Agent skill

Session To Skill

by entireio in entireio/skills

A skill your agent uses when the user wants to turn one or more Entire-tracked sessions, checkpoints, or repeated agent workflows into a reusable agent skill.

MITAuto-check passed

Install Session To Skill

skills CLI
$ npx skills add entireio/skills --skill session-to-skill -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install entireio/skills session-to-skill --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/entireio/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/session-to-skill .claude/skills/session-to-skill && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
session-to-skill
GitHub stars
223
Token cost
~2.2k tokens
SKILL.md length
1,013 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the user wants to turn one or more Entire-tracked sessions, checkpoints, or repeated agent workflows into a reusable agent skill.

  • Works in 6 steps: Clarify The Skill Target → Infer The Repeated Pattern → Read Source Material → …
  • The user wants to turn one
  • SKILL.md covers Response Format, Rules and Workflow
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Session To Skill is an agent skill from entireio/skills. Use when the user wants to turn one or more Entire-tracked sessions, checkpoints, or repeated agent workflows into a reusable agent skill.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: ✨ Cross-agent skills that help coding agents use Entire context from Checkpoints, sessions, and git history to search past work, explain code, and hand off sessions. The licence is MIT.

When your agent uses it

  • The user wants to turn one
  • More Entire-tracked sessions
  • Repeated agent workflows into a reusable agent skill

Example prompts

  • “/session-to-skill”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Clarify The Skill Target
  2. Infer The Repeated Pattern
  3. Read Source Material
  4. Extract Durable Lessons
  5. Draft The Skill
  6. Deliver And Offer Installation

What it can do on your machine

Read from SKILL.md and the folder at commit fe5266f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Session To Skill loads about 2.2k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 1,013 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from entireio/skills at commit fe5266f, republished under its MIT licence (© entireio). 1,013 words, ~2,169 tokens.

Download SKILL.mdSave it as .claude/skills/session-to-skill/SKILL.md (or your agent's skills folder).
name
session-to-skill
description
Use when the user wants to turn one or more Entire-tracked sessions, checkpoints, or repeated agent workflows into a reusable agent skill.

Session To Skill

Use this skill to help the user turn Entire session history into a focused skill draft.

The goal is not to convert a whole transcript mechanically. Treat sessions and checkpoints as source material, then extract the reusable workflow the user wants to repeat.

Response Format

Begin the first response to this skill invocation with the line:

Entire Session To Skill:

followed by a blank line, then the content.

  • Apply the header to the first response of the invocation only. Do not re-print it on follow-up turns within the same invocation.
  • Do not include the header on error or early-exit responses, such as when Entire is not installed, the current directory is not a git repository, no relevant sessions are found, or the user has not identified what reusable behavior they want.

Rules

  1. First identify the reusable behavior the skill should capture. If the user has not said what the skill should help with, ask that question before reading transcripts.
  2. Use Entire history as evidence. Prefer entire search, entire session current, session metadata files, and entire checkpoint explain over asking the user to paste old transcripts.
  3. A skill draft should be focused on future behavior, not a recap of the session. Preserve durable workflow, repo conventions, user corrections, commands, validation, and things to avoid.
  4. When several sessions may be relevant, summarize the repeated workflow pattern, recommend a source set, and ask the user to confirm before expanding transcripts.
  5. Do not write, install, or overwrite a skill file unless the user explicitly approves the destination. By default, present the SKILL.md draft in the response.
  6. Do not include secrets, private credentials, raw logs, or unnecessary transcript detail in the generated skill.

Workflow

1. Clarify The Skill Target

If the user gives a clear target, continue. Examples:

  • "turn my blog publishing workflow into a skill"
  • "make a skill from session 019..."
  • "I keep doing release note drafting; make that reusable"

If the target is vague, ask:

text
What should this skill help you do repeatedly?

If the user wants a specific skill name, use it. Otherwise infer a short hyphen-case name from the target, then confirm it before writing files.

2. Infer The Repeated Pattern

If the user gives a checkpoint ID, skip to checkpoint expansion.

If the user gives a session ID, read the matching session metadata from:

text
.git/entire-sessions/<session-id>.json

If the user describes a repeated workflow but does not give a session or checkpoint, search Entire history with terms from the target:

bash
entire search "<workflow terms>" --json

Use repo, branch, author, or date filters when the user provides them:

bash
entire search "<workflow terms>" --json --repo owner/name --branch branch-name --author "Name" --date month

Interpret search results carefully:

  • If entire search returns valid JSON with "total": 0 or an empty results array, do not call it an authentication failure. Say no indexed matches were found, then fall back to local session metadata.
  • Only say authentication is required if the command output explicitly says authentication, login, or credentials are required.
  • If search fails for any other reason, report the short error and fall back to local session metadata when available.

When falling back locally, inspect .git/entire-sessions/*.json and match against last_prompt, description, files_touched, started_at, last_interaction_time, agent_type, and the user's workflow terms.

Review the top results and infer the repeated workflow pattern before showing raw session choices. Lead with the pattern, not the IDs.

Present:

  • the repeated workflow you think the user wants to capture
  • the strongest source set you recommend using
  • what each source contributes in plain language, such as "core workflow", "image handling", "validation", or "copy-editing pattern"
  • any sessions you plan to ignore because they look metadata-only, duplicate, or one-off

Keep session IDs and checkpoint IDs as supporting details, not the main decision surface. Ask the user to confirm the pattern and source set before expanding detailed transcripts.

Example:

text
I found a repeated workflow: publishing blog posts in entire.io from drafts, using repo-specific front matter, slugged asset folders, user-provided images, and website checks.

I recommend using the strongest matching sessions as source material:
- core workflow: <session-id>
- image handling: <session-id>
- validation mechanics: <session-id>

I will ignore metadata-only or one-off edit sessions unless you want them included. Should I continue with this source set?
Show full SKILL.md (399 more words)Show less
3. Read Source Material

For a checkpoint, run:

bash
entire checkpoint explain --checkpoint <checkpoint-id> --full --no-pager

If full output fails and the user wants more detail, fall back to:

bash
entire checkpoint explain --checkpoint <checkpoint-id> --raw-transcript --no-pager

For an active or current session, prefer:

bash
entire session current

If the installed Entire CLI does not support the singular session group yet, use the session metadata fallback directly: inspect .git/entire-sessions/*.json, pick the relevant session by last_interaction_time, started_at, agent_type, or the user's requested agent, then extract transcript_path.

When reading a raw transcript, extract relevant conversation and tool-call lines without dumping them to the user:

bash
grep -E '"type":"(message|function_call|user|assistant)"' <transcript_path> | cut -c1-2000

For large transcripts, inspect the first prompts and final state first:

bash
grep -E '"type":"(message|function_call|user|assistant)"' <transcript_path> | head -40 | cut -c1-2000
grep -E '"type":"(message|function_call|user|assistant)"' <transcript_path> | tail -160 | cut -c1-2000

If the session metadata lists files touched, inspect only files needed to understand durable conventions. Avoid broad repo exploration unless the skill target requires it.

4. Extract Durable Lessons

Before drafting, privately identify:

  • the repeated goal the future skill should accomplish
  • triggers that should activate the skill
  • required inputs the future agent should ask for
  • repo-specific paths, file formats, front matter, naming, or asset placement
  • commands and checks that proved the workflow worked
  • user corrections and preferences from the session
  • failed approaches or behaviors to avoid
  • what was one-off and should not go into the skill

If multiple sessions were selected, combine only the repeated or clearly reusable lessons. Do not average contradictory instructions; ask the user to choose when sessions disagree.

5. Draft The Skill

Create a complete SKILL.md draft with required front matter:

markdown
---
name: <hyphen-case-skill-name>
description: Use when <specific trigger and task>.
---

The body should include:

  • a short purpose statement
  • clear rules or guardrails
  • a step-by-step workflow
  • exact commands only when they are part of the reusable behavior
  • expected outputs and validation steps
  • failure handling or when to ask the user

Keep the skill concise. Do not include the session recap, full transcript excerpts, checkpoint IDs, or implementation notes unless they are essential to future use.

6. Deliver And Offer Installation

Present the SKILL.md draft first unless the user already gave an explicit write path.

After presenting the draft, ask whether the user wants it installed globally. Recommend the cross-agent path:

text
~/.agents/skills/<skill-name>/SKILL.md

Use this install prompt shape:

text
Do you want me to install this skill globally?

Recommended:
- Cross-agent: ~/.agents/skills/<skill-name>/SKILL.md

Other options:
- Codex only: ~/.codex/skills/<skill-name>/SKILL.md
- Write to a repo-local draft: skills/<skill-name>/SKILL.md
- Leave as draft only

Only write files after the user chooses a destination. If the destination already exists, ask before overwriting it.

Do not create symlinks unless the user explicitly asks for a development-linked install. If they ask for a symlink, explain the source and target paths before creating it.

After writing a skill file, summarize:

  • where it was written
  • which session(s) or checkpoint(s) informed it
  • any assumptions or open questions

© entireio, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/session-to-skill of entireio/skills.

Open the folder on GitHubat commit fe5266f

Compare with similar skills

Session To Skill next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Session To Skill compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Session To Skill this skillentireio/skills223—~2.2kAutomated safety check: PassMIT
Entire Searchjdx/communique1191 repos~623Automated safety check: PassMIT
Grep For Mock Module Across The Entire Test File And Confirm EveZaxbyHub/opencode-swarm493—~363Automated safety check: PassMIT
Deploy Release Testvercel/next.js143k—~1.2kAutomated safety check: PassMIT
Local AI Agentsmicrosoft/ai-agents-for-beginners77k—~1.3kAutomated safety check: PassMIT
Cf Crawldavila7/claude-code-templates32k—~2.6kAutomated safety check: NotesMIT

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  • Recall

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  • Replay

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  • Session Crosslink

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Questions about Session To Skill

What does Session To Skill do?

A skill your agent uses when the user wants to turn one or more Entire-tracked sessions, checkpoints, or repeated agent workflows into a reusable agent skill. Session To Skill is an agent skill from entireio/skills. Use when the user wants to turn one or more Entire-tracked sessions, checkpoints, or repeated agent workflows into a reusable agent skill.

When should I use Session To Skill?

Session To Skill fits situations like: the user wants to turn one; more Entire-tracked sessions; repeated agent workflows into a reusable agent skill.

How do I install Session To Skill in Claude Code?

Run `npx skills add entireio/skills --skill session-to-skill -a claude-code`. Or copy the skill folder (skills/session-to-skill in entireio/skills) into .claude/skills/session-to-skill in your project. Claude Code loads it when a task matches its description.

How do I install Session To Skill in Codex?

Run `npx skills add entireio/skills --skill session-to-skill -a codex`. Or copy the skill folder (skills/session-to-skill in entireio/skills) into .agents/skills/session-to-skill in your project. Codex loads it when a task matches its description.

Can I use Session To Skill in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add entireio/skills --skill session-to-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/session-to-skill, .gemini/skills/session-to-skill, .github/skills/session-to-skill and .opencode/skills/session-to-skill in your project.

What does Session To Skill need to run?

SKILL.md names no scripts, command-line tools or credentials: Session To Skill is instructions for the agent only.

Does Session To Skill access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Session To Skill safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Session To Skill use?

Session To Skill is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Session To Skill use?

About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Session To Skill?

Skills that share tags, products or a category with Session To Skill: Entire Search (jdx/communique, 119 stars), Grep For Mock Module Across The Entire Test File And Confirm Eve (ZaxbyHub/opencode-swarm, 493 stars), Deploy Release Test (vercel/next.js, 143k stars) and Local AI Agents (microsoft/ai-agents-for-beginners, 77k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Session To Skill?

entireio (a GitHub organization) maintains it in entireio/skills, which has 223 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on September 29, 2026.

Source: entireio/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.