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.
npx agentmods add skills/tentacleopera/switchboard/deep-planningnpx skills add TentacleOpera/switchboard --skill deep-planninggit clone --depth 1 https://github.com/TentacleOpera/switchboardWrote 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.
[](https://agentmods.dev/skills/tentacleopera/switchboard/deep-planning)<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>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.
| Model | Per session | Once 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 |
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.
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:
- Planning objectives: What specific change will be planned and what questions will be answered
- 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
- Analysis strategy: Codebase search patterns and web research domains to target
- Expected sources: Types of sources (code files, docs, Stack Overflow, official docs, etc.)
- Scope: What will and won't be covered (files, modules, external research)
- Estimated phases: Brief outline of analysis phases
- Estimated time: Time estimate based on chosen depth level
- 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.
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.
- 4d ago First seen · 133 lines · 0 tokens per session scan A 454992f520b6
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.
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brainstorming
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auto-perf-optimize
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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.
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.
cpu-profile-analysis
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