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BD ClawMeter — Cost Tracking Dashboard

ClawMeter is a self-hosted cost tracking dashboard that monitors your OpenClaw session logs, calculates token usage and API costs, and provides real-time analytics with customizable budget alerts.

modbender/skill-library-mcp Agent Skills author: modbender MIT 21 files · 1 script body ≈ 3 047 tokens Open the sourcegithub.com↗ analyzed 3 d ago

ClawMeter is a self-hosted cost tracking dashboard that monitors your OpenClaw session logs, calculates token usage and API costs, and provides real-time…

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, consistency

IntegrationGitHubDiscordTelegramData and analyticstype 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
B
75/100
safety, quality, tests
Safety 60%
87
Quality 40%
58
Run on models
none yet
Process rating
D
49/100
Unfinished process
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 13

✓ No critical or high findings

Medium and low: 13
  • low Exfiltration read-dotenv CONTRIBUTING.md:126
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv docs/QUICKSTART.md:53
    Reads a .env file
    cp .env.example .env
  • low Dangerous commands cmd-background-process docs/QUICKSTART.md:139
    Starts a background / autostarted process
    nohup npm start > clawmeter.log 2>&1 &
  • low Dangerous commands cmd-background-process docs/QUICKSTART.md:163
    Starts a background / autostarted process
    sudo systemctl enable clawmeter
  • low Secrets in code secret-high-entropy-token package-lock.json:21
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…GLi+2W/6ao+6Y7gu/RCwR…Kng==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:151
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…GLw+xYSd…cqA==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:218
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…FrF+LTRo…W3g==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:227
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…A6j+hAmM…GbS+kf5c…csw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:236
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…5bm+c2gQ…aG5+esrLODihIorn+Pe6F…dXA==",
    detector
  • low Exfiltration read-dotenv README.md:38
    Reads a .env file
    cp .env.example .env
  • low Exfiltration read-dotenv SKILL.md:50
    Reads a .env file
    cp .env.example .env
  • low Dangerous commands cmd-background-process SKILL.md:488
    Starts a background / autostarted process
    sudo systemctl enable clawmeter
  • low Exfiltration exfil-webhook-url src/alerts.mjs:79
    Webhook / callback URL commonly used for exfiltration (verify the destination) (placeholder value)
    const url = `https://api.telegram.org/bot${config.telegram.botToken}/sendMessage`;
    placeholder

Files scanned: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 49/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 8 mutating operations with no state check
  • 40Consistency. Frontmatter name (ClawMeter — Cost Tracking Dashboard) differs from the folder (clawmeter)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 101 steps, 1 vague phrases
  • 100Execution cost. Instruction body is 3047 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 15 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)
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • -42 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 196: enough signal without eating the budget
  • +4Structure: 54 headings
  • +3Step-by-step instructions: 101 items
  • +4Has examples (27 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.