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AC token-optimizer

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and multi-provider fallbacks. Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. Includes ready-to-use scripts for task classification, usage monitoring, and optimized heartbeat scheduling. All operations are local file analysis only - no network requests, no code execution. See SECURITY.md for details.

modbender/skill-library-mcp Agent Skills author: modbender MIT 14 files body ≈ 4 186 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Reduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and multi-provider fallbacks.

As a process C 50/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

IntegrationAI and agentsInfrastructuretype 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
97/100
safety, quality, tests
Safety 60%
99
Quality 40%
94
Run on models
none yet
Process rating
C
50/100
Has gaps
Inputs and preconditions w 11
0
When it triggers w 12
20
Running it twice w 4
30
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

    ✓ Guard found no suspicious behaviour. 1 matches are attack strings quoted in this security skill's own documentation.

    Files scanned: 14. 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 50/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (token-optimizer) differs from the folder (token-optimizer-qsmtco)
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70Execution cost. Instruction body is 4186 tokens
    • 85Steps. 84 steps, 2 vague phrases
    • 100Progress reporting. Reports progress
    • 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 464: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 84 items
    • +3Output format is stated explicitly
    • +4Has examples (25 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.