Skills openclaw-skill-lazy-loader

Dramatically reduce per-session token usage by loading skills and context files only when needed — not at session start. Includes the SKILLS catalog pattern, AGENTS.md lazy loading strategy, and a Python helper that recommends exactly which files to load for any given task. Compatible with all OpenClaw agents. Works alongside Token Optimizer.

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/asif2bd/openclaw-skill-lazy-loader" ~/.claude/skills/clawdbot-skills-openclaw-skill-lazy-loader && rm -rf "$T"

OpenClaw · Install into ~/.openclaw/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.openclaw/skills && cp -r "$T/skills/asif2bd/openclaw-skill-lazy-loader" ~/.openclaw/skills/clawdbot-skills-openclaw-skill-lazy-loader && rm -rf "$T"

manifest: skills/asif2bd/openclaw-skill-lazy-loader/SKILL.md

source content

OpenClaw Skill Lazy Loader

Stop loading every skill file at session start. Load what you need, when you need it — and cut your token usage by 40–70%.

The Problem

Most OpenClaw agents load their entire skill library at startup:

# AGENTS.md (naive approach)
Read ALL of these before starting:
- skills/python/SKILL.md
- skills/git/SKILL.md
- skills/docker/SKILL.md
- skills/aws/SKILL.md
- skills/browser/SKILL.md
... (20 more)

Each session burns 3,000–15,000 tokens just loading context that may never be used. At scale, this is your biggest cost.

The Solution: Lazy Loading

Instead of loading skills upfront, agents check a SKILLS catalog (a lightweight index) and load individual skill files only when a task requires them.

Before: Load 20 skill files = ~12,000 tokens/session After: Load catalog (300 tokens) + 1–2 relevant skills (~800 tokens) = ~1,100 tokens/session

That's an 89% reduction on context loading alone.

Implementation Guide

Step 1: Create Your SKILLS Catalog

Create

SKILLS.md

in your agent workspace — a lightweight index of all available skills:

# Available Skills

| Skill | File | Use When |
|-------|------|----------|
| Python | skills/python/SKILL.md | Writing/debugging Python code |
| Git | skills/git/SKILL.md | Git operations, PRs, branches |
| Docker | skills/docker/SKILL.md | Containers, images, compose |
| Browser | skills/browser/SKILL.md | Web scraping, UI automation |
| AWS | skills/aws/SKILL.md | Cloud deployments, S3, Lambda |

This catalog is the ONLY file loaded at session start. ~200–400 tokens instead of 10,000+.

See

SKILLS.md.template

for a complete starter template.

Step 2: Update Your AGENTS.md

Replace bulk loading with the catalog pattern:

## Skills

At session start: Read SKILLS.md (the index only).
When a task needs a skill: Read the specific SKILL.md for that skill.
Never load all skills upfront.

### Loading Decision
Before loading any skill file:
1. Does the current task need it? (yes → load it, no → skip)
2. Has it already been loaded this session? (yes → skip, no → load once)

See

AGENTS.md.template

for the full recommended AGENTS.md skills section.

Step 3: Use the Context Optimizer (Optional)

The included

context_optimizer.py

analyzes your task description and recommends which skills to load:

python3 context_optimizer.py recommend "Write a Python script to push to S3"
# Output:
# Recommended skills to load:
#   - skills/python/SKILL.md  (confidence: high — Python task)
#   - skills/aws/SKILL.md     (confidence: high — S3 mentioned)
#   - skills/git/SKILL.md     (confidence: low  — skip unless pushing to GitHub)

Step 4: Apply to Memory Files Too

The same pattern works for memory and context files:

## Memory Loading (AGENTS.md)

At session start: Read MEMORY.md (summary only).
Load daily files (memory/YYYY-MM-DD.md) only when:
- User asks about past work
- Task references a specific date or project
- Debugging requires historical context

Token Savings Calculator

Scenario	Before	After	Savings
5 skills loaded	~3,000 tokens	~600 tokens	80%
10 skills loaded	~6,500 tokens	~750 tokens	88%
20 skills loaded	~13,000 tokens	~900 tokens	93%
+Memory files (5)	+4,000 tokens	+400 tokens	90%

Estimates based on average SKILL.md size of ~600 tokens. Catalog averages ~150 tokens.

Integration with Token Optimizer

This skill pairs directly with OpenClaw Token Optimizer. Lazy loading handles context loading costs; Token Optimizer handles model routing, heartbeat budgeting, and runtime costs. Together they cover the full token lifecycle.

Install both:

clawhub install openclaw-skill-lazy-loader
clawhub install openclaw-token-optimizer

Files in This Skill

File	Purpose
`SKILL.md`	This guide
`SKILLS.md.template`	Starter SKILLS catalog template
`AGENTS.md.template`	Lazy loading AGENTS.md section
`context_optimizer.py`	CLI helper — recommends skills to load per task
`README.md`	ClawHub listing description
`SECURITY.md`	Security audit and script disclosure
`.clawhubsafe`	File integrity manifest

Quick Start (5 minutes)

# 1. Install
clawhub install openclaw-skill-lazy-loader

# 2. Copy templates to your agent workspace
cp ~/.openclaw/skills/openclaw-skill-lazy-loader/SKILLS.md.template ~/my-agent/SKILLS.md
cp ~/.openclaw/skills/openclaw-skill-lazy-loader/AGENTS.md.template ~/my-agent/AGENTS.lazy.md

# 3. Edit SKILLS.md — fill in your actual skills
# 4. Merge AGENTS.lazy.md into your AGENTS.md
# 5. Test with context_optimizer.py
python3 ~/.openclaw/skills/openclaw-skill-lazy-loader/context_optimizer.py recommend "your next task"

By M Asif Rahman (@Asif2BD) · Apache 2.0 · https://clawhub.ai/Asif2BD/openclaw-skill-lazy-loader