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AC openboard-cli

Install and use OpenBoardCLI with `npm install -g openboard-cli` to turn Gmail receipts or CSV, Excel, and JSON data into authenticated React spending dashboards. Requires Node.js 18+; Gmail invoice fetchers require Python 3 and `beautifulsoup4`, plus `pdfplumber` for PDF bills. Agent command contract: https://openboard-site.vercel.app/llms.txt. Supports Local only, Hybrid, and All remote operation; full remote needs GitHub and Vercel tokens (or their supported authenticated integrations) plus a runnable Codex CLI or another configured cloud LLM, while local users can use Ollama or LM Studio.

ClawHub Agent Skills author: Syed Ateebul Islam v1.0.0 MIT-0 2 files body ≈ 542 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Install and use OpenBoardCLI with npm install -g openboard-cli to turn Gmail receipts or CSV, Excel, and JSON data into authenticated React spending dashboards.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

IntegrationGitHubGmailAI and agentsData and analyticsSoftware developmenttype 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%
84
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
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: 2. 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 51/100

    • 0Result and completion. Does not say what the result is
    • 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. 3 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 542 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

    • +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
    • +1No license
    • +2Single-language instructions
    • +3Description length 599: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This skill gives disclosed, purpose-aligned instructions for using OpenBoardCLI, but users should be careful with the global npm install and mutable remote command reference.
    LLM: benign (medium) · VirusTotal: · 10 Sept 2026