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AC biz-reporter

Automated business intelligence reports pulling data from Google Analytics GA4, Google Search Console, Stripe revenue, social media metrics (Twitter/X, LinkedIn, Instagram), HubSpot CRM, and Airtable into formatted daily KPI snapshots, weekly marketing reports, and monthly business reviews with trend detection and anomaly alerts. Use this skill for: business reports, KPI dashboard, weekly metrics, marketing report, revenue summary, traffic report, analytics summary, performance report, "how are we doing", "show me our metrics", "what are our numbers", MRR tracking, conversion rate analysis, SEO performance report, social media analytics, sales pipeline report, automated reporting via cron, data visualization, business intelligence, growth metrics, churn analysis, or any request to combine data from multiple business tools into a single formatted report. Also works for ad-hoc questions like "how did our launch go" or "compare this month to last month". Delivers via Slack, email, Notion, or Markdown file.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 940 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Automated business intelligence reports pulling data from Google Analytics GA4, Google Search Console, Stripe revenue, social media metrics (Twitter/X…

As a process C 60/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

ProcedureGoogle AnalyticsNotionStripeSlackData 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
60/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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: 1. 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 60/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 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
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 46 steps, 1 vague phrases
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1940 tokens
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 1018: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 5 example trigger phrases
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 46 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.