Agent skill

Deep Plan

by piercelamb in piercelamb/deep-plan

Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review.

MITAuto-check passedAgent Workflows

Install Deep Plan

skills CLI
$ npx skills add piercelamb/deep-plan --skill deep-plan -a claude-code

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

GitHub CLI
$ gh skill install piercelamb/deep-plan deep-plan --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/piercelamb/deep-plan.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/deep-plan .claude/skills/deep-plan && 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
deep-plan
GitHub stars
101
Token cost
~4.8k tokens
SKILL.md length
1,865 words
Files
9 (incl. references)
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review.

  • Works in 12 steps: Print Intro and Validate Environment → Handle Environment Errors → Validate Spec File Input → …
  • Planning features that need thorough pre-implementation analysis
  • SKILL.md covers CRITICAL: First Actions and Resuming After Compaction
  • Calls uv and bash

What it does

Deep Plan is an agent skill from piercelamb/deep-plan. Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.

Its SKILL.md is about 4.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `references/context-check.md`, `references/external-review.md` and `references/interview-protocol.md`). Compatibility notes: Requires uv (Python 3.11+), Gemini or OpenAI API key for external review

It sits in Agent Workflows, covering Planning and Test-driven development. The repository describes itself as: Claude Code plugin that transforms vague requirements into detailed implementation plans via research, interviews, and multi-LLM review. The licence is MIT.

When your agent uses it

  • Planning features that need thorough pre-implementation analysis
  • Tasks that involve Planning
  • Tasks that involve Test-driven development

Example prompts

  • “Use the deep-plan skill to create detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM…”
  • “/deep-plan”

Requirements

  • Python 3
  • Compatibility (from SKILL.md): Requires uv (Python 3.11+), Gemini or OpenAI API key for external review

Workflow steps

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

  1. Print Intro and Validate Environment
  2. Handle Environment Errors
  3. Validate Spec File Input
  4. Setup Planning Session
  5. Research Decision
  6. Execute Research
  7. Detailed Interview
  8. Save Interview Transcript
  9. Write Initial Spec
  10. Generate Implementation Plan
  11. Context Check (Pre-External Review)
  12. External LLM Review

What it can do on your machine

Read from SKILL.md and the folder at commit 9cc88c4. 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:

    • uv
    • bash

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

  • Network

    No URLs in SKILL.md. Its commands use uv, 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 no API keys, tokens, secrets or passwords.

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

  • Compatibility

    Requires uv (Python 3.11+), Gemini or OpenAI API key for external review

    From compatibility in the SKILL.md frontmatter.

Context cost

Deep Plan loads about 4.8k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 53 tokens; SKILL.md has 1,865 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~4.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~13k

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 piercelamb/deep-plan at commit 9cc88c4, republished under its MIT licence (© piercelamb). 1,865 words, ~4,798 tokens.

Download SKILL.mdSave it as .claude/skills/deep-plan/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
deep-plan
description
Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.
compatibility
Requires uv (Python 3.11+), Gemini or OpenAI API key for external review
license
MIT

Deep Planning Skill

Orchestrates a multi-step planning process: Research → Interview → External LLM Review → TDD Plan

CRITICAL: First Actions

BEFORE using any other tools, do these in order:

1. Print Intro and Validate Environment

Print intro banner immediately:

⚠️  CONTEXT WARNING: This workflow is token-intensive. Consider compacting first.

═══════════════════════════════════════════════════════════════
DEEP-PLAN: AI-Assisted Implementation Planning
═══════════════════════════════════════════════════════════════
Research → Interview → External LLM Review → TDD Plan

DEEP-PLAN starts by running `validate-env.sh`. This script:
  - Checks env for external LLM auth values
  - Validates external LLM access by running tiny prompt(s) programmatically

SECURITY:
  - `validate-env.sh` reads secret auth values in order to validate LLM access
  - It never publishes these values or exposes them to claude
  
 Note: DEEP-PLAN will write many .md files to the planning directory you pass it

CRITICAL: Locate plugin root BEFORE running any scripts.

The SessionStart hook injects DEEP_PLUGIN_ROOT=<path> into your context. Look for it now — it appears alongside DEEP_SESSION_ID in your context from session startup. Use it as plugin_root for all script paths.

If DEEP_PLUGIN_ROOT is in your context, run validate-env.sh directly:

bash
bash <DEEP_PLUGIN_ROOT value>/scripts/checks/validate-env.sh

Only if DEEP_PLUGIN_ROOT is NOT in your context (hook didn't run), fall back to search:

bash
find "$(pwd)" -name "validate-env.sh" -path "*/scripts/checks/*" -type f 2>/dev/null | head -1

If not found: find ~ -name "validate-env.sh" -path "*/scripts/checks/*" -path "*deep*plan*" -type f 2>/dev/null | head -1 Then run: bash <found_path>

Parse the JSON output:

json
{
  "valid": true,
  "errors": [],
  "warnings": [],
  "gemini_auth": "api_key",
  "openai_auth": true,
  "plugin_root": "/path/to/plugin"
}

Store plugin_root from the JSON output - it's used throughout the workflow.

2. Handle Environment Errors

If valid == false:

  • Show the errors to the user

If errors are critical (uv not installed, plugin root not found):

  • Stop the workflow. User must fix these before proceeding.

If errors are ONLY about missing LLM credentials (gemini_auth is null AND openai_auth is false):

AskUserQuestion:
  question: "No external LLMs configured. How should plan review be handled?"
  options:
    - label: "Use Claude Opus for review (Recommended)"
      description: "Launch an Opus subagent to review the plan"
    - label: "Exit to configure LLMs"
      description: "Stop to set up Gemini/OpenAI credentials"
    - label: "Skip external review"
      description: "Proceed without any external plan review"

Store the choice as review_mode:

  • "Use Claude Opus" → review_mode = "opus_subagent"
  • "Skip external review" → review_mode = "skip"
  • Default (LLMs available) → review_mode = "external_llm"
Environment validated:
  Gemini: {gemini_auth or "not configured"}
  OpenAI: {openai_auth ? "configured" : "not configured"}
  Review mode: {review_mode}
3. Validate Spec File Input

Check if user provided @file at invocation AND it's a spec file (ends with .md).

If NO @file was provided OR the path doesn't end with .md, output this and STOP:

═══════════════════════════════════════════════════════════════
DEEP-PLAN: Spec File Required
═══════════════════════════════════════════════════════════════

This skill requires a markdown spec file path (must end with .md).
The planning directory is inferred from the spec file's parent directory.

To start a NEW plan:
  1. Create a markdown spec file describing what you want to build
  2. It can be as detailed or as vague as you like
  3. Place it in a directory where deep-plan can save planning files
  4. Run: /deep-plan @path/to/your-spec.md

To RESUME an existing plan:
  1. Run: /deep-plan @path/to/your-spec.md

Example: /deep-plan @planning/my-feature-spec.md
═══════════════════════════════════════════════════════════════

Do not continue. Wait for user to re-invoke with a .md file path.

4. Setup Planning Session

First, check for session_id in your context. Look for DEEP_SESSION_ID=xxx which was set by the SessionStart hook. This appears in your context from when the session started.

Run setup-planning-session.py with the spec file, plugin root, review mode, and session ID:

bash
uv run {plugin_root}/scripts/checks/setup-planning-session.py \
  --file "<file_path>" \
  --plugin-root "{plugin_root}" \
  --review-mode "{review_mode}" \
  --session-id "{DEEP_SESSION_ID}"

IMPORTANT: If DEEP_SESSION_ID is in your context, you MUST pass it via --session-id. This ensures tasks work correctly after /clear commands. If it's not in your context, omit --session-id (fallback to env var).

Note: review_mode is from Step 2. If LLMs are available, use external_llm (or omit the flag).

Parse the JSON output:

This script:

  1. Validates the spec file exists and has content
  2. Creates deep_plan_config.json in the planning directory with plugin_root, planning_dir, and initial_file
  3. Detects whether this is a new or resume session
  4. Writes task files directly to ~/.claude/tasks/<task_list_id>/
  5. If sections/index.md exists, also writes section tasks (positions 22+)

If success == false: The script failed validation. Display the error and stop:

═══════════════════════════════════════════════════════════════
DEEP-PLAN: Setup Failed
═══════════════════════════════════════════════════════════════
Error: {error}

Please fix the issue and re-run: /deep-plan @path/to/your-spec.md
═══════════════════════════════════════════════════════════════

Do not continue. Wait for user to fix the issue and re-invoke.

Common errors:

  • "Spec file not found" → User provided a path to a file that doesn't exist
  • "Spec file is empty" → User provided an empty file with no content
  • "Expected a spec file, got a directory" → User provided a directory path instead of a file

Handle conflict (if present):

If conflict is present in output, this means CLAUDE_CODE_TASK_LIST_ID was set and the task list already has tasks. Use AskUserQuestion:

  • Question: "CLAUDE_CODE_TASK_LIST_ID is set to '{task_list_id}' which already contains {existing_task_count} tasks. Proceeding will overwrite these tasks with the deep-plan workflow. Continue?"
  • Options:
    • "Proceed and overwrite existing tasks" - Re-run with --force flag
    • "Exit" - Stop here, user can unset CLAUDE_CODE_TASK_LIST_ID and retry

If user chooses "Exit": Stop and tell user to unset CLAUDE_CODE_TASK_LIST_ID If user chooses "Proceed": Re-run setup-planning-session.py with --force flag added.

Handle no task list ID (mode == "no_task_list"):

If mode == "no_task_list", this is a fatal error. The workflow cannot proceed without a task list ID. Use AskUserQuestion:

Question: "Cannot proceed: No task list ID available. The SessionStart hook may not have run. How would you like to proceed?"
Options:
  - "Start a fresh session" (Recommended) - Exit Claude and start a new session
  - "Show troubleshooting steps" - Display the error_details.troubleshooting steps

If user chooses "Start a fresh session":

Please exit this Claude session and start a new one. The SessionStart hook
will capture the session ID on startup.

Command: claude --plugin-dir <plugin_path>

If user chooses "Show troubleshooting steps": Display each item from error_details.troubleshooting and STOP.

DO NOT PROCEED past step 4 if this error occurs.

Verify tasks are visible:

After the script completes successfully, run TaskList to verify the workflow tasks are visible. The output tasks_written shows how many task files were written.

Reading session context: After task writing, the task list includes context tasks with values in their subjects:

  • plugin_root=... - extract path after =
  • planning_dir=... - extract path after =
  • initial_file=... - extract path after =
  • review_mode=... - extract value after =

Print status:

Planning directory: {planning_dir}
Mode: {mode}

If mode == "resume":

Resuming from step {resume_from_step}
To start fresh, delete the planning directory files.

If resuming, skip to step {resume_from_step} in the workflow below.


Workflow

Note: All scripts use {plugin_root} from step 1's validate-env.sh output.

6. Research Decision

Read {plugin_root}/skills/deep-plan/references/research-protocol.md for details.

  1. Read the spec file (find task with subject starting with initial_file= and extract path)
  2. Extract potential research topics from the spec content (technologies, patterns, integrations)
  3. Ask user about codebase research needs (existing code to analyze?)
  4. Ask user about web research needs (present derived topics as multi-select options)
  5. Record which research types to perform in step 7

Always include testing - either research existing test setup (codebase) or ask about preferences (new project).

7. Execute Research

Read {plugin_root}/skills/deep-plan/references/research-protocol.md for details.

Based on decisions from step 6, launch research subagents:

  • Codebase research: Task(subagent_type=Explore)
  • Web research: Task(subagent_type=web-search-researcher)

If both are needed, launch both Task tools in parallel (single message with multiple tool calls).

Important: Subagents return their findings - they do NOT write files directly. After collecting results from all subagents, combine them and write to <planning_dir>/claude-research.md.

Skip this step entirely if user chose no research in step 6.

8. Detailed Interview

Read {plugin_root}/skills/deep-plan/references/interview-protocol.md for details.

Run in main context (AskUserQuestion requires it). The interview should be informed by:

  • The initial spec (from initial_file)
  • Research findings (from step 7, if any research was done)
9. Save Interview Transcript

Write Q&A to <planning_dir>/claude-interview.md

10. Write Initial Spec

Combine into <planning_dir>/claude-spec.md:

  • Initial input (read the file from task with subject initial_file=...)
  • Research findings (if step 7 was done)
  • Interview answers (from step 8)

This synthesizes the user's raw requirements into a complete specification.

11. Generate Implementation Plan

Read {plugin_root}/skills/deep-plan/references/plan-writing.md before writing anything.

Create detailed plan → <planning_dir>/claude-plan.md

CRITICAL CONSTRAINTS (from plan-writing.md):

  • Plans are prose documents, not code
  • ZERO full function implementations - that's deep-implement's job

Write for an unfamiliar reader. The plan must be fully self-contained - an engineer or LLM with no prior context should understand what we're building, why, and how just from reading this document. But it does not need to see full code implementations

12. Context Check (Pre-External Review)

Run:

bash
uv run {plugin_root}/scripts/checks/check-context-decision.py \
  --planning-dir "<planning_dir>" \
  --upcoming-operation "External LLM Review"

Read {plugin_root}/skills/deep-plan/references/context-check.md for handling the output.

  • If user chooses "Continue", proceed to step 13
  • If user chooses "/clear + re-run", they will restart with fresh context (file-based recovery resumes here)
13. External LLM Review

Read {plugin_root}/skills/deep-plan/references/external-review.md for the full protocol.

Check review_mode from task with subject review_mode=... and follow the appropriate path:

  • external_llm → Run review.py script
  • opus_subagent → Launch opus-plan-reviewer subagent
  • skip → Skip to step 16
14. Integrate External Feedback

Analyze the suggestions in <planning_dir>/reviews/.

Remember that you are the authority on what to integrate or not. It's OK if you decide to not integrate anything.

Step 1: Write <planning_dir>/claude-integration-notes.md documenting:

  • What suggestions you're integrating and why
  • What suggestions you're NOT integrating and why

Step 2: Update <planning_dir>/claude-plan.md with the integrated changes.

Show full SKILL.md (736 more words)Show less
15. User Review of Integrated Plan

Use AskUserQuestion:

The plan has been updated with external feedback. You can now review and edit claude-plan.md.

If you want Claude's help editing the plan, open a separate Claude session - this session
is mid-workflow and can't assist with edits until the workflow completes.

When you're done reviewing, select "Done" to continue.

Options: "Done reviewing"

Wait for user confirmation before proceeding.

16. Apply TDD Approach

Read {plugin_root}/skills/deep-plan/references/tdd-approach.md for details.

Verify testing context exists in claude-research.md. If missing, research (existing codebase) or recommend (new project).

Create claude-plan-tdd.md mirroring the plan structure with test stubs for each section.

17. Context Check (Pre-Section Split)

Run:

bash
uv run {plugin_root}/scripts/checks/check-context-decision.py \
  --planning-dir "<planning_dir>" \
  --upcoming-operation "Section splitting"

Read {plugin_root}/skills/deep-plan/references/context-check.md for handling the output.

  • If user chooses "Continue", proceed to step 18
  • If user chooses "/clear + re-run", they will restart with fresh context (file-based recovery resumes here)
18. Create Section Index

Read {plugin_root}/skills/deep-plan/references/section-index.md for details.

Read claude-plan.md and claude-plan-tdd.md. Identify natural section boundaries and create <planning_dir>/sections/index.md.

CRITICAL: index.md MUST start with a SECTION_MANIFEST block. See the reference for format requirements and examples.

Write index.md before proceeding to section file creation.

19. Generate and Write Section Tasks

Run generate-section-tasks.py to write section tasks directly to disk:

bash
uv run {plugin_root}/scripts/checks/generate-section-tasks.py \
  --planning-dir "<planning_dir>" \
  --session-id "{DEEP_SESSION_ID}"

IMPORTANT: If DEEP_SESSION_ID is in your context, you MUST pass it via --session-id. This ensures tasks work correctly after /clear commands. If it's not in your context, omit --session-id (fallback to env var).

What this script does:

  1. Reads sections/index.md to get the section list
  2. INSERTs batch and section tasks starting at position 19
  3. SHIFTS Final Verification and Output Summary to positions after section tasks
  4. Updates all dependencies to reflect new positions

Handle based on result:

  • If success == false: Read error and fix the issue (common: missing/invalid SECTION_MANIFEST in index.md, no DEEP_SESSION_ID). Re-run until successful.
  • If state == "complete": All sections already written, skip to Final Verification.
  • Otherwise: Tasks were written successfully.

Verify section tasks are visible:

After the script completes successfully, run TaskList to see the updated task structure. The output tasks_written shows how many task files were written (section tasks + Final Verification + Output Summary).

Task positions after insertion:

  • Position 19+: Batch and section tasks
  • Final Verification: Position 19 + section_task_count
  • Output Summary: Position 19 + section_task_count + 1

Task list includes batch coordination tasks (subjects like "Run batch 1 section subagents") and section tasks (subjects like "Write section-01-setup.md"). Sections are blocked by their batch task, enabling parallel execution within each batch.

20. Write Section Files (Parallel Subagents)

Read {plugin_root}/skills/deep-plan/references/section-splitting.md for the batch execution loop.

For each batch:

  1. Mark batch task in_progress (find by subject "Run batch N section subagents")
  2. Run generate-batch-tasks.py --batch-num N → get JSON with prompt_files array
  3. Launch Task calls for ALL prompt files in a single message (parallel execution)
  4. Each Task: subagent_type="section-writer", prompt="Read {prompt_file} and execute the instructions."
  5. Wait for all subagents to complete
  6. Verify section files were written (SubagentStop hook writes them automatically)
  7. Mark each section task completed (find by subject "Write {filename}")
  8. Mark batch task completed
  9. If more batches remain, repeat from step 1 with next batch number

Validation After Each Batch:

Hooks execute in isolation - Claude doesn't see success/failure. After subagents complete:

bash
ls {planning_dir}/sections/section-*.md | wc -l

Compare count to expected sections. If any files are missing:

  1. Re-run the missing section's subagent
  2. If still failing, fall back to manual: parse subagent response JSON and Write the file
21. Final Status & Cleanup

Verify all section files were created successfully by running check-sections.py one final time. Confirm state is "complete".

22. Output Summary

Print generated files and next steps.


Resuming After Compaction

CRITICAL: When resuming this workflow after context compaction, the detailed instructions from this file are lost. The task list is preserved but may not have enough detail. Follow these rules:

  1. ALWAYS read the reference file for your current step before proceeding

    • Task descriptions include hints like "(read section-index.md)" - follow them
    • Reference files are in {plugin_root}/skills/deep-plan/references/
    • Get plugin_root from task with subject plugin_root=...
  2. NEVER skip steps - follow the task list exactly in order

    • If a task says "Run generate-section-tasks.py", run the script
    • If a task says "use section-writer subagents", use subagents (don't write files directly)
    • You can always re-read in the /deep-plan skill if unsure
  3. If message says "MISSING PREREQUISITE" - a required file is missing but later files exist

    • This means a step was skipped but later steps ran anyway
    • Resume from the indicated step and OVERWRITE any subsequent files
    • Example: if claude-plan-tdd.md is missing but sections/index.md exists, create the TDD plan, then recreate the index (the old index was made without TDD context)
  4. Key reference files by step:

    • Step 6-7: research-protocol.md
    • Step 8: interview-protocol.md
    • Step 11: plan-writing.md
    • Step 13: external-review.md
    • Step 16: tdd-approach.md
    • Step 18: section-index.md (CRITICAL - has required format)
    • Step 20: section-splitting.md (subagent workflow)

© piercelamb, 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 8 other files (references) in skills/deep-plan of piercelamb/deep-plan.

  • SKILL.md
  • references/context-check.md
  • references/external-review.md
  • references/interview-protocol.md
  • references/plan-writing.md
  • references/research-protocol.md
  • references/section-index.md
  • references/section-splitting.md
  • references/tdd-approach.md

Open the folder on GitHubat commit 9cc88c4

Compare with similar skills

Deep Plan 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.

Deep Plan compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Deep Plan this skillpiercelamb/deep-plan101—~4.8kAutomated safety check: PassMIT
Plan Py4vaspvasp-dev/py4vasp100—~2.3kAutomated safety check: PassApache-2.0
Test-First Implementation Plangittower/git-flow-next458—~1.3kAutomated safety check: NotesCustom licence
Writing PlansProgrammerAnthony/Expert-Coding-Harness235—~876Automated safety check: PassMIT
Solo BuildLeoYeAI/openclaw-master-skills2.2k—~4.6kAutomated safety check: NotesMIT
Executing Plansdev-toolings/superpowers-symfony222—~405Automated safety check: NotesMIT

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Questions about Deep Plan

What does Deep Plan do?

Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review. Deep Plan is an agent skill from piercelamb/deep-plan. Creates detailed, sectionized, TDD-oriented implementation plans through research, stakeholder interviews, and multi-LLM review.

When should I use Deep Plan?

Deep Plan fits situations like: planning features that need thorough pre-implementation analysis; tasks that involve Planning; tasks that involve Test-driven development.

How do I install Deep Plan in Claude Code?

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

How do I install Deep Plan in Codex?

Run `npx skills add piercelamb/deep-plan --skill deep-plan -a codex`. Or copy the skill folder (skills/deep-plan in piercelamb/deep-plan) into .agents/skills/deep-plan in your project. Codex loads it when a task matches its description.

Can I use Deep Plan 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 piercelamb/deep-plan --skill deep-plan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deep-plan, .gemini/skills/deep-plan, .github/skills/deep-plan and .opencode/skills/deep-plan in your project.

What does Deep Plan need to run?

Going by SKILL.md and its folder, Deep Plan needs the command-line tools its instructions call (uv and bash). Our summary lists: Python 3. Compatibility (from SKILL.md): Requires uv (Python 3.11+), Gemini or OpenAI API key for external review.

Does Deep Plan access the network?

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

Is Deep Plan 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 Deep Plan use?

Deep Plan is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deep Plan use?

About 4.8k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 8.7k tokens, read only when the agent opens those files.

What are the alternatives to Deep Plan?

Skills that share tags, products or a category with Deep Plan: Plan Py4vasp (vasp-dev/py4vasp, 100 stars), Test-First Implementation Plan (gittower/git-flow-next, 458 stars), Writing Plans (ProgrammerAnthony/Expert-Coding-Harness, 235 stars) and Solo Build (LeoYeAI/openclaw-master-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deep Plan?

piercelamb (a GitHub user) maintains it in piercelamb/deep-plan, which has 101 GitHub stars. The repository was last updated on June 21, 2026.

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