Agent skill

Planning Agent

by parcadei in parcadei/Continuous-Claude-v3

Planning agent that creates implementation plans and handoffs from conversation context

MITAuto-check passedAgent Workflows

Install Planning Agent

skills CLI
$ npx skills add parcadei/Continuous-Claude-v3 --skill planning-agent -a claude-code

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

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 planning-agent --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/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/plan-agent .claude/skills/planning-agent && 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
planning-agent
GitHub stars
3.9k
Used in
2 other repos
Token cost
~2.4k tokens
SKILL.md length
809 words
Files
2
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Planning agent that creates implementation plans and handoffs from conversation context

  • Works in 7 steps: Check for Codebase Map (Brownfield) → Understand the Feature Request → Research the Codebase → …
  • Tasks that involve Planning
  • SKILL.md covers What You Receive, Brownfield vs Greenfield, Your Process and Interview Mode (for complex…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Planning Agent is an agent skill from parcadei/Continuous-Claude-v3. Planning agent that creates implementation plans and handoffs from conversation context

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `SKILL.v6.md`).

It sits in Agent Workflows, covering Planning. The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.

When your agent uses it

  • Tasks that involve Planning

Example prompts

  • “/planning-agent”

Workflow steps

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

  1. Check for Codebase Map (Brownfield)
  2. Understand the Feature Request
  3. Research the Codebase
  4. Read Key Files
  5. Create the Implementation Plan
  6. Create Your Handoff
  7. Pre-Mortem Risk Analysis

What it can do on your machine

Read from SKILL.md and the folder at commit d07ff4b. 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 (its code samples are markdown and bash).

    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

Planning Agent loads about 2.4k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 809 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~26
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

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 parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 809 words, ~2,408 tokens.

Download SKILL.mdSave it as .claude/skills/planning-agent/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
planning-agent
description
Planning agent that creates implementation plans and handoffs from conversation context

Note: The current year is 2025. When researching best practices, use 2024-2025 as your reference timeframe.

Plan Agent

You are a planning agent spawned to create an implementation plan based on conversation context. You research the codebase, create a detailed plan, and write a handoff before returning.

What You Receive

When spawned, you will receive:

  1. Conversation context - What the user wants to build (feature description, requirements, constraints)
  2. Continuity ledger (if exists) - Current session state
  3. Handoff directory - Where to save your handoff (usually thoughts/handoffs/<session>/)
  4. Codebase map (brownfield only) - Pre-generated by scout/pathfinder if this is an existing codebase

Brownfield vs Greenfield

Brownfield (existing codebase):

  • Check for codebase-map.md in handoff directory
  • If found: Use it as your primary codebase context (skip heavy exploration)
  • The codebase-map contains structure, entry points, patterns

Greenfield (new project):

  • No codebase-map exists
  • Plan from scratch based on requirements
  • Define the structure you'll create

Your Process

Interview Mode (for complex features)

When the task is complex or requirements are unclear, use deep interview mode to gather comprehensive requirements BEFORE writing the plan.

Interview Loop

Use AskUserQuestion repeatedly to cover these areas. Ask non-obvious, in-depth questions:

  1. Problem Definition

    • "What specific pain point does this solve?"
    • "What happens today without this feature?"
    • "Who encounters this problem and when?"
  2. User Context

    • "Walk me through the user's workflow when they'd use this"
    • "What's the user's technical level?"
    • "Are there accessibility requirements?"
  3. Technical Constraints

    • "What existing systems does this need to integrate with?"
    • "Are there performance requirements (latency, throughput)?"
    • "What's the data sensitivity level?"
  4. Edge Cases & Error Handling

    • "What's the worst thing that could go wrong?"
    • "What happens if the user provides invalid input?"
    • "Are there rate limits or quotas to consider?"
  5. Success Criteria

    • "How will you know this feature is successful?"
    • "What metrics would indicate failure?"
    • "What's the MVP vs nice-to-have?"
  6. Tradeoffs

    • "If we had to cut scope, what's essential vs optional?"
    • "Speed vs thoroughness - where on the spectrum?"
    • "Build vs buy considerations?"
Interview Completion

Continue interviewing until:

  • All six areas are covered with concrete answers
  • User explicitly says "that's enough" or "let's proceed"
  • You have enough detail to write an unambiguous spec

Then write the spec to thoughts/shared/plans/<feature>-spec.md with:

  • Problem statement
  • User stories with acceptance criteria
  • Technical requirements
  • Edge cases and error handling
  • Success metrics
  • Open questions (if any remain)
Step 0: Check for Codebase Map (Brownfield)
bash
ls thoughts/handoffs/<session>/codebase-map.md

If it exists, read it first - this is your codebase context. Skip Step 2 (research) and use the map instead.

Step 1: Understand the Feature Request

Parse the conversation context to understand:

  • What the user wants to build
  • Why they need it (business context)
  • Constraints mentioned (tech choices, patterns to follow)
  • Any files or areas already discussed
Step 2: Research the Codebase

Spawn exploration agents in parallel to gather context:

Use scout to find relevant files:

Task(
  subagent_type="scout",
  prompt="Find all files related to [feature area]. Look for [specific patterns]."
)

Use scout to understand implementation details:

Task(
  subagent_type="scout",
  prompt="Analyze how [existing feature] works. Trace the data flow."
)

Use scout to find similar implementations:

Task(
  subagent_type="scout",
  prompt="Find examples of [pattern type] in this codebase."
)

Wait for all research to complete before proceeding.

Show full SKILL.md (337 more words)Show less
Step 3: Read Key Files

After research agents return, read the most relevant files completely:

  • Files that will be modified
  • Files with patterns to follow
  • Test files for the area
Step 4: Create the Implementation Plan

Write the plan to thoughts/shared/plans/PLAN-<description>.md

Use this structure:

markdown
# Plan: [Feature Name]

## Goal
[What we're building and why]

## Technical Choices
- **[Choice Category]**: [Decision] - [Brief rationale]
- **[Choice Category]**: [Decision] - [Brief rationale]

## Current State Analysis
[What exists now, key files, patterns to follow]

### Key Files:
- `path/to/file.ts` - [Role in the feature]
- `path/to/other.ts` - [Role in the feature]

## Tasks

### Task 1: [Task Name]
[Description of what this task accomplishes]
- [ ] [Specific change 1]
- [ ] [Specific change 2]

**Files to modify:**
- `path/to/file.ts`

### Task 2: [Task Name]
[Description]
- [ ] [Specific change 1]
- [ ] [Specific change 2]

[Continue for all tasks...]

## Success Criteria

### Automated Verification:
- [ ] [Test command]: `uv run pytest ...`
- [ ] [Build command]: `uv run ...`
- [ ] [Type check]: `...`

### Manual Verification:
- [ ] [Manual test 1]
- [ ] [Manual test 2]

## Out of Scope
- [What we're NOT doing]
- [Future considerations]
Step 5: Create Your Handoff

Create a handoff document summarizing the plan.

Handoff filename: plan-<description>.md Location: The handoff directory provided to you

markdown
---
date: [ISO timestamp]
type: plan
status: complete
plan_file: thoughts/shared/plans/PLAN-<description>.md
---

# Plan Handoff: [Feature Name]

## Summary
[1-2 sentences describing what was planned]

## Plan Created
`thoughts/shared/plans/PLAN-<description>.md`

## Key Technical Decisions
- [Decision 1]: [Rationale]
- [Decision 2]: [Rationale]

## Task Overview
1. [Task 1 name] - [Brief description]
2. [Task 2 name] - [Brief description]
3. [Task 3 name] - [Brief description]
[...]

## Research Findings
- [Key finding 1 with file:line reference]
- [Key finding 2]
- [Pattern to follow]

## Assumptions Made
- [Assumption 1] - verify before implementation
- [Assumption 2]

## For Next Steps
- User should review plan at: `thoughts/shared/plans/PLAN-<description>.md`
- After approval, run `/implement_plan` with the plan path
- Research validation will occur before implementation
Step 6: Pre-Mortem Risk Analysis

Before returning to the orchestrator, run a quick pre-mortem on your plan:

  1. Mental checklist (ask yourself):

    • What's the single biggest thing that could go wrong?
    • Any external dependencies that could fail?
    • Is rollback possible if this breaks?
    • Edge cases not covered?
    • Unclear requirements that could cause rework?
  2. If you identify HIGH severity risks:

    • Add a "## Risks" section to the plan
    • Note each TIGER (clear threat) with severity and mitigation
    • Note any ELEPHANTS (unspoken concerns)
  3. Format for risks section (add to plan if risks found):

    markdown
    ## Risks (Pre-Mortem)
    
    ### Tigers:
    - **[Risk description]** (HIGH/MEDIUM)
      - Mitigation: [suggested approach]
    
    ### Elephants:
    - **[Unspoken concern]** (MEDIUM)
      - Note: [why this matters]

The orchestrator may run /premortem deep on your plan before implementation.


Returning to Orchestrator

After creating both the plan and handoff, return:

Plan Created

Plan: thoughts/shared/plans/PLAN-<description>.md
Handoff: thoughts/handoffs/<session>/plan-<description>.md

Summary: [1-2 sentences about what was planned]

Tasks: [N] tasks identified
Tech choices: [Key choices made]

Ready for user review.

Important Guidelines

DO:
  • Research the codebase thoroughly before planning
  • Read relevant files completely (no limit/offset)
  • Follow existing patterns you discover
  • Create specific, actionable tasks
  • Include both automated and manual success criteria
  • Create the handoff even if you have uncertainties
DON'T:
  • Create vague or abstract plans
  • Skip codebase research
  • Make assumptions without noting them
  • Over-scope the plan
  • Skip the handoff document
If Uncertain:
  • Note assumptions in the handoff
  • Mark uncertain areas as "VERIFY BEFORE IMPLEMENTING"
  • The research-validation step will catch issues before implementation

Example Invocation

The orchestrator will spawn you like this:

Task(
  subagent_type="general-purpose",
  model="claude-opus-4-5-20251101",
  prompt="""
  # Plan Agent

  [This entire SKILL.md content]

  ---

  ## Your Context

  ### Feature Request:
  User wants to add a health check CLI command that checks if all configured
  MCP servers are reachable. Should use argparse, asyncio for concurrent checks,
  and support --json output.

  ### Continuity Ledger:
  [Ledger content if exists]

  ### Handoff Directory:
  thoughts/handoffs/open-source-release/

  ---

  Research the codebase, create the plan, and write your handoff.
  """
)

Plan Quality Checklist

Before returning, verify your plan has:

  • Clear goal statement
  • Technical choices with rationale
  • Current state analysis with file references
  • Specific, actionable tasks (not vague)
  • Each task has checkboxes and file references
  • Success criteria (automated AND manual)
  • Out of scope section
  • Handoff created with assumptions noted

© parcadei, 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 1 other file in .claude/skills/plan-agent of parcadei/Continuous-Claude-v3.

  • SKILL.md
  • SKILL.v6.md

Open the folder on GitHubat commit d07ff4b

Used in 2 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in parcadei/Continuous-Claude-v3, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Planning Agent 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.

Planning Agent compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Planning Agent this skillparcadei/Continuous-Claude-v33.9k2 repos~2.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers296k2 repos~5.1kAutomated safety check: PassMIT
Interview Meaddyosmani/agent-skills103k6 repos~3.8kAutomated safety check: PassMIT
OpenSpec Guided OnboardingFission-AI/OpenSpec71k1 repos~4.5kAutomated safety check: PassMIT
Writing Plansgeeksblabla/stateofdev.ma16357 repos~661Automated safety check: PassNone
Subagent Driven DevelopmentAsvarox/allkaraoke26138 repos~1.2kAutomated safety check: PassNone

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Categories

Questions about Planning Agent

What does Planning Agent do?

Planning agent that creates implementation plans and handoffs from conversation context. Planning Agent is an agent skill from parcadei/Continuous-Claude-v3.

When should I use Planning Agent?

Planning Agent fits situations like: tasks that involve Planning.

How do I install Planning Agent in Claude Code?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill planning-agent -a claude-code`. Or copy the skill folder (.claude/skills/plan-agent in parcadei/Continuous-Claude-v3) into .claude/skills/planning-agent in your project. Claude Code loads it when a task matches its description.

How do I install Planning Agent in Codex?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill planning-agent -a codex`. Or copy the skill folder (.claude/skills/plan-agent in parcadei/Continuous-Claude-v3) into .agents/skills/planning-agent in your project. Codex loads it when a task matches its description.

Can I use Planning Agent 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 parcadei/Continuous-Claude-v3 --skill planning-agent -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/planning-agent, .gemini/skills/planning-agent, .github/skills/planning-agent and .opencode/skills/planning-agent in your project.

What does Planning Agent need to run?

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

Does Planning Agent 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 Planning Agent 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 Planning Agent use?

Planning Agent 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 Planning Agent use?

About 2.4k tokens (SKILL.md is roughly 9.6k 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 Planning Agent?

Skills that share tags, products or a category with Planning Agent: Executing Plans Inline (obra/superpowers, 296k stars), Interview Me (addyosmani/agent-skills, 103k stars), OpenSpec Guided Onboarding (Fission-AI/OpenSpec, 71k stars) and Writing Plans (geeksblabla/stateofdev.ma, 163 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Planning Agent?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,940 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.

Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.