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AD 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.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 1 307 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Dramatically reduce per-session token usage by loading skills and context files only when needed — not at session start.

As a process D 36/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

ProcedureAWSDockerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
79
Run on models
none yet
Process rating
D
36/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token README.md:5
      High-entropy token-like string (may be an id, hash or a credential)
      [![ClawHub](https://img.shields.io/badge/Claw…2BD%2Fop…een)](https://clawhub.ai/Asif2BD/openclaw-skill-lazy-loader)

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "security"

    Process rating: all ten parameters 36/100

    • 0Steps. Prose only: no discrete steps
    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 2 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1307 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3No numbered steps or checklist
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +3Description length 344: enough signal without eating the budget
    • +4Structure: 12 headings
    • +4Has examples (7 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.