SKILLEMALL.ai

AB community-building-playbook

Build and grow developer and user communities from scratch — covering ambassador programs, community-led growth (CLG), event operations, content strategy, and health metrics. Grounded in real playbooks from Notion, Lovable, AFFiNE, ClickUp, Asana, and Lark. Use when: building a community for an open-source project, SaaS product, or brand; recruiting and managing ambassadors or community leaders; planning community events (online/offline meetups, AMAs, hackathons); designing ambassador tier/points systems; setting community health KPIs; launching a Build-in-Public strategy; setting up Discord, Slack, or Lark communities; running developer relations (DevRel); or when the user asks about community operations, ambassador programs, CLG, user advocacy, KOC/KOL cultivation, UGC flywheels, community monetization, or any variation of "how do I grow my community".

ClawHub Agent Skills author: Iris Wei v1.0.6 MIT-0 4 files body ≈ 4 440 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process B 69/100 · Nearly there — weak spots: result and completion, progress reporting

ProcedureAsanaNotionSlackDiscordData and analyticsMarketingInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Failures and branches w 10
60
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: 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 69/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 70Execution cost. Instruction body is 4440 tokens
    • 85Steps. 75 steps, 2 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. Mutating operations check current state
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Description length 866: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -221 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +4Structure: 37 headings
    • +3Step-by-step instructions: 75 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

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

    External checks

    ClawHub: suspicious
    The skill is a non-executable community growth playbook, but it includes cold outreach and personal-data collection guidance without enough consent, privacy, or platform-rule safeguards.
    LLM: suspicious (medium) · VirusTotal: · 4 Aug 2026