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AC gleap

Gleap REST API integration for customer support analytics and ticket management. Use when the user asks to fetch support tickets, analyze customer support metrics, track team performance, measure response times, generate support reports, monitor ticket volume, check SLA compliance, analyze busiest hours, export team stats, or interact with Gleap data in any way. Trigger on: "gleap", "support tickets", "support metrics", "support report", "team performance", "response time", "time to close", "ticket analysis", "customer support", "support dashboard", "agent performance", "ticket volume", "SLA", "first response time", "reply time", "busiest hours", "ticket topics", "support trends". Also trigger when a user wants to build a support reporting pipeline, automate support analytics, or connect Gleap to other tools (Notion, Slack, etc.).

ClawHub Agent Skills author: berthelol v1.0.0 MIT-0 4 files body ≈ 1 773 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process C 59/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

IntegrationNotionSlackData and analyticsCustomer supporttype 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%
93
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
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
    • low Dangerous commands cmd-cron-mention references/use-cases.md:734
      Mentions editing / listing crontab
      crontab -e

    Files scanned: 4. 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 59/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 40Consistency. Frontmatter name (gleap) differs from the folder (berthelol-gleap)
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 11 steps
    • 100Execution cost. Instruction body is 1773 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)
    • +3Description length 842: 120–800 characters recommended
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 16 example trigger phrases
    • +4Structure: 21 headings
    • +3Step-by-step instructions: 11 items
    • +3Output format is stated explicitly
    • +4Has examples (8 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 93.

    External checks

    ClawHub: suspicious
    This Gleap reporting skill is coherent, but it can expose support data to third-party services and scheduled jobs without enough privacy and control guidance.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026