Skill Loadout Manager

Skill Loadout Manager

What it does

Installing more skills increases OpenClaw's system prompt size. Every installed skill contributes its description to the context window on every session start — even skills you haven't used in months.

Skill Loadout Manager lets you define named loadouts: curated subsets of skills for specific contexts. You switch to a loadout and only those skills are active. Everything else is installed but dormant.

Examples:

• coding — tools for writing, testing, reviewing code
• research — browsing, fact-checking, note synthesis
• ops — monitoring, cron hygiene, spend tracking
• minimal — just the essentials: memory, handoff, recovery

When to invoke

• When you notice system prompt bloat slowing context initialisation
• When switching between focused work modes (deep coding vs. research)
• When you want to test a single skill in isolation
• After adding many new skills that aren't always relevant

Loadout structure

A loadout is a named list of skill names stored in state. Activating a loadout signals to OpenClaw's skill loader which skills to surface in the system prompt. Skills not in the active loadout remain installed but excluded from description injection.

yamlCopy code
# Example loadout definition
name: coding
skills:
  - systematic-debugging
  - test-driven-development
  - verification-before-completion
  - skill-doctor
  - dangerous-action-guard

How to use

bashCopy code
python3 loadout.py --list                      # Show all loadouts and active one
python3 loadout.py --create coding             # Create new loadout (interactive)
python3 loadout.py --add coding skill-doctor   # Add skill to loadout
python3 loadout.py --remove coding skill-doctor  # Remove skill
python3 loadout.py --activate coding           # Switch to loadout
python3 loadout.py --activate --all            # Activate all skills
python3 loadout.py --show coding               # List skills in a loadout
python3 loadout.py --status                    # Current active loadout
python3 loadout.py --estimate coding           # Estimate token savings

Procedure

Step 1 — Assess current footprint

bashCopy code
python3 loadout.py --estimate --all

This shows the estimated description token count for all installed skills and highlights candidates for loadout pruning.

Step 2 — Define your loadouts

Think in contexts: What skills do you actually need when writing code? When doing research? During maintenance windows? Create one loadout per context, aiming for 5–10 skills each.

bashCopy code
python3 loadout.py --create coding
python3 loadout.py --add coding systematic-debugging test-driven-development
python3 loadout.py --add coding verification-before-completion dangerous-action-guard

Step 3 — Activate a loadout

bashCopy code
python3 loadout.py --activate coding

OpenClaw reads the active loadout from state on next session start and only injects those skill descriptions.

Step 4 — Switch as needed

Switching is instant and takes effect on the next session. No restart required.

Step 5 — Return to full mode

bashCopy code
python3 loadout.py --activate --all

State

Active loadout name and all loadout definitions stored in ~/.openclaw/skill-state/skill-loadout-manager/state.yaml.

Fields: active_loadout, loadouts map, switch_history.

Notes

• Always-on skills (e.g., dangerous-action-guard, prompt-injection-guard) can be marked pinned: true so they're included in every loadout automatically.
• The minimal loadout is pre-seeded at install time with only safety and recovery skills.