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.

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

How to use

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

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.

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

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

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.