go-deep

go-deep is a skill for Claude Code, Codex from insideout-ai/make-it-work. It costs 46 tokens per session (4,879 once invoked), scanned A, original, MIT.

A system for creating three layers of documentation for a codebase: general instructions, orientation maps, and focused domain or use-case guides.

In plain words
What is it for?
Use it when onboarding a project to Claude Code or reviewing and updating its documentation after new areas or patterns are added.
Why use it?
It helps a coding agent understand an unfamiliar project quickly and keep its guidance organized as the codebase changes.

Skill for Claude CodeCodex

Part of the make-it-work plugin — 5 skills shipped together

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/insideout-ai/make-it-work/go-deep
Any agent
npx skills add insideout-ai/make-it-work --skill go-deep
Clone the repo
git clone --depth 1 https://github.com/insideout-ai/make-it-work

Made for: Claude Code, Codex.

Or install make-it-work, the plugin that ships this one along with the rest of its 5 skills.

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 go-deep

README.md
[![agentmods](https://agentmods.dev/badge/skills/insideout-ai/make-it-work/go-deep.svg)](https://agentmods.dev/skills/insideout-ai/make-it-work/go-deep)
Your own site
<a href="https://agentmods.dev/skills/insideout-ai/make-it-work/go-deep"><img src="https://agentmods.dev/badge/skills/insideout-ai/make-it-work/go-deep.svg" alt="Measured on agentmods" height="20"></a>
Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,879 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 $0.00046 $0.04879
Opus 5 $0.00023 $0.02440
Sonnet 5 $0.00009 $0.00976
Haiku 4.5 $0.00005 $0.00488

Measured 3d ago against content hash c3f1cc8f3a9b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

go-deep 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 3d 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.

skills/go-deep/SKILL.md · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Create a 3-tier layered documentation system from scratch. The goal is to keep context concise and well-organized while ensuring the AI agent has all necessary information.

Information Gathering Process

Phase 1: Code Discovery First, analyze the existing codebase to understand what's already there:

  • Scan the directory structure to identify main folders and organization
  • Read key configuration files (package.json, requirements.txt, etc.) to identify tech stack
  • Look for existing documentation (README, docs folders, inline comments)
  • Identify main code patterns, components, and modules
  • Search for entry points (main.py, index.js, app routes, etc.)
  • Find existing use case implementations or feature areas
  • Identify domain models, entities, or data structures

Phase 2: Targeted Questions After code discovery, announce how many questions you have, then ask each one using the AskUserQuestion tool — one call per question. Only ask about information that could not be determined from code analysis.

Announce first:

"I've analyzed the codebase and have [N] questions. I'll ask them one at a time."

For each question:

  • Call AskUserQuestion with:
    • question: "Question [X] of [N] · [Category]: [The question]\n\n[One sentence explaining why this matters.]" — where [Category] is either Product/domain (questions 1–10) or Architecture/technical (questions 11–20).
    • options: up to 3 substantive pre-enumerated choices + always { label: "Skip — clear from code", description: "Already determined from code analysis; no input needed." } (last). Tool caps at 4 options total. Use open-ended format (options omitted except Skip) only when the answer truly cannot be pre-enumerated.
    • Always recommend one option: place it first and append (Recommended) to its label.
    • Never place Skip first — it must always be last.
  • Wait for the user's response before calling AskUserQuestion for the next question.
  • Record each answer (or skip) before proceeding.

After the last question: "All questions answered. Confirming scope..."

Product & Domain Questions:

  1. What is the primary purpose/goal of this project? (1-2 sentences) [if not clear from README/docs]
  2. Who are the main user types/actors in this system? [if not evident from code]
  3. What are the key use cases this application supports? (list them with brief descriptions) [if not explicitly documented]
  4. What are the main user journeys or workflows users go through? [if implementation doesn't make it clear]
  5. What are the core domain concepts/entities in this product? [ask for clarification on any unclear entities]
  6. What are the critical business rules or validation rules users encounter? [if not evident from validation code]
  7. What triggers each use case? (user actions, scheduled events, external APIs) [if not clear from implementation]
  8. What are the key product features or capabilities? [if features aren't well-defined in code]
  9. Are there different user roles with different permissions/capabilities? [if auth/permissions exist but aren't clear]
  10. What are the success criteria or outcomes for each major use case? [if not documented]

Architecture & Technical Questions: 11. What is the tech stack? [only ask about non-obvious choices or integrations] 12. What are the main functional domains/modules in the codebase? [ask for clarification on unclear domains] 13. What is the directory/folder structure logic? [only if organization isn't self-evident] 14. What are the main development commands? [if not in package.json scripts or Makefile] 15. Are there any critical platform constraints or rules? [if not documented in code/config] 16. What is the testing approach? [if tests exist but approach isn't clear] 17. Are there environment variables that need to be documented? [supplement what's found in .env.example] 18. What are common pitfalls or gotchas developers should know? [developer knowledge not in code] 19. Are there any special import aliases or path configurations? [if not in tsconfig/webpack config] 20. What external services or APIs does the system integrate with? [clarify purpose if integrations exist]

Read the full file on GitHub · 208 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. 3d ago First seen · 208 lines · 46 tokens per session scan A c3f1cc8f3a9b

Subscribe to this mod's changes

go-deep is a skill published in the GitHub repository insideout-ai/make-it-work (11 stars, last pushed 22d ago), licensed MIT. It adds 46 tokens to every session and 4,879 once invoked, about $0.0002 per session on Opus 5. 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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