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AC Classify Customer Intents with AI — Route to Slack & CRM

Classify customer micro-moments into Buy Now, Research, Frustration, Advocacy, or Churn Risk with confidence scores. Use when the user needs real-time intent detection, personalized next actions, or audience segmentation from support tickets, DMs, and comments.

ClawHub Agent Skills author: ncreighton v1.0.0 MIT-0 2 files body ≈ 3 697 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Classify customer micro-moments into Buy Now, Research, Frustration, Advocacy, or Churn Risk with confidence scores.

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice

AnalyzerSlackSales and CRMInfrastructureCustomer 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Consistency w 8
40
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

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 58/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 7 mutating operations with no state check
    • 40Consistency. Frontmatter name (Classify Customer Intents with AI — Route to Slack & CRM) differs from the folder (micro-moment-intent-classifier)
    • 50Failures and branches. 0 branches, has a failure section
    • 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
    • 85Steps. 84 steps, 1 vague phrases
    • 100Execution cost. Instruction body is 3697 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 261: enough signal without eating the budget
    • +4Structure: 40 headings
    • +3Step-by-step instructions: 84 items
    • +3Output format is stated explicitly
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    The skill is purpose-aligned for classifying customer intent, but it under-specifies risky automation and data-sharing behavior for customer communications and CRM workflows.
    LLM: suspicious (high) · 31 Aug 2026