deep-planning

deep-planning is a skill for Claude Code, Codex from TentacleOpera/switchboard. It costs 0 tokens per session (1,720 once invoked), scanned A, original, MIT.

A detailed planning workflow for complex code changes that combines analysis of the existing codebase with research into outside development practices. It is intended for changes that may affect several files or technical areas.

In plain words
What is it for?
It is for planning refactors, new features, cross-cutting changes, and work where understanding both the repository and external guidance matters.
Why use it?
It helps produce plans that account for the current code, architecture, security, performance, and relevant established practices before implementation begins.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/tentacleopera/switchboard/deep-planning
Any agent
npx skills add TentacleOpera/switchboard --skill deep-planning
Clone the repo
git clone --depth 1 https://github.com/TentacleOpera/switchboard

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for deep-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/tentacleopera/switchboard/deep-planning.svg)](https://agentmods.dev/skills/tentacleopera/switchboard/deep-planning)
Your own site
<a href="https://agentmods.dev/skills/tentacleopera/switchboard/deep-planning"><img src="https://agentmods.dev/badge/skills/tentacleopera/switchboard/deep-planning.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,720 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.01720
Opus 5 $0.00000 $0.00860
Sonnet 5 $0.00000 $0.00344
Haiku 4.5 $0.00000 $0.00172

Measured 4d ago against content hash 454992f520b6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deep-planning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/protocols/deep-planning/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Deep Planning for Codebase Changes

Purpose

Perform comprehensive planning for codebase edits by combining internal codebase analysis with external best practices research. This hybrid approach produces implementation plans that account for both codebase reality and industry standards.

When to Use

  • User requests complex code changes that require understanding existing architecture
  • Planning refactoring, feature additions, or cross-cutting concerns
  • User needs "super plans" that go beyond simple code analysis
  • Assessing impact of changes across multiple files/modules
  • Evaluating security, performance, or best practice implications

Research Protocol

Phase 0: Planning Proposal

Before conducting any analysis, propose a planning approach to the user for approval:

Present the plan with:

  1. Planning objectives: What specific change will be planned and what questions will be answered
  2. Research depth options: Present source count options for user to choose:
    • Quick (5-10 sources): Rapid overview, codebase-only analysis, high-level plan
    • Standard (15-30 sources): Balanced depth, codebase + targeted web research, moderate detail
    • Deep (50-100+ sources): Comprehensive analysis, extensive web research, exhaustive coverage
    • Academic (100-200+ sources): Scholarly rigor, includes academic papers, systematic review
  3. Analysis strategy: Codebase search patterns and web research domains to target
  4. Expected sources: Types of sources (code files, docs, Stack Overflow, official docs, etc.)
  5. Scope: What will and won't be covered (files, modules, external research)
  6. Estimated phases: Brief outline of analysis phases
  7. Estimated time: Time estimate based on chosen depth level
  8. Clarifying questions: If the codebase context or requirements are thin or missing crucial details, formulate 2-3 specific clarifying questions and include them at the end of the proposal.

Wait for user response:

  • If approved: Proceed with Phase 1
  • If amendments requested: Revise plan and re-present
  • If rejected: Clarify requirements and propose new plan
  • If clarifying questions were posed: wait for answers before proceeding to Phase 1. Incorporate answers into the refined planning approach.

Read the full file on GitHub · 133 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 133 lines · 0 tokens per session scan A 454992f520b6

Subscribe to this mod's changes

deep-planning is a skill published in the GitHub repository TentacleOpera/switchboard (213 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,720 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens