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Agent skill

Compress Onboarding

by hashgraph-online in hashgraph-online/awesome-codex-plugins

A skill your agent uses to compress any agent-instruction file (AGENTS.md, CLAUDE.md, .claude/rules/.md, legacy AIBOARDING.md) into terse, high-signal prose without altering commands, code, URLs, or…

Apache-2.0Auto-check passedAgent Workflows

Install Compress Onboarding

skills CLI
$ npx skills add hashgraph-online/awesome-codex-plugins --skill compress-onboarding -a claude-code

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

GitHub CLI
$ gh skill install hashgraph-online/awesome-codex-plugins compress-onboarding --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/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/gustavo-meilus/aiboarding/skills/compress-onboarding .claude/skills/compress-onboarding && 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
compress-onboarding
GitHub stars
1.3k
Token cost
~1.6k tokens
SKILL.md length
740 words
Files
1
Skills in repo
714
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses to compress any agent-instruction file (AGENTS.md, CLAUDE.md, .claude/rules/.md, legacy AIBOARDING.md) into terse, high-signal prose without altering commands, code, URLs, or…

  • Works in 6 steps: Snapshot. Copy the target file to a temp… → Classify and compress. Identify… → Verify. Run… → …
  • Compress any agent-instruction file (AGENTS.md
  • SKILL.md covers Levels (sticky per repo), Byte-preservation invariants…, High-consequence preservation and Procedure, plus 1 more section
  • Calls npm

What it does

Compress Onboarding is an agent skill from hashgraph-online/awesome-codex-plugins. Use to compress any agent-instruction file (AGENTS.md, CLAUDE.md, .claude/rules/.md, legacy AIBOARDING.md) into terse, high-signal prose without altering commands, code, URLs, or paths. Standalone compression engine with levels (off/lite/full/ultra), byte-preservation verification, and token receipts. Also invoked by the create/update onboarding skills.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Agent instruction files. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.

When your agent uses it

  • Compress any agent-instruction file (AGENTS.md
  • .claude/rules/.md
  • Legacy AIBOARDING.md) into terse
  • High-signal prose without altering commands

Example prompts

  • “/compress-onboarding”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Snapshot. Copy the target file to a temp path (before).
  2. Classify and compress. Identify high-consequence regions, report their
  3. Verify. Run .aiboarding/tools/check-preservation (fall
  4. Approval gate. Show the user a diff of the compressed file against the
  5. Receipt. Measure before/after: exact bytes and lines always; token counts
  6. Size check. Run .aiboarding/tools/check-size-budget as a local

What it can do on your machine

Read from SKILL.md and the folder at commit 9e7b281. 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

    Shell commands in SKILL.md call:

    • npm

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

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    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

Compress Onboarding loads about 1.6k tokens when it runs. Until then it costs about 94 tokens; SKILL.md has 740 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~94
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

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 hashgraph-online/awesome-codex-plugins at commit 9e7b281, republished under its Apache-2.0 licence (© hashgraph-online). 740 words, ~1,634 tokens.

Download SKILL.mdSave it as .claude/skills/compress-onboarding/SKILL.md (or your agent's skills folder).
name
compress-onboarding
description
Use to compress any agent-instruction file (AGENTS.md, CLAUDE.md, .claude/rules/*.md, legacy AIBOARDING.md) into terse, high-signal prose without altering commands, code, URLs, or paths. Standalone compression engine with levels (off/lite/full/ultra), byte-preservation verification, and token receipts. Also invoked by the create/update onboarding skills.

Compressing onboarding files

Instruction files load every session, so every saved token compounds. Compress prose aggressively while never touching the technical payload - and never trading away clarity where ambiguity is dangerous.

Announce at start: "Using compress-onboarding on <file> at level <level>."

Usage: compress-onboarding <file> [--level off|lite|full|ultra] Default file: the repo's AGENTS.md. Works under any SKILL.md-compatible runtime.

Levels (sticky per repo)

Resolve the level from --level if given, else .aiboarding/config.json: compression_level, else full. When --level is given, persist it back to config.json - the level is a per-repo decision, not per-run.

  • off - no rewriting. Still run the size report (step 5) so bloat is visible.
  • lite - remove filler, pleasantries, hedging, and restatement. Full sentences kept. ("In order to build the project, you should run…" → "To build, run…")
  • full (default) - additionally drop articles, compress to fragments and short synonyms, allow X → Y notation. ("The dev server can be started with npm run dev" → "Dev server: npm run dev.")
  • ultra - telegraphic; every non-load-bearing word goes. Use only when the effective instruction-chain audit needs it; confirm with the user before first use.

Byte-preservation invariants (hard guarantees)

Compression must NEVER alter: fenced code blocks (including the fence lines), inline backtick spans, shell commands, URLs, file paths, identifiers and symbol names, quoted error strings, <!-- aiboarding-* --> markers, YAML frontmatter, and table structure. If a protected span is wrong, fixing it is an update, not a compression - route it through update-agent-onboarding.

While rewriting, keep commands/identifiers/paths/error strings backtick-quoted (add backticks where the source lacks them - adding protection is allowed; removing it is not). The checker treats backtick spans as protected.

High-consequence preservation

Before rewriting, classify complete high-consequence regions. Preserve every identified region verbatim, byte-for-byte, by default. full or ultra never authorize rewriting one.

Always classify complete Agent Guardrails and Escalation - Ask the User When sections. Outside those headings, classify smallest complete paragraph, list item, warning, or ordered procedure needed to retain context when it governs security, authorization, approval or escalation, destructive or irreversible actions, or required ordering or prerequisites for destructive or migration work. Record each region's source section or line location and category. Do not classify ordinary project purpose, architecture, domain, or routine descriptive prose solely because it is important or technical; compress that prose at selected level.

If user asks to rewrite identified high-consequence content, name affected regions and obtain explicit opt-in for selected regions in current operation. Level choice and final diff approval do not count. Do not persist consent. Rewrite only selected regions, retain unselected regions verbatim, preserve all protected spans, and keep behavioral force unambiguous. Then show final diff and obtain normal approval.

Show full SKILL.md (342 more words)Show less

Procedure

  1. Snapshot. Copy the target file to a temp path (before).
  2. Classify and compress. Identify high-consequence regions, report their locations and categories, and copy them verbatim unless current-operation, per-region explicit opt-in permits rewriting. Compress remaining prose at resolved level.
  3. Verify. Run .aiboarding/tools/check-preservation <before> <after> (fall back to the plugin's templates/tools/check-preservation if not installed). Fix every reported span and re-run until clean. Never hand-wave this step.
  4. Approval gate. Show the user a diff of the compressed file against the original. Write only after approval.
  5. Receipt. Measure before/after: exact bytes and lines always; token counts with a real tokenizer if one is available in the environment (e.g. Python tiktoken), otherwise tokens_approx = bytes / 4, explicitly labeled approximate. Append to .aiboarding/state.json:receipts (one object per line, keeping the file hook-readable):
    json
    { "file": "AGENTS.md", "level": "full", "bytes_before": 8123, "bytes_after": 4310, "lines_before": 190, "lines_after": 121, "tokens_before_approx": 2031, "tokens_after_approx": 1078, "high_consequence_regions": [{ "location": "Agent Guardrails", "category": "guardrails", "outcome": "preserved", "explicit_opt_in": false }], "measured_at": "2026-07-02" }
    high_consequence_regions is optional for backward compatibility. Each entry records location, category, outcome (preserved or rewritten), and explicit opt-in status; never copy instruction text. Include [] when classification verified no such regions. Report same evidence to user. Report the saving to the user; since the file loads every session, note the per-session saving - never claim unlabeled exact token numbers without a real tokenizer. Also write a compact compression-verification record with the subject, level, measurements, preservation and size outcomes through write-evidence. Keep the legacy receipt unchanged; a failed preservation or size check is recorded when possible and never authorizes a sync-pointer advance.
  6. Size check. Run .aiboarding/tools/check-size-budget <file> as a local sensor. It does not prove chain safety; run .aiboarding/tools/audit-onboarding-evidence <repo-root> before claiming an effective Codex chain fits. If local guidance still WARNs after full, suggest moving detail to .claude/rules/ or nested AGENTS.md files rather than jumping to ultra.

Writing into shared files

When compression output must land inside a file that also has user-owned content (e.g. a hand-written CLAUDE.md), write only within the aiboarding marker fence via .aiboarding/tools/inject-fenced - re-runs stay idempotent and uninstall stays clean.

© hashgraph-online, Apache-2.0. 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 plugins/gustavo-meilus/aiboarding/skills/compress-onboarding of hashgraph-online/awesome-codex-plugins.

Open the folder on GitHubat commit 9e7b281

Compare with similar skills

Compress Onboarding 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.

Compress Onboarding compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Compress Onboarding this skillhashgraph-online/awesome-codex-plugins1.3k—~1.6kAutomated safety check: PassApache-2.0
Using Agent Skillsaddyosmani/agent-skills103k4 repos~2.4kAutomated safety check: PassMIT
Claude ReflectBayramAnnakov/claude-reflect1.7k2 repos~627Automated safety check: PassMIT
Neat-Freak Knowledge CloseoutKKKKhazix/khazix-skills21k—~1.9kAutomated safety check: PassMIT
Writing For Agentsbestofjs/bestofjs3.1k18 repos~2.7kAutomated safety check: PassMIT
Task Observerrebelytics/one-skill-to-rule-them-all3.2k1 repos~12kAutomated safety check: PassCC-BY-4.0

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Categories

Questions about Compress Onboarding

What does Compress Onboarding do?

A skill your agent uses to compress any agent-instruction file (AGENTS.md, CLAUDE.md, .claude/rules/.md, legacy AIBOARDING.md) into terse, high-signal prose without altering commands, code, URLs, or…. Compress Onboarding is an agent skill from hashgraph-online/awesome-codex-plugins.md) into terse, high-signal prose without altering commands, code, URLs, or paths.

When should I use Compress Onboarding?

Compress Onboarding fits situations like: compress any agent-instruction file (AGENTS.md; .claude/rules/.md; legacy AIBOARDING.md) into terse; high-signal prose without altering commands.

How do I install Compress Onboarding in Claude Code?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill compress-onboarding -a claude-code`. Or copy the skill folder (plugins/gustavo-meilus/aiboarding/skills/compress-onboarding in hashgraph-online/awesome-codex-plugins) into .claude/skills/compress-onboarding in your project. Claude Code loads it when a task matches its description.

How do I install Compress Onboarding in Codex?

Run `npx skills add hashgraph-online/awesome-codex-plugins --skill compress-onboarding -a codex`. Or copy the skill folder (plugins/gustavo-meilus/aiboarding/skills/compress-onboarding in hashgraph-online/awesome-codex-plugins) into .agents/skills/compress-onboarding in your project. Codex loads it when a task matches its description.

Can I use Compress Onboarding 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 hashgraph-online/awesome-codex-plugins --skill compress-onboarding -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/compress-onboarding, .gemini/skills/compress-onboarding, .github/skills/compress-onboarding and .opencode/skills/compress-onboarding in your project.

What does Compress Onboarding need to run?

Going by SKILL.md and its folder, Compress Onboarding needs the command-line tools its instructions call (npm). Our summary lists: Python 3.

Does Compress Onboarding access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Compress Onboarding 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 Compress Onboarding use?

Compress Onboarding is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Compress Onboarding use?

About 1.6k tokens (SKILL.md is roughly 6.5k 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 Compress Onboarding?

Skills that share tags, products or a category with Compress Onboarding: Using Agent Skills (addyosmani/agent-skills, 103k stars), Claude Reflect (BayramAnnakov/claude-reflect, 1.7k stars), Neat-Freak Knowledge Closeout (KKKKhazix/khazix-skills, 21k stars) and Writing For Agents (bestofjs/bestofjs, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Compress Onboarding?

hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,255 GitHub stars. The repository holds 714 skills in this directory. The repository was last updated on October 9, 2026.

Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.