements
  • Non-functional requirements (performance, security)
  • Technical constraints and dependencies

Phase 2: Define Development Phases

POC (Proof of Concept):

  • Single most important feature proving core value
  • Minimal implementation, 1-2 sprints

MVP (Minimum Viable Product):

  • Essential features for first release
  • Core user workflows

Full Features:

  • Remaining enhancements
  • Nice-to-haves and polish

Phase 3: Create Sprint Plan

SprintFocusScope
Sprint 1POCCore differentiating feature
Sprint 2MVP FoundationAuth, data models, primary workflows
Sprint 3MVP CompletionUI/UX, integration, validation
Sprint 4+Full FeaturesEnhancements, optimization, polish

Phase 4: Analyze Dependencies

  1. Map Dependencies: For each task, identify "Depends On" and "Blocks"
  2. Group Parallel Tasks: Assign tasks to execution waves
  3. Calculate Critical Path — the longest dependency chain; it sets the minimum duration. Referred to as "critical path" everywhere below.
  4. Validate: reject circular dependencies (the dependency graph must be a DAG — no cycles) and broken references

Phase 5: Generate tasks.md

Create tasks.md in same directory as PRD. See references/tasks-template.md for full template.

Task Format

Each task must include:

markdown
### Task X.Y: [Action-oriented Title]

**Description**: What and why, referencing PRD

**Acceptance Criteria**:
- [ ] Specific, testable condition 1
- [ ] Specific, testable condition 2

**Dependencies**: None / Task X.X

**PRD Reference**: [Section]

Task Guidelines

  • Title: Action-oriented (e.g., "Implement user authentication API")
  • Size: 1-3 days of work; break larger features
  • Criteria: Cover happy path and edge cases
  • Dependencies: List prerequisites and external dependencies

Quality Checks

The pre-finalize bars are defined once, in Acceptance Criteria — check every item there before reporting tasks.md complete. references/self-test.md holds the mechanical greps behind them.

README Maintenance (ideas repo only)

After writing tasks.md, if the PRD lives inside an ideas repo, update the repo README ideas table:

  • Preferred: cd to the repo root and run python3 scripts/update_readme_ideas_index.py (if it exists)
  • Fallback: update README.md manually (ensure Tasks status becomes ✅ for that idea)

Commit and push

  • Commit immediately after updates.
  • Confirm before pushing — this is a visible action:
bash
git push origin <branch>
  • If push is rejected: git fetch origin && git rebase origin/main && git push.

Reporting with GitHub links (mandatory)

When reporting completion, include:

  • GitHub link to tasks.md
  • GitHub link to README.md when it was updated
  • Commit hash

Link format (derive <owner>/<repo> from git remote get-url origin):

  • https://github.com/<owner>/<repo>/blob/main/<relative-path>

Step Completion Reports

After completing each major step, output a status report in this format:

◆ [Step Name] ([step N of M] — [context])
··································································
  [Check 1]:          √ pass
  [Check 2]:          √ pass (note if relevant)
  [Check 3]:          × fail — [reason]
  [Check 4]:          √ pass
  [Criteria]:         √ N/M met
  ____________________________
  Result:             PASS | FAIL | PARTIAL

Adapt the check names to match what the step actually validates. Use for pass, × for fail, and to add brief context. The "Criteria" line summarizes how many acceptance criteria were met. The "Result" line gives the overall verdict.

Requirements phase checks: PRD parsed, Features extracted, Constraints identified

Sprint Planning phase checks: Phases defined, Stories created, Dependencies mapped

Generation phase checks: tasks.md written, Sprint breakdown complete, Estimates assigned

Output phase checks: README updated, Committed, Links reported

Output Summary

After generating, provide:

  1. File location
  2. Sprint overview (count, tasks per sprint)
  3. MVP scope summary
  4. Dependency analysis (waves, critical path, bottlenecks)
  5. Flagged ambiguous requirements
  6. Next steps: Review Sprint 1 and Wave 1 tasks first

Acceptance Criteria

The skill run is considered successful only if ALL of the following hold:

  • tasks.md exists in the same directory as the input PRD.
  • tasks.md contains at least 3 sprints (POC, MVP Foundation, MVP Completion at minimum).
  • Each sprint contains at least 3 tasks; total task count is between 15 and 80.
  • Every task includes ALL of: Description, Acceptance Criteria (>=2 testable items), Dependencies (explicit None or task IDs), PRD Reference, and an effort estimate (e.g., Effort: 1-3 days or S/M/L).
  • Every task ID follows the Task <sprint>.<index> pattern (e.g., Task 1.1, Task 2.3).
  • A dependency table is present and references only tasks that exist in the file (no broken IDs).
  • No circular dependencies (see Phase 4).
  • At least one task per PRD requirement; ambiguous PRD items are flagged in a dedicated section.
  • Critical path is identified and stated explicitly.
  • The MVP vs post-MVP split is explicit — every sprint is labelled POC, MVP, or Full Features.
  • If a prior tasks.md existed, a tasks_backup_YYYY_MM_DD_HHMMSS.md file is created.
  • Final report includes GitHub links to tasks.md (and README.md if updated) plus the commit hash.

If any criterion fails, the skill must report it as a FAIL row in the Step Completion Report and not claim success.

Expected Output

The skill produces tasks.md next to the PRD, plus a final agent message with GitHub links and commit hash. See references/tasks-template.md for the full reviewable shape, dependency table, critical path, and final-message format. See references/self-test.md for the pre-success self-test checklist.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Tasks Generator AI skill do?

Generate sprint-based development tasks from a PRD. Use when asked to create tasks or break down requirements. Don't use for PRD/TAD authoring or task execution.

Why use Tasks Generator on TypingMind?

Because you install it once and use it with any model. Tasks Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Tasks Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/luongnv89/skills/tree/main/skills/tasks-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Tasks Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Tasks Generator?

As many as you like. As long as a model supports skills, you can use Tasks Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Tasks Generator AI skill free?

Yes. It is published on GitHub by luongnv89 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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