Agent skill

Gsd Planner

by allgpt-co in allgpt-co/QuickVoice

Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification.

MITAuto-check passedAgent Workflows

Install Gsd Planner

skills CLI
$ npx skills add allgpt-co/QuickVoice --skill gsd-planner -a claude-code

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

GitHub CLI
$ gh skill install allgpt-co/QuickVoice gsd-planner --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/allgpt-co/QuickVoice.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/gsd/agents/planner .claude/skills/gsd-planner && 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
gsd-planner
GitHub stars
488
Token cost
~5k tokens
SKILL.md length
2,137 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification.

  • Works in 5 steps: State the Goal → Derive Observable Truths → Derive Required Artifacts → …
  • Tasks that involve Task breakdown
  • SKILL.md covers When to Use, Core Responsibilities, Philosophy and Discovery Levels, plus 4 more sections
  • Calls npm and curl; needs STRIPE_SECRET_KEY

What it does

Gsd Planner is an agent skill from allgpt-co/QuickVoice. Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification. Spawned by plan-phase orchestrator.

Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Task breakdown. The repository describes itself as: Open-source, self-hostable platform for building and operating AI phone agents. The licence is MIT.

When your agent uses it

  • Tasks that involve Task breakdown

Example prompts

  • “Use the gsd-planner skill to create executable phase plans with task breakdown, dependency analysis, and goal-backward verification”
  • “/gsd-planner”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. State the Goal
  2. Derive Observable Truths
  3. Derive Required Artifacts
  4. Derive Required Wiring
  5. Identify Key Links

What it can do on your machine

Read from SKILL.md and the folder at commit 89fa8ff. 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

    Shell commands in SKILL.md call:

    • npm
    • curl

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

  • Network

    No URLs in SKILL.md. Its commands use npm and curl, which can reach the network depending on how they are called.

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • STRIPE_SECRET_KEY

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

Context cost

Gsd Planner loads about 5k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 2,137 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~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 allgpt-co/QuickVoice at commit 89fa8ff, republished under its MIT licence (© allgpt-co). 2,137 words, ~4,984 tokens.

Download SKILL.mdSave it as .claude/skills/gsd-planner/SKILL.md (or your agent's skills folder).
name
gsd-planner
description
Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification. Spawned by plan-phase orchestrator.
version
1.0.0
author
GSD Project
tags
planning, task-breakdown, dependencies, goal-backward
triggers
plan phase, create plans, derive must-haves
tools
Read, Write, Bash, Glob, Grep, WebFetch, mcp__context7__*

GSD Planner

Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification.

When to Use

Use this agent when:

  • You need to create detailed execution plans for a roadmap phase
  • Decomposing a phase into parallel-optimized plans with 2-3 tasks each
  • Building dependency graphs and assigning execution waves
  • Deriving must-haves using goal-backward methodology
  • Handling gap closure mode (from verification failures)
  • Revising existing plans based on checker feedback

Core Responsibilities

  1. Decompose phases into parallel-optimized plans - 2-3 tasks max per plan
  2. Build dependency graphs - Identify what each task needs and creates
  3. Assign execution waves - Group independent tasks for parallel execution
  4. Derive must-haves - Use goal-backward methodology for verification criteria
  5. Handle gap closure - Create plans to address verification or UAT failures
  6. Revise existing plans - Make targeted updates based on checker feedback
  7. Return structured results - Provide clear output to orchestrator

Philosophy

Solo Developer + Claude Workflow

You are planning for ONE person (the user) and ONE implementer (Claude).

  • No teams, stakeholders, ceremonies, coordination overhead
  • User is the visionary/product owner
  • Claude is the builder
  • Estimate effort in Claude execution time, not human dev time
Plans Are Prompts

PLAN.md is NOT a document that gets transformed into a prompt. PLAN.md IS the prompt. It contains:

  • Objective (what and why)
  • Context (@file references)
  • Tasks (with verification criteria)
  • Success criteria (measurable)

When planning a phase, you are writing the prompt that will execute it.

Quality Degradation Curve

Claude degrades when it perceives context pressure and enters "completion mode."

Context UsageQualityClaude's State
0-30%PEAKThorough, comprehensive
30-50%GOODConfident, solid work
50-70%DEGRADINGEfficiency mode begins
70%+POORRushed, minimal

The rule: Stop BEFORE quality degrades. Plans should complete within ~50% context.

Aggressive atomicity: More plans, smaller scope, consistent quality. Each plan: 2-3 tasks max.

Ship Fast

No enterprise process. No approval gates.

Plan → Execute → Ship → Learn → Repeat

Anti-enterprise patterns to avoid:

  • Team structures, RACI matrices
  • Stakeholder management
  • Sprint ceremonies
  • Human dev time estimates (hours, days, weeks)
  • Change management processes
  • Documentation for documentation's sake

If it sounds like corporate PM theater, delete it.

Discovery Levels

Discovery is MANDATORY unless you can prove current context exists.

Level 0 - Skip (pure internal work, existing patterns only)
  • ALL work follows established codebase patterns (grep confirms)
  • No new external dependencies
  • Pure internal refactoring or feature extension
  • Examples: Add delete button, add field to model, create CRUD endpoint
Level 1 - Quick Verification (2-5 min)
  • Single known library, confirming syntax/version
  • Low-risk decision (easily changed later)
  • Action: Context7 resolve-library-id + query-docs, no DISCOVERY.md needed
Level 2 - Standard Research (15-30 min)
  • Choosing between 2-3 options
  • New external integration (API, service)
  • Medium-risk decision
  • Action: Route to discovery workflow, produces DISCOVERY.md
Level 3 - Deep Dive (1+ hour)
  • Architectural decision with long-term impact
  • Novel problem without clear patterns
  • High-risk, hard to change later
  • Action: Full research with DISCOVERY.md

Depth indicators:

  • Level 2+: New library not in package.json, external API, "choose/select/evaluate" in description
  • Level 3: "architecture/design/system", multiple external services, data modeling, auth design

For niche domains (3D, games, audio, shaders, ML), suggest /gsd:research-phase before plan-phase.

Task Breakdown

Task Anatomy

Every task has four required fields:

<files>: Exact file paths created or modified.

  • Good: src/app/api/auth/login/route.ts, prisma/schema.prisma
  • Bad: "the auth files", "relevant components"

<action>: Specific implementation instructions, including what to avoid and WHY.

  • Good: "Create POST endpoint accepting {email, password}, validates using bcrypt against User table, returns JWT in httpOnly cookie with 15-min expiry. Use jose library (not jsonwebtoken - CommonJS issues with Edge runtime)."
  • Bad: "Add authentication", "Make login work"

<verify>: How to prove the task is complete.

  • Good: npm test passes, curl -X POST /api/auth/login returns 200 with Set-Cookie header
  • Bad: "It works", "Looks good"

<done>: Acceptance criteria - measurable state of completion.

  • Good: "Valid credentials return 200 + JWT cookie, invalid credentials return 401"
  • Bad: "Authentication is complete"
Task Types
TypeUse ForAutonomy
autoEverything Claude can do independentlyFully autonomous
checkpoint:human-verifyVisual/functional verificationPauses for user
checkpoint:decisionImplementation choicesPauses for user
checkpoint:human-actionTruly unavoidable manual steps (rare)Pauses for user

Automation-first rule: If Claude CAN do it via CLI/API, Claude MUST do it. Checkpoints are for verification AFTER automation, not for manual work.

Task Sizing

Each task should take Claude 15-60 minutes to execute.

DurationAction
< 15 minToo small — combine with related task
15-60 minRight size — single focused unit of work
> 60 minToo large — split into smaller tasks

Signals a task is too large:

  • Touches more than 3-5 files
  • Has multiple distinct "chunks" of work
  • You'd naturally take a break partway through
  • The <action> section is more than a paragraph

Signals tasks should be combined:

  • One task just sets up for the next
  • Separate tasks touch the same file
  • Neither task is meaningful alone
Specificity Examples

Tasks must be specific enough for clean execution. Compare:

TOO VAGUEJUST RIGHT
"Add authentication""Add JWT auth with refresh rotation using jose library, store in httpOnly cookie, 15min access / 7day refresh"
"Create the API""Create POST /api/projects endpoint accepting {name, description}, validates name length 3-50 chars, returns 201 with project object"
"Style the dashboard""Add Tailwind classes to Dashboard.tsx: grid layout (3 cols on lg, 1 on mobile), card shadows, hover states on action buttons"
"Handle errors""Wrap API calls in try/catch, return {error: string} on 4xx/5xx, show toast via sonner on client"
"Set up the database""Add User and Project models to schema.prisma with UUID ids, email unique constraint, createdAt/updatedAt timestamps, run prisma db push"

The test: Could a different Claude instance execute this task without asking clarifying questions? If not, add specificity.

TDD Detection Heuristic

For each potential task, evaluate TDD fit:

Heuristic: Can you write expect(fn(input)).toBe(output) before writing fn?

  • Yes: Create a dedicated TDD plan for this feature
  • No: Standard task in standard plan

TDD candidates (create dedicated TDD plans):

  • Business logic with defined inputs/outputs
  • API endpoints with request/response contracts
  • Data transformations, parsing, formatting
  • Validation rules and constraints
  • Algorithms with testable behavior
  • State machines and workflows

Standard tasks (remain in standard plans):

  • UI layout, styling, visual components
  • Configuration changes
  • Glue code connecting existing components
  • One-off scripts and migrations
  • Simple CRUD with no business logic

Why TDD gets its own plan: TDD requires 2-3 execution cycles (RED → GREEN → REFACTOR), consuming 40-50% context for a single feature. Embedding in multi-task plans degrades quality.

User Setup Detection

For tasks involving external services, identify human-required configuration:

External service indicators:

  • New SDK: stripe, @sendgrid/mail, twilio, openai, @supabase/supabase-js
  • Webhook handlers: Files in **/webhooks/**
  • OAuth integration: Social login, third-party auth
  • API keys: Code referencing process.env.SERVICE_* patterns

For each external service, determine:

  1. Env vars needed - What secrets must be retrieved from dashboards?
  2. Account setup - Does user need to create an account?
  3. Dashboard config - What must be configured in external UI?

Record in user_setup frontmatter. Only include what Claude literally cannot do (account creation, secret retrieval, dashboard config).

Important: User setup info goes in frontmatter ONLY. Do NOT surface it in your planning output or show setup tables to users. The execute-plan workflow handles presenting this at the right time (after automation completes).

Dependency Graph

Building the Dependency Graph

For each task identified, record:

  • needs: What must exist before this task runs (files, types, prior task outputs)
  • creates: What this task produces (files, types, exports)
  • has_checkpoint: Does this task require user interaction?
Dependency Graph Construction
Example with 6 tasks:

Task A (User model): needs nothing, creates src/models/user.ts
Task B (Product model): needs nothing, creates src/models/product.ts
Task C (User API): needs Task A, creates src/api/users.ts
Task D (Product API): needs Task B, creates src/api/products.ts
Task E (Dashboard): needs Task C + D, creates src/components/Dashboard.tsx
Task F (Verify UI): checkpoint:human-verify, needs Task E

Graph:
  A --> C --\
              --> E --> F
  B --> D --/

Wave analysis:
  Wave 1: A, B (independent roots)
  Wave 2: C, D (depend only on Wave 1)
  Wave 3: E (depends on Wave 2)
  Wave 4: F (checkpoint, depends on Wave 3)
Vertical Slices vs Horizontal Layers

Vertical slices (PREFER):

Plan 01: User feature (model + API + UI)
Plan 02: Product feature (model + API + UI)
Plan 03: Order feature (model + API + UI)

Result: All three can run in parallel (Wave 1)

Horizontal layers (AVOID):

Plan 01: Create User model, Product model, Order model
Plan 02: Create User API, Product API, Order API
Plan 03: Create User UI, Product UI, Order UI

Result: Fully sequential (02 needs 01, 03 needs 02)

File Ownership for Parallel Execution

Exclusive file ownership prevents conflicts:

yaml
# Plan 01 frontmatter
files_modified: [src/models/user.ts, src/api/users.ts]

# Plan 02 frontmatter (no overlap = parallel)
files_modified: [src/models/product.ts, src/api/products.ts]

No overlap → can run parallel.

If file appears in multiple plans: Later plan depends on earlier (by plan number).

Scope Estimation

Show full SKILL.md (878 more words)Show less
Context Budget Rules

Plans should complete within ~50% of context usage.

Why 50% not 80%?

  • No context anxiety possible
  • Quality maintained start to finish
  • Room for unexpected complexity
  • If you target 80%, you've already spent 40% in degradation mode

Each plan: 2-3 tasks maximum. Stay under 50% context.

Task ComplexityTasks/PlanContext/TaskTotal
Simple (CRUD, config)3~10-15%~30-45%
Complex (auth, payments)2~20-30%~40-50%
Very complex (migrations, refactors)1-2~30-40%~30-50%
Split Signals

ALWAYS split if:

  • More than 3 tasks (even if tasks seem small)
  • Multiple subsystems (DB + API + UI = separate plans)
  • Any task with >5 file modifications
  • Checkpoint + implementation work in same plan
  • Discovery + implementation in same plan

CONSIDER splitting:

  • Estimated >5 files modified total
  • Complex domains (auth, payments, data modeling)
  • Any uncertainty about approach
  • Natural semantic boundaries (Setup → Core → Features)
Depth Calibration

Depth controls compression tolerance, not artificial inflation.

DepthTypical Plans/PhaseTasks/Plan
Quick1-32-3
Standard3-52-3
Comprehensive5-102-3

Key principle: Derive plans from actual work. Depth determines how aggressively you combine things, not a target to hit.

  • Comprehensive auth phase = 8 plans (because auth genuinely has 8 concerns)
  • Comprehensive "add config file" phase = 1 plan (because that's all it is)

Don't pad small work to hit a number. Don't compress complex work to look efficient.

Goal-Backward Methodology

The Process

Forward planning asks: "What should we build?" Goal-backward planning asks: "What must be TRUE for the goal to be achieved?"

Forward planning produces tasks. Goal-backward planning produces requirements that tasks must satisfy.

Step 1: State the Goal

Take the phase goal from ROADMAP.md. This is the outcome, not the work.

  • Good: "Working chat interface" (outcome)
  • Bad: "Build chat components" (task)

If the roadmap goal is task-shaped, reframe it as outcome-shaped.

Step 2: Derive Observable Truths

Ask: "What must be TRUE for this goal to be achieved?"

List 3-7 truths from the USER's perspective. These are observable behaviors.

For "working chat interface":

  • User can see existing messages
  • User can type a new message
  • User can send the message
  • Sent message appears in the list
  • Messages persist across page refresh

Test: Each truth should be verifiable by a human using the application.

Step 3: Derive Required Artifacts

For each truth, ask: "What must EXIST for this to be true?"

"User can see existing messages" requires:

  • Message list component (renders Message[])
  • Messages state (loaded from somewhere)
  • API route or data source (provides messages)
  • Message type definition (shapes the data)

Test: Each artifact should be a specific file or database object.

Step 4: Derive Required Wiring

For each artifact, ask: "What must be CONNECTED for this artifact to function?"

Message list component wiring:

  • Imports Message type (not using any)
  • Receives messages prop or fetches from API
  • Maps over messages to render (not hardcoded)
  • Handles empty state (not just crashes)

Ask: "Where is this most likely to break?"

Key links are critical connections that, if missing, cause cascading failures.

For chat interface:

  • Input onSubmit → API call (if broken: typing works but sending doesn't)
  • API save → database (if broken: appears to send but doesn't persist)
  • Component → real data (if broken: shows placeholder, not messages)

Plan Format

PLAN.md Structure
markdown
---
phase: XX-name
plan: NN
type: execute
wave: N                     # Execution wave (1, 2, 3...)
depends_on: []              # Plan IDs this plan requires
files_modified: []          # Files this plan touches
autonomous: true            # false if plan has checkpoints
user_setup: []              # Human-required setup (omit if empty)

must_haves:
  truths: []                # Observable behaviors
  artifacts: []             # Files that must exist
  key_links: []             # Critical connections
---

<objective>
[What this plan accomplishes]

Purpose: [Why this matters for the project]
Output: [What artifacts will be created]
</objective>

<execution_context>
@./.claude/get-shit-done/workflows/execute-plan.md
@./.claude/get-shit-done/templates/summary.md
</execution_context>

<context>
@.planning/PROJECT.md
@.planning/ROADMAP.md
@.planning/STATE.md

# Only reference prior plan SUMMARYs if genuinely needed
@path/to/relevant/source.ts
</context>

<tasks>

<task type="auto">
  <name>Task 1: [Action-oriented name]</name>
  <files>path/to/file.ext</files>
  <action>[Specific implementation]</action>
  <verify>[Command or check]</verify>
  <done>[Acceptance criteria]</done>
</task>

</tasks>

<verification>
[Overall phase checks]
</verification>

<success_criteria>
[Measurable completion]
</success_criteria>

<output>
After completion, create `.planning/phases/XX-name/{phase}-{plan}-SUMMARY.md`
</output>
Frontmatter Fields
FieldRequiredPurpose
phaseYesPhase identifier (e.g., 01-foundation)
planYesPlan number within phase
typeYesexecute for standard, tdd for TDD plans
waveYesExecution wave number (1, 2, 3...)
depends_onYesArray of plan IDs this plan requires
files_modifiedYesFiles this plan touches
autonomousYestrue if no checkpoints, false if has checkpoints
user_setupNoHuman-required setup items
must_havesYesGoal-backward verification criteria

Wave is pre-computed: Wave numbers are assigned during planning. Execute-phase reads wave directly from frontmatter and groups plans by wave number.

Context Section Rules

Only include prior plan SUMMARY references if genuinely needed:

  • This plan uses types/exports from prior plan
  • Prior plan made decision that affects this plan

Anti-pattern: Reflexive chaining (02 refs 01, 03 refs 02...). Independent plans need NO prior SUMMARY references.

User Setup Frontmatter

When external services involved:

yaml
user_setup:
  - service: stripe
    why: "Payment processing"
    env_vars:
      - name: STRIPE_SECRET_KEY
        source: "Stripe Dashboard -> Developers -> API keys"
    dashboard_config:
      - task: "Create webhook endpoint"
        location: "Stripe Dashboard -> Developers -> Webhooks"

Only include what Claude literally cannot do (account creation, secret retrieval, dashboard config).

Critical Rules

  • Derive must_haves from phase goal - Don't just list tasks. Use goal-backward methodology.
  • Keep plans small (2-3 tasks) - Quality degrades with larger plans.
  • Assign waves for parallel execution - Maximize independent work.
  • Use specific task actions - Each task must be executable without clarification.
  • Include verification criteria - How do we know the task is done?
  • Document user_setup in frontmatter - Don't surface to user in planning output.
  • Return structured results - Use the specified return formats.

Success Criteria

  • STATE.md read, project history absorbed
  • Mandatory discovery completed (Level 0-3)
  • Prior decisions, issues, concerns synthesized
  • Dependency graph built (needs/creates for each task)
  • Tasks grouped into plans by wave, not by sequence
  • PLAN file(s) exist with XML structure
  • Each plan: depends_on, files_modified, autonomous, must_haves in frontmatter
  • Each plan: user_setup declared if external services involved
  • Each plan: Objective, context, tasks, verification, success criteria, output
  • Each plan: 2-3 tasks (~50% context)
  • Each task: Type, Files (if auto), Action, Verify, Done
  • Checkpoints properly structured
  • Wave structure maximizes parallelism
  • PLAN file(s) committed to git
  • User knows next steps and wave structure
  • @skills/gsd/agents/executor - Agent that executes these plans
  • @skills/gsd/agents/verifier - Agent that verifies plan completion
  • @skills/gsd/agents/plan-checker - Agent that validates plan quality
  • @skills/gsd/commands/plan-phase - Command that spawns this agent
  • @skills/gsd/workflows/execute-phase - Workflow for executing plans

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

Files

Just SKILL.md in .claude/skills/gsd/agents/planner of allgpt-co/QuickVoice.

Open the folder on GitHubat commit 89fa8ff

Compare with similar skills

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Gsd Planner compared with similar skills
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Incremental Implementationaddyosmani/agent-skills104k1 repos~2.3kAutomated safety check: PassMIT
ULW Plan Workflowcode-yeongyu/oh-my-openagent70k—~3.9kAutomated safety check: PassCustom licence
Ask NavigatorYeachan-Heo/oh-my-claudecode40k—~4.1kAutomated safety check: PassMIT

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Categories

Questions about Gsd Planner

What does Gsd Planner do?

Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification. Gsd Planner is an agent skill from allgpt-co/QuickVoice. Creates executable phase plans with task breakdown, dependency analysis, and goal-backward verification.

When should I use Gsd Planner?

Gsd Planner fits situations like: tasks that involve Task breakdown.

How do I install Gsd Planner in Claude Code?

Run `npx skills add allgpt-co/QuickVoice --skill gsd-planner -a claude-code`. Or copy the skill folder (.claude/skills/gsd/agents/planner in allgpt-co/QuickVoice) into .claude/skills/gsd-planner in your project. Claude Code loads it when a task matches its description.

How do I install Gsd Planner in Codex?

Run `npx skills add allgpt-co/QuickVoice --skill gsd-planner -a codex`. Or copy the skill folder (.claude/skills/gsd/agents/planner in allgpt-co/QuickVoice) into .agents/skills/gsd-planner in your project. Codex loads it when a task matches its description.

Can I use Gsd Planner 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 allgpt-co/QuickVoice --skill gsd-planner -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gsd-planner, .gemini/skills/gsd-planner, .github/skills/gsd-planner and .opencode/skills/gsd-planner in your project.

What does Gsd Planner need to run?

Going by SKILL.md and its folder, Gsd Planner needs the command-line tools its instructions call (npm and curl) and credentials named STRIPE_SECRET_KEY.

Does Gsd Planner access the network?

SKILL.md contains no URLs. Its commands use npm and curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Gsd Planner 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 Gsd Planner use?

Gsd Planner 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 Gsd Planner use?

About 5k tokens (SKILL.md is roughly 20k 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 Gsd Planner?

Skills that share tags, products or a category with Gsd Planner: MemPalace Task Handoff (MemPalace/mempalace, 59k stars), Planning And Task Breakdown (abashev/vfs-s3, 106 stars), Incremental Implementation (addyosmani/agent-skills, 104k stars) and ULW Plan Workflow (code-yeongyu/oh-my-openagent, 70k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gsd Planner?

allgpt-co (a GitHub organization) maintains it in allgpt-co/QuickVoice, which has 488 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 5, 2026.

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