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AC slack-lead-scanner

Monitor a Slack channel for images/text of leads (badges, business cards), extract data, enrich with Apollo.io API, let Apollo auto-sync to HubSpot, search HubSpot for the new contact link, and reply in-thread with summary and link. Also monitors Slack DM replies to update HubSpot fields on existing leads. Use when setting up or managing Slack-based lead scanning, including polling configuration via cron.

ClawHub Agent Skills author: Mark Chen v1.0.0 MIT-0 3 files body ≈ 1 182 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

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

IntegrationSlackHubSpotSales and CRMtype 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
91/100
safety, quality, tests
Safety 60%
96
Quality 40%
83
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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 · 4

    ✓ No critical or high findings

    Medium and low: 4

    ✓ Guard found no suspicious behaviour. 4 matches are attack strings quoted in this security skill's own documentation.

    Files scanned: 3. 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
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 7 mutating operations with no state check
    • 40Consistency. Frontmatter name (slack-lead-scanner) differs from the folder (badgebot)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 21 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 1182 tokens
    • 100Progress reporting. Reports progress

    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)
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 408: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 21 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: suspicious
    This skill is a coherent Slack lead-capture automation, but it needs review because it can move personal contact data across Slack, Apollo, and HubSpot and update CRM records with limited safeguards.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026