Using Entire

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:

• A request phrased as "research X", "investigate X", "look into X", "dig into X", or "find out how X works"
• 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

For a research- or investigate-shaped request, the very first action is an entire search via the search skill. Do not open source files or grep first; the recorded history says what was tried and why, and code reads come after to confirm it.

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:

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

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

entire checkpoint 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 checkpoint 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