using-entire

name	using-entire
description	Use for any codebase exploration or understanding task — reads historical intent from Entire checkpoints instead of guessing. Orchestrates other Entire skills to give the agent provenance-backed answers about code.

Using Entire

Your default when exploring or understanding code should be: read the recorded intent, don't guess.

Entire checkpoints capture the prompts, transcripts, and decisions behind every agent-driven change. When you need to understand why code exists or how a module evolved, look up the checkpoint history first. Only fall back to inference from code structure when no history is available — and label that explicitly.

When This Skill Activates

Use this skill whenever the task involves:

• Exploring or understanding an unfamiliar module or file
• Preparing to refactor, extend, or debug code you didn't write
• Answering "why is this like this?" or "what was the intent?"
• Doing pre-work research before making changes
• Any codebase exploration where historical context would help

Do not use this skill for simple, well-understood edits where you already have full context (e.g. "add a comment to line 5").

First Step: Check Repository Status

Before diving into specific lookups, check whether Entire is available and the repository has checkpoint history:

entire status

This tells you whether Entire is enabled, which git hooks are installed, whether the metadata branch is initialized, and any active sessions on the current branch.

Scenario Routing

When this skill activates, determine which sub-skill best fits the user's need:

Scenario	Typical user expressions	Delegate to
Code block provenance	"why is this like this" / "wtf is going on" / "what happened at src/auth.ts:42" / "tell me why this changed"	what-happened
Find prior work	"has anyone done X before" / "search past work for rate limiting" / "find the previous implementation"	search
Recall a task playbook	"have we done this before" / "recall how we did X" / "how did we do this last time"	recall
Replay feature history	"replay " / "walk me through how X was built" / "show me the journey of Y"	replay
Teach a repo topic	"teach me " / "how does work in this repo" / "give me a lesson on X"	teach
Understand original intent	"explain this function" / "explain parseConfig" / "what was the intent behind src/auth.ts"	explain
Generate a dispatch / summary	"summarize recent work" / "generate a dispatch" / "what was accomplished this week"	run entire dispatch directly
Code review with intent context	"review these changes" / "review this branch" / "audit before merging"	review
Hand off to another agent	"hand off this session" / "continue in another agent" / "pick up the codex session"	session-handoff
Convert session to skill	"turn this into a skill" / "make a skill from this session"	session-to-skill
Link session to other repos	"link session to other repos" / "attach session to the foo repo"	session-crosslink
General exploration	"understand this module" / "explore how auth works" / "help me understand the codebase"	(self — see below)

If the scenario clearly maps to a sub-skill, delegate entirely to that skill's workflow. Do not duplicate their steps here.

General Exploration Flow

When no specific sub-skill fits — e.g. the user asks to "understand this module" or "explore how auth works" — use this flow:

1. Identify key files in the target area (entry points, main types, core functions).

2. Check for checkpoint coverage on each key file:

git log --format='%H %s' -5 -- <file>

Look for Entire-Checkpoint: trailers in the commit bodies:

git log --format='%H %b' -5 -- <file> | grep -B1 'Entire-Checkpoint:'

3. Read intent for covered commits — extract the checkpoint ID from the trailer, then use JSON output for non-interactive consumption:

entire explain --checkpoint <checkpoint-id> --json --no-pager

If you only have a commit hash (not a checkpoint ID), use:

entire explain --commit <sha> --no-pager

4. Synthesize: combine the recorded intent (from checkpoints) with the current code structure to build a comprehensive understanding.

5. Report your findings, clearly distinguishing:

   • Facts backed by checkpoint transcripts (label as "recorded intent")
   • Observations inferred from code alone (label as "inferred from code")

AI Agent Usage Notes

When running Entire commands from an agent subprocess:

• Always pass --json for commands that support it and might prompt interactively. --json implies non-interactive output.
• Always pass --no-pager to prevent pager activation in subprocess contexts.
• Always specify --checkpoint <id> or --commit <sha> for entire explain — without a locator, the command falls back to an interactive picker.

When No Checkpoints Exist

If the target code predates Entire or has no checkpoint coverage:

1. Say explicitly: "No Entire checkpoint history is available for this code."
2. Proceed with standard code understanding (read source, check git log, trace call sites).
3. Label all explanations as "inferred from code — not backed by recorded intent."
4. If the user wants deeper history, suggest search with related keywords — there may be checkpoints on related work even if the specific file isn't directly covered.

Transparency Principle

Always make it clear whether your answer comes from:

• Checkpoint-backed history — you read the actual session transcript and know the developer's stated intent
• Code inference — you analyzed the code structure and are reasoning about probable intent

Never present inferred intent as if it were recorded fact. The core value of this workflow is that distinction.

References

For a deeper understanding of Entire's capabilities beyond what the sub-skills cover, consult these resources:

• CLI help: run entire help to see all available commands and flags
• Documentation: https://docs.entire.io/llms.txt — AI-friendly documentation index following the llms.txt standard, designed to be consumed directly by LLMs and coding agents