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AD llm-cost-tracker

Track OpenClaw LLM token usage and cost from OpenRouter API. Reports: last 24h, 7d, 30d, 90d, 365d with model breakdown. Skills that trigger this: - "llm cost", "token usage", "openrouter cost", "llm spend", "daily cost report" → runs run_tracker.py - "collect usage data" → runs collect_usage.py (populates DB silently) When "collect usage data" is received (e.g. from a cron job), run: python3 scripts/collect_usage.py and return "Done" — do NOT send any message to Telegram or any chat.

ClawHub Agent Skills author: larryjoe v1.1.2 MIT-0 12 files body ≈ 495 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationTelegramAI and agentsData 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
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 0

    ✓ No critical or high findings

    Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "read_when"

    Process rating: all ten parameters 46/100

    • 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. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (llm-cost-tracker) differs from the folder (llm-cost-tracker-openrouter-api)
    • 100Tools and files. No external tools needed
    • 100Steps. 7 steps
    • 100Execution cost. Instruction body is 495 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -32 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 7 example trigger phrases
    • +3Description length 489: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 7 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    This cost tracker is mostly coherent, but it needs Review because it silently scans local OpenClaw session files, stores usage metadata locally, and its documented dry-run pruning command actually deletes records.
    LLM: suspicious (high) · 28 May 2026