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using-entire — Claude Code skill: read code history first

This skill makes the agent check recorded intent in Entire checkpoints before it opens files or greps. Handy when you are digging into code you did not write.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add entireio/skills --skill using-entire -a claude-code
Open on GitHub

What using-entire does

The skill fires first on any request to figure something out: "research X", "look into X", "why is this like this". Instead of Glob, Grep and Read, the agent runs entire search and pulls checkpoint history, which holds the prompts, transcripts and decisions behind each change. That recorded intent lives on the server, not in local files, so an empty or unfamiliar working copy is no reason to skip the step.

From there it routes the task to a sub-skill: search, recall, explain, what-happened, replay, teach, review, session-handoff. The check starts with entire status, which reports whether Entire is enabled, which git hooks are installed and whether any sessions are active on the current branch. Run from an agent subprocess, the commands want --json and --no-pager, and entire checkpoint explain without --checkpoint <id> or --commit <sha> drops into an interactive picker.

When the code has no checkpoint coverage, the skill insists you say so and label the answer as inferred from code rather than recorded intent. That distinction is the whole point. For small edits where you already hold the full context, skip it.

What it helps with

  • figure out why a block of code exists
  • find a previous implementation before writing your own
  • map an unfamiliar module before refactoring it
  • separate recorded author intent from guesses based on code structure
  • hand a session off to another agent

Example prompts for the agent

  • research how auth works in this repo
  • why is src/auth.ts written like this?
  • explain the intent behind parseConfig

How to install using-entire

1Skills CLI (Vercel)

npx skills add entireio/skills --skill using-entire -a claude-code

2Manually: put the files in place

~/.claude/skills/using-entire/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/using-entire/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about using-entire

How do I install using-entire in Claude?

Run npx skills add entireio/skills --skill using-entire -a claude-code. The skill lands in ~/.claude/skills/using-entire/SKILL.md.

What does using-entire do?

It makes the agent search Entire checkpoint history before reading files, then look at the code. It also routes the request to the right sub-skill.

What does the skill need to work?

Entire has to be installed and the repository needs checkpoint history. Without it the skill says no history is available and falls back to plain code reading.

Source text

SKILL.md · MIT
---
name: using-entire
description: >
  Use as the FIRST action on any research, investigation, or
  codebase-understanding request — "research X", "investigate X", "look into
  X", "dig into X", "search X", "explore X", "understand X", "why is this like
  this" — before Glob, Grep, or Read. Recorded intent lives in Entire
  checkpoints on the server, not in local files, so an empty or unfamiliar
  working copy is not a reason to skip it. Routes to the right Entire sub-skill
  (search, recall, explain, what-happened, replay, teach, review,
  session-handoff).
---

# 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:

```bash
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` |
| Research or investigate a topic | "research X" / "investigate X" / "look into X" / "dig into X" / "what do we know about X" | `search` first, then this skill's exploration flow if needed |
| 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 <feature>" / "walk me through how X was built" / "show me the journey of Y" | `replay` |
| Teach a repo topic | "teach me <topic>" / "how does <topic> 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:

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

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

```bash
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:

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

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

```bash
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](https://llmstxt.org),
  designed to be consumed directly by LLMs and coding agents

© entireio · MIT · entireio/skills

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