SKILLEMALL.ai
GitHub ↗ RU Sign in

AB kimi-usage-monitor

Monitor Kimi K2.5 API usage and quota from the Kimi console. Use when the agent needs to (1) Check remaining usage percentage and reset timers, (2) Make autonomous decisions about task prioritization based on available quota, (3) Monitor rate limit status before starting intensive operations, (4) Log usage patterns over time for resource planning. Essential for self-managing agents operating under quota constraints.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files · 1 script body ≈ 1 247 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Monitor Kimi K2.5 API usage and quota from the Kimi console. Use when the agent needs to (1) Check remaining usage percentage and reset timers, (2) Make…

As a process B 74/100 · Nearly there — weak spots: when it triggers

IntegrationSales and CRMAI 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
B
74/100
Nearly there
When it triggers w 12
20
Failures and branches w 10
50
Result and completion w 14
60
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: 8. 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 74/100

    • 20When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 25 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1247 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress
    • low 11 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 419: enough signal without eating the budget
    • +4Structure: 14 headings
    • +3Step-by-step instructions: 25 items
    • +3Output format is stated explicitly
    • +4Has examples (9 code blocks)
    • +3All 4 scripts are documented

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