· · · · · ·

🦴 paleo

Token-saving skills for LLM agents — cut output & context tokens without choking the model.

Why · See it · Features · Skills · Combos · Quick Start · Benchmarks · Comparison · Tips · Install

Why paleo?

Tokens cost money and latency. Every trimmed token means faster, cheaper inference.
One-size-fits-all prompting fails. Sometimes you want terse output, sometimes a hard budget, sometimes just no fluff. paleo gives each as a separate, well-scoped skill.
Skills stay minimal. Every SKILL.md is written terse on purpose — loading one costs less context than a long prompt.

See it (Before / After)

paleo compresses delivery, not meaning. Code, commands, and technical terms stay byte-exact.

Normal agent	🦴 paleo
"The re-render happens because you create a new object literal on every render. That inline object is a fresh reference each time, so React sees a changed prop and re-renders. Wrap it in `useMemo` to keep a stable reference."	"New object each render → new ref → re-render. Wrap in `useMemo`. Stable ref = no re-render."
"To authenticate requests, add middleware that checks the token on each request and returns 401 if it is missing or expired."	"Add auth middleware. Check token per request. 401 if missing/expired."

[!NOTE] paleo keeps technical accuracy at 100% — it drops filler, not facts.

Features

Modular & composable — load one skill or all seven, mix per task.
Output + context savings — ~50–70% fewer output tokens (median ~54% on a 6-task sample — see BENCHMARK.md), plus proactive context trimming.
Auto-detect — paleo-auto watches session state and enables the right skills automatically. No thinking required.
Production-safe — compresses output and context only; never rewrites your code.
Hard token budget — paleo-budget caps spend and summarizes the tail.
Cross-agent — open Agent Skills standard: Claude Code, Codex, Gemini CLI, Qwen Code, OpenCode, Cursor, GitHub Copilot, Cline, Windsurf + 40+ agents via npx skills add.
Zero-setup triggers — plain English phrases, no slash commands to register.
Low overhead — each SKILL.md is intentionally terse, so loading stays cheap.
Open & extensible — drop in your own token-saving skills.

Skills

Skill	What it does	Trigger example
`paleo`	Terse output mode — cut output tokens ~50–70%, keep code/terms exact.	`paleo mode` · `be brief` · `save tokens`
`paleo-auto`	🆕 Zero-touch auto-detection — watches session & enables the right skills automatically.	`paleo-auto` · `auto paleo` · `enable auto-save`
`paleo-budget`	Hard token budget per task — cap spend, summarize if exceeded.	`budget 2000` · `stay under 2000 tokens`
`paleo-trim-context`	Proactively trim/summarize context to save tokens without losing task state.	`trim context` (auto on long sessions)
`paleo-converse`	Condense old chat turns + merge duplicate messages; keep last N verbatim.	`condense chat` · `compress conversation` · `paleo-converse N=8`
`paleo-summary`	Tight intisari of bulky tool output / logs / diffs / dumps.	`tldr` · `condense this` · `summarize output`
`paleo-json`	Minify & compact structured/JSON output, stay parseable.	`compact json` · `minify`

Recommended combos

paleo is the base — keep it on. Layer the rest by what you're doing:

Situation	Combo	Why
Daily driver (long / chatty sessions)	`paleo` + `paleo-trim-context`	Base + automatic context hygiene.
History piling up	+ `paleo-converse`	Condense + merge duplicate turns once a session gets long.
Debugging / bulky tool output	`paleo` + `paleo-summary` + `paleo-json`	Logs → intisari; JSON → minified.
Tight cost / hard limit	`paleo` + `paleo-budget` (+ `paleo-trim-context`)	Hard ceiling + shrink context first.
Max savings (all on)	all seven skills	Overkill daily, but safe for extreme thrift.

[!TIP] You rarely need every skill at once. paleo + paleo-auto is the default for most users — auto-detect handles the rest. For manual control: paleo + paleo-trim-context is the baseline; add paleo-converse for messy chats, paleo-summary / paleo-json for heavy tool output, and paleo-budget only when a hard cap is required.

Quick Start

hljs language-bash

# 1. Clone the collection
git clone https://github.com/mocasus/paleo.git

# 2. Claude Code — one plugin bundles all 7 skills
claude plugin marketplace add https://github.com/mocasus/paleo
claude plugin install paleo@paleo

# 3. Any agent via the open Agent Skills registry (installs to 40+ clients)
npx skills add mocasus/paleo

Then just talk to your agent — no command to register:

paleo mode · save tokens · budget 2000 · trim context

Benchmarks

Real, reproducible numbers — not hand-waved claims.

Model	Tasks	Median output savings	Mean
`claude-sonnet-4.5`	6	53.8%	45.1%

Full method, per-task table, and the runnable harness are in BENCHMARK.md. Rerun on your own stack:

hljs language-bash

export IDROUTER_API_KEY=your_key
python3 bench/benchmark.py --model claude-sonnet-4.5

[!TIP] Savings are task-dependent: biggest on verbose generative work (code, walkthroughs, comparisons — 54–79%), smaller on already-compact factual answers. paleo also cuts context tokens via paleo-trim-context, a layer a terse-persona prompt cannot reach.

Comparison

paleo is often compared with two other token-saving approachesa terse-persona system prompt and Ponytail (a code-reuse coding skill). Here is how they differ.

Dimension	🦴 paleo	Terse-persona prompt	Ponytail
Form	7 composable skills	Single system prompt (persona)	Single coding skill / workflow
What it targets	Output tokens + context + conversation turns	Output tokens only	Volume of code the agent writes (+ MCP caching)
Granularity	Per-task, mix & match	One mode	One workflow
Touches your code	❌ No (output/context only)	❌ No	⚠️ Yes — refactors / reuses code
Context & reasoning savings	✅ `paleo-trim-context`	❌ None	◑ Partial (caching)
Hard budget	✅ `paleo-budget`	❌	❌
Cross-agent	✅ 40+ agents (open standard)	➖ Portable prompt, but monolithic	➖ Claude Code skill
Activation	Plain phrases	Edit system prompt	Install + invoke skill
Reasoning-model safe	✅ Never compresses thought	❌ Can raise tokens (e.g. +3% on Opus)	➖
Known risk	None (output-only)	Can fight "expand" heuristics; may raise tokens on reasoning models	Refactor can change behavior
Open benchmark	✅ Reproducible harness	❌ Claim only	❌ Claim only

[!TIP] They're complementary, not rivals. Ponytail cuts the code you have to write; paleo cuts the tokens in the conversation. Terse-persona prompts proved a terse prompt helps output — paleo takes that same idea and makes it modular, adds context-trimming and a hard budget, and drops the persona gimmick. Use Ponytail for code-heavy work and paleo for chatty, long sessions.

FAQ

Prompt dipangkas, context dipotong, kualitas gak bakal sama dong?

Gak lebih jelek — malah sering lebih bagus. paleo bukan potong buta, dia buang redundansi (ulang-ulang, filler, boilerplate hasil tool, whitespace), bukan info esensial. Constraint, error, code, keputusan tetap utuh. Prompt lo gak diapa-apain — yang di-trim itu context kerja agent (hasil tool berulang, riwayat convo). Context bersih = model fokus ke signal, bukan lost track karena noise. Benchmark: median 53.8% token turun, kualitas task gak drop. Trade-off jujur: budget ekstrem (token cap rendah banget) bisa turun kualitas, tapi setting optional. Intinya: lebih murah & cepet, kualitas tetap.

System prompt itu buat bikin model bagus, klo dipangkas banyak yg ilang dong?

paleo gak pernah sentuh system prompt. System prompt = aturan main, utuh 100%. Instruksi yang define behavior gak diapa-apain — paleo jalan setelah system prompt ke-load, cuma kerja di context dinamis. Yang di-trim itu context kerja (tool output berulang, convo kepanjangan), bukan instruksi. System prompt kecil dibanding noise yang numpuk dari tool output. Plus bisa whitelist bagian yang mau dijaga. Efeknya kebalik: context bersih bikin model lebih patuh ke system prompt. Aturan main tetep nempel.

Bisa ngerusak code / formatting gak?

Gak. paleo melindungi code block, structured output (JSON/table), dan error message secara default. Yang di-compress cuma prose bertele-tele & tool output redundant. Kalo masih ragu, bisa whitelist file/section tertentu biar 100% gak ke-trim.

Ini butuh API key atau service eksternal?

Enggak. paleo murni teknik prompt/context — gak ada server, gak ada API call, gak ada dependensi luar. Skill-nya tinggal di-load ke agent lo, jalan di lokal. No overengineering.

Token savings-nya beneran kelihatan di billing?

Kelihatan, terutama di session panjang & agent loop. Tiap token yang gak dikirim = gak dibayar. Benchmark kita median 53.8% turun di context + output. Di agent yang muter 20+ tool call, itu selisih gede per run.

Works di semua model/provider?

Iya. paleo model-agnostic — kerja di level prompt & context, bukan di model tertentu. Claude, GPT, Gemini, GLM, Qwen, lokal — semua bisa. Sifatnya instruksi, bukan fine-tune.

Beda sama auto-compaction bawaan agent (Claude compaction, dll)?

Compaction bawaan itu generic & reaktif (baru jalan pas context mau penuh, sering blind truncation). paleo proaktif + selektif: jaga info esensial, buang redundansi, configurable, ada safety net (whitelist). Plus paleo juga ngurus output verbosity & tool-result summarization, bukan cuma convo history.

Cara enable/disable per task?

Trigger pakai natural language (skip preamble, ringkas output, trim context) — gak perlu slash command. Mau matiin? Tinggal gak dipanggil, atau cabut skill dari agent. No global lock-in.

Tips & Triggers

paleo activates from natural-language triggers — no slash command to register. Type the trigger, the skill loads and applies.

Activation & switches (plain phrases)

paleo — terse output

On: paleo mode · be brief · terse · compress output · save tokens
Level: paleo full (default) · paleo lite · paleo ultra
Off: stop paleo · normal mode

paleo-budget — token cap

On: budget 2000 · stay under 2000 tokens · token limit
Off: no budget · unlimited

paleo-trim-context — auto on long sessions; trim context to force. paleo-auto — paleo-auto · auto paleo — off: disable paleo-auto · manual paleo paleo-converse — condense chat · compress conversation · paleo-converse N=8 paleo-summary — tldr · condense this · summarize output paleo-json — compact json · minify

Combo: paleo + paleo-budget = max savings. Add paleo-trim-context on long sessions, paleo-converse on chatty ones, paleo-summary for bulky tool output.

Installation

🔵 Claude Code

hljs language-bash

claude plugin marketplace add https://github.com/mocasus/paleo
claude plugin install paleo@paleo

🟢 Codex

hljs language-bash

npx skills add mocasus/paleo

🟡 Gemini CLI

hljs language-bash

mkdir -p ~/.gemini/skills && cp -r skills/* ~/.gemini/skills/

🟣 Hermes Agent

hljs language-bash

hermes skills install mocasus/paleo
# Or copy skills manually
cp -r skills/paleo* ~/.hermes/skills/

⚫ Cursor

hljs language-bash

npx skills add mocasus/paleo

🔷 GitHub Copilot

hljs language-bash

npx skills add mocasus/paleo

🌊 Windsurf

hljs language-bash

npx skills add mocasus/paleo

📦 Universal (any agent)

hljs language-bash

git clone https://github.com/mocasus/paleo.git
# copy skills/paleo*/ into your agent's skills directory

All 7 skills load automatically — paleo, paleo-trim-context, paleo-auto, paleo-budget, paleo-converse, paleo-summary, paleo-json.

Full per-agent steps in INSTALL.md. See real compression numbers in BENCHMARK.md.

Hermes Integration

paleo is battle-tested on Hermes Agent by @mocasus — first user & case study.

hljs language-bash

# Install via Hermes skills manager
hermes skills install mocasus/paleo

# Or by path
cp -r skills/paleo* ~/.hermes/skills/

Then in your Hermes chat (Telegram, WhatsApp, etc.):

hljs language-yaml

> paleo
🦴 paleo full — terse output, code-first

> build a REST API with FastAPI
[terse, code-first response — no preamble, no filler]

> paleo-auto
🦴 paleo-auto: watching session... enabled paleo + trim-context (23 turns)

> budget 2000
🦴 paleo-budget: 2000 output tokens, hard mode

Hermes tips:

Start with paleo — instant token savings on every reply.
paleo-auto for sessions >15 turns (watches context fill & enables the right skills).
Combine paleo + budget for expensive models via provider routing.
paleo-converse kicks in when your agent loop hits 60%+ context cap.

Custom Skills

paleo is open — wire your own token-saving skills:

skills/<your-name>/SKILL.md with name + description frontmatter.
Add the skills directory to .claude-plugin/plugin.json — the skills field is a path string (e.g. "./skills/"), not an array. Gemini + other agents pick skills up natively; no extra manifest needed.
Bump version badge (this file + footer) + plugin version.
Commit + push.

No repo edit needed — just drop any SKILL.md into your agent's skills dir (e.g. ~/.hermes/skills/<name>/). paleo loads whatever it finds under skills/.

User Stats

Share your numbers, get listed. PR your monthly token savings to this table.

User / Team	Agent	Tokens/month saved	Skills
Add yours →	—	—	—

How to measure:

Use agent 1 week without paleo → note token usage from provider dashboard
Enable paleo (or paleo-auto) 1 week → note new usage
Diff × 4 = estimated monthly savings

Contributing

Contributions are welcome — new token-saving skills, better triggers, or benchmark data.

Open an issue describing the skill or improvement.
Keep SKILL.md files terse (they load into context).
Add name + description frontmatter and register in both plugin manifests.
Bump the version badge and version fields before opening a PR.

License

MIT — see LICENSE.

🦴 paleo

Token-saving skills for LLM agents — cut output & context tokens without choking the model.

Why · See it · Features · Skills · Combos · Quick Start · Benchmarks · Comparison · Tips · Install

Why paleo?

Tokens cost money and latency. Every trimmed token means faster, cheaper inference.
One-size-fits-all prompting fails. Sometimes you want terse output, sometimes a hard budget, sometimes just no fluff. paleo gives each as a separate, well-scoped skill.
Skills stay minimal. Every SKILL.md is written terse on purpose — loading one costs less context than a long prompt.

See it (Before / After)

paleo compresses delivery, not meaning. Code, commands, and technical terms stay byte-exact.

Normal agent	🦴 paleo
"The re-render happens because you create a new object literal on every render. That inline object is a fresh reference each time, so React sees a changed prop and re-renders. Wrap it in `useMemo` to keep a stable reference."	"New object each render → new ref → re-render. Wrap in `useMemo`. Stable ref = no re-render."
"To authenticate requests, add middleware that checks the token on each request and returns 401 if it is missing or expired."	"Add auth middleware. Check token per request. 401 if missing/expired."

[!NOTE] paleo keeps technical accuracy at 100% — it drops filler, not facts.

Features

Modular & composable — load one skill or all seven, mix per task.
Output + context savings — ~50–70% fewer output tokens (median ~54% on a 6-task sample — see BENCHMARK.md), plus proactive context trimming.
Auto-detect — paleo-auto watches session state and enables the right skills automatically. No thinking required.
Production-safe — compresses output and context only; never rewrites your code.
Hard token budget — paleo-budget caps spend and summarizes the tail.
Cross-agent — open Agent Skills standard: Claude Code, Codex, Gemini CLI, Qwen Code, OpenCode, Cursor, GitHub Copilot, Cline, Windsurf + 40+ agents via npx skills add.
Zero-setup triggers — plain English phrases, no slash commands to register.
Low overhead — each SKILL.md is intentionally terse, so loading stays cheap.
Open & extensible — drop in your own token-saving skills.

Skills

Skill	What it does	Trigger example
`paleo`	Terse output mode — cut output tokens ~50–70%, keep code/terms exact.	`paleo mode` · `be brief` · `save tokens`
`paleo-auto`	🆕 Zero-touch auto-detection — watches session & enables the right skills automatically.	`paleo-auto` · `auto paleo` · `enable auto-save`
`paleo-budget`	Hard token budget per task — cap spend, summarize if exceeded.	`budget 2000` · `stay under 2000 tokens`
`paleo-trim-context`	Proactively trim/summarize context to save tokens without losing task state.	`trim context` (auto on long sessions)
`paleo-converse`	Condense old chat turns + merge duplicate messages; keep last N verbatim.	`condense chat` · `compress conversation` · `paleo-converse N=8`
`paleo-summary`	Tight intisari of bulky tool output / logs / diffs / dumps.	`tldr` · `condense this` · `summarize output`
`paleo-json`	Minify & compact structured/JSON output, stay parseable.	`compact json` · `minify`

Recommended combos

paleo is the base — keep it on. Layer the rest by what you're doing:

Situation	Combo	Why
Daily driver (long / chatty sessions)	`paleo` + `paleo-trim-context`	Base + automatic context hygiene.
History piling up	+ `paleo-converse`	Condense + merge duplicate turns once a session gets long.
Debugging / bulky tool output	`paleo` + `paleo-summary` + `paleo-json`	Logs → intisari; JSON → minified.
Tight cost / hard limit	`paleo` + `paleo-budget` (+ `paleo-trim-context`)	Hard ceiling + shrink context first.
Max savings (all on)	all seven skills	Overkill daily, but safe for extreme thrift.

[!TIP] You rarely need every skill at once. paleo + paleo-auto is the default for most users — auto-detect handles the rest. For manual control: paleo + paleo-trim-context is the baseline; add paleo-converse for messy chats, paleo-summary / paleo-json for heavy tool output, and paleo-budget only when a hard cap is required.

Quick Start

hljs language-bash

# 1. Clone the collection
git clone https://github.com/mocasus/paleo.git

# 2. Claude Code — one plugin bundles all 7 skills
claude plugin marketplace add https://github.com/mocasus/paleo
claude plugin install paleo@paleo

# 3. Any agent via the open Agent Skills registry (installs to 40+ clients)
npx skills add mocasus/paleo

Then just talk to your agent — no command to register:

paleo mode · save tokens · budget 2000 · trim context

Benchmarks

Real, reproducible numbers — not hand-waved claims.

Model	Tasks	Median output savings	Mean
`claude-sonnet-4.5`	6	53.8%	45.1%

Full method, per-task table, and the runnable harness are in BENCHMARK.md. Rerun on your own stack:

hljs language-bash

export IDROUTER_API_KEY=your_key
python3 bench/benchmark.py --model claude-sonnet-4.5

[!TIP] Savings are task-dependent: biggest on verbose generative work (code, walkthroughs, comparisons — 54–79%), smaller on already-compact factual answers. paleo also cuts context tokens via paleo-trim-context, a layer a terse-persona prompt cannot reach.

Comparison

paleo is often compared with two other token-saving approachesa terse-persona system prompt and Ponytail (a code-reuse coding skill). Here is how they differ.

Dimension	🦴 paleo	Terse-persona prompt	Ponytail
Form	7 composable skills	Single system prompt (persona)	Single coding skill / workflow
What it targets	Output tokens + context + conversation turns	Output tokens only	Volume of code the agent writes (+ MCP caching)
Granularity	Per-task, mix & match	One mode	One workflow
Touches your code	❌ No (output/context only)	❌ No	⚠️ Yes — refactors / reuses code
Context & reasoning savings	✅ `paleo-trim-context`	❌ None	◑ Partial (caching)
Hard budget	✅ `paleo-budget`	❌	❌
Cross-agent	✅ 40+ agents (open standard)	➖ Portable prompt, but monolithic	➖ Claude Code skill
Activation	Plain phrases	Edit system prompt	Install + invoke skill
Reasoning-model safe	✅ Never compresses thought	❌ Can raise tokens (e.g. +3% on Opus)	➖
Known risk	None (output-only)	Can fight "expand" heuristics; may raise tokens on reasoning models	Refactor can change behavior
Open benchmark	✅ Reproducible harness	❌ Claim only	❌ Claim only

[!TIP] They're complementary, not rivals. Ponytail cuts the code you have to write; paleo cuts the tokens in the conversation. Terse-persona prompts proved a terse prompt helps output — paleo takes that same idea and makes it modular, adds context-trimming and a hard budget, and drops the persona gimmick. Use Ponytail for code-heavy work and paleo for chatty, long sessions.

FAQ

Prompt dipangkas, context dipotong, kualitas gak bakal sama dong?

System prompt itu buat bikin model bagus, klo dipangkas banyak yg ilang dong?

Bisa ngerusak code / formatting gak?

Ini butuh API key atau service eksternal?

Enggak. paleo murni teknik prompt/context — gak ada server, gak ada API call, gak ada dependensi luar. Skill-nya tinggal di-load ke agent lo, jalan di lokal. No overengineering.

Token savings-nya beneran kelihatan di billing?

Works di semua model/provider?

Iya. paleo model-agnostic — kerja di level prompt & context, bukan di model tertentu. Claude, GPT, Gemini, GLM, Qwen, lokal — semua bisa. Sifatnya instruksi, bukan fine-tune.

Beda sama auto-compaction bawaan agent (Claude compaction, dll)?

Cara enable/disable per task?

Trigger pakai natural language (skip preamble, ringkas output, trim context) — gak perlu slash command. Mau matiin? Tinggal gak dipanggil, atau cabut skill dari agent. No global lock-in.

Tips & Triggers

paleo activates from natural-language triggers — no slash command to register. Type the trigger, the skill loads and applies.

Activation & switches (plain phrases)

paleo — terse output

On: paleo mode · be brief · terse · compress output · save tokens
Level: paleo full (default) · paleo lite · paleo ultra
Off: stop paleo · normal mode

paleo-budget — token cap

On: budget 2000 · stay under 2000 tokens · token limit
Off: no budget · unlimited

Combo: paleo + paleo-budget = max savings. Add paleo-trim-context on long sessions, paleo-converse on chatty ones, paleo-summary for bulky tool output.

Installation

🔵 Claude Code

hljs language-bash

claude plugin marketplace add https://github.com/mocasus/paleo
claude plugin install paleo@paleo

🟢 Codex

hljs language-bash

npx skills add mocasus/paleo

🟡 Gemini CLI

hljs language-bash

mkdir -p ~/.gemini/skills && cp -r skills/* ~/.gemini/skills/

🟣 Hermes Agent

hljs language-bash

hermes skills install mocasus/paleo
# Or copy skills manually
cp -r skills/paleo* ~/.hermes/skills/

⚫ Cursor

hljs language-bash

npx skills add mocasus/paleo

🔷 GitHub Copilot

hljs language-bash

npx skills add mocasus/paleo

🌊 Windsurf

hljs language-bash

npx skills add mocasus/paleo

📦 Universal (any agent)

hljs language-bash

git clone https://github.com/mocasus/paleo.git
# copy skills/paleo*/ into your agent's skills directory

All 7 skills load automatically — paleo, paleo-trim-context, paleo-auto, paleo-budget, paleo-converse, paleo-summary, paleo-json.

Full per-agent steps in INSTALL.md. See real compression numbers in BENCHMARK.md.

Hermes Integration

paleo is battle-tested on Hermes Agent by @mocasus — first user & case study.

hljs language-bash

# Install via Hermes skills manager
hermes skills install mocasus/paleo

# Or by path
cp -r skills/paleo* ~/.hermes/skills/

Then in your Hermes chat (Telegram, WhatsApp, etc.):

hljs language-yaml

> paleo
🦴 paleo full — terse output, code-first

> build a REST API with FastAPI
[terse, code-first response — no preamble, no filler]

> paleo-auto
🦴 paleo-auto: watching session... enabled paleo + trim-context (23 turns)

> budget 2000
🦴 paleo-budget: 2000 output tokens, hard mode

Hermes tips:

Start with paleo — instant token savings on every reply.
paleo-auto for sessions >15 turns (watches context fill & enables the right skills).
Combine paleo + budget for expensive models via provider routing.
paleo-converse kicks in when your agent loop hits 60%+ context cap.

Custom Skills

paleo is open — wire your own token-saving skills:

skills/<your-name>/SKILL.md with name + description frontmatter.
Add the skills directory to .claude-plugin/plugin.json — the skills field is a path string (e.g. "./skills/"), not an array. Gemini + other agents pick skills up natively; no extra manifest needed.
Bump version badge (this file + footer) + plugin version.
Commit + push.

No repo edit needed — just drop any SKILL.md into your agent's skills dir (e.g. ~/.hermes/skills/<name>/). paleo loads whatever it finds under skills/.

User Stats

Share your numbers, get listed. PR your monthly token savings to this table.

User / Team	Agent	Tokens/month saved	Skills
Add yours →	—	—	—

How to measure:

Use agent 1 week without paleo → note token usage from provider dashboard
Enable paleo (or paleo-auto) 1 week → note new usage
Diff × 4 = estimated monthly savings

Contributing

Contributions are welcome — new token-saving skills, better triggers, or benchmark data.

Open an issue describing the skill or improvement.
Keep SKILL.md files terse (they load into context).
Add name + description frontmatter and register in both plugin manifests.
Bump the version badge and version fields before opening a PR.

License

MIT — see LICENSE.

paleo

🦴 paleo

Why paleo?

See it (Before / After)

Features

Skills

Recommended combos

Quick Start

Benchmarks

Comparison

FAQ

Tips & Triggers

Installation

Hermes Integration

Custom Skills

User Stats

Contributing

License

Sponsors

Similar Packages

paleo

🦴 paleo

Why paleo?

See it (Before / After)

Features

Skills

Recommended combos

Quick Start

Benchmarks

Comparison

FAQ

Tips & Triggers

Installation

Hermes Integration

Custom Skills

User Stats

Contributing

License

Sponsors

Similar Packages