SKILLEMALL.ai

AC social-proof-and-testimonials

The proof content type — turn real customer reviews, testimonials, UGC, case studies, and results into believable, objection-matched social content that converts. Use when someone wants to share testimonials, post reviews, turn happy customers or a case study into content, reshare UGC, build social proof, or add proof at a decision point. Uses the VOUCH framework. Reads brand-profile + audience-research first. The agent curates + frames REAL proof, matched to the buyer objection; the human sources the proof and secures consent/rights; WoopSocial publishes. Feeds the format writers, design-and-templates, and the placement skills. NEVER fabricate, AI-generate, inflate, or deceptively suppress reviews/testimonials; disclose paid/gifted/insider connections (FTC); get consent + likeness rights; never guarantee conversions. Distinct from ugc-and-influencer (sources/manages creators), storytelling-and-narrative (the narrative craft), and data-and-original-research (originates stats).

ClawHub Agent Skills author: Social Media Skills v1.0.1 MIT-0 7 files body ≈ 1 639 tokens Open the sourceclawhub.ai analyzed 3 d ago

The proof content type — turn real customer reviews, testimonials, UGC, case studies, and results into believable, objection-matched social content that…

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

AnalyzerMarketingtype 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. 5 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 100Tools and files. No external tools needed
    • 100Steps. 11 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1639 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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 991: 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: 11 items
    • +4Reference files are cited in the instructions (3 of 4)

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

    External checks

    ClawHub: clean
    This is a non-executable writing workflow for real customer proof, with one wording ambiguity about who verifies authenticity before publication.
    LLM: benign (high) · VirusTotal: · 27 Jul 2026