SKILLEMALL.ai

AC campaign-and-launch-planning

Plan and orchestrate a product launch or social media campaign end to end — the multi-week arc, channel by channel. Use when someone wants to plan a launch, product drop, campaign, big announcement, waitlist/pre-launch push, or coordinated multi-platform moment. Plans a time-boxed campaign as ONE story across three phases (tease -> launch -> sustain) toward a single goal, and schedules the coordinated social posts via WoopSocial. Uses the STORY framework. Reads brand-profile + goals-and-kpis first. WoopSocial schedules + publishes the social posts on the timeline; the waitlist/hub, email sequence, checkout, paid ads, press/creator outreach, and livestream are external/native, and conversion/ROI come from native analytics + the store (never fabricated). Urgency must be credible (no fake countdowns/scarcity). Distinct from content-calendar (the ongoing always-on cadence) and goals-and-kpis (the targets a campaign serves).

ClawHub Agent Skills author: Social Media Skills v1.0.0 MIT-0 7 files body ≈ 1 470 tokens Open the sourceclawhub.ai analyzed 2 d ago

Plan and orchestrate a product launch or social media campaign end to end — the multi-week arc, channel by channel.

As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 6. 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 56/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 14 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 100Tools and files. No external tools needed
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1470 tokens
    • high The skill tells the model to perform an irreversible action with no human approval
    • low No test case covers injection arriving through data

    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 933: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 10 items
    • +4Reference files are cited in the instructions (4 of 4)

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

    External checks

    ClawHub: clean
    This skill is a launch-planning helper that clearly discloses its role in drafting and scheduling social posts, with no hidden code or unrelated access found.
    LLM: benign (high) · VirusTotal: · 22 Jul 2026