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AC token-monitor

Monitor OpenClaw token/quota usage and alert when any quota drops below a threshold (default 20%). Uses `openclaw models status` and writes only a local state file to avoid duplicate alerts. **Does not handle secrets.** **Token cost:** Script itself: 0 tokens (pure bash). Heartbeat integration: ~1k-2k tokens/hour (reading HEARTBEAT.md + executing script). Alert delivery: ~500-1k tokens/alert. **Optimization:** Use system cron instead of heartbeat to reduce to ~0 tokens (except alerts).

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files · 1 script body ≈ 1 208 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Monitor OpenClaw token/quota usage and alert when any quota drops below a threshold (default 20%).

As a process C 60/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

IntegrationGitHubSales and CRMInfrastructuretype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
60/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 0

    ✓ No critical or high findings

    Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 60/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 6 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 25 steps, 2 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1208 tokens
    • low 10 top-level sections: this looks like several domains in one skill

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 490: enough signal without eating the budget
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 25 items
    • +3Output format is stated explicitly
    • +4Has examples (14 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.