SKILLEMALL.ai

BC social

When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, or Facebook, or wants to do social listening and engagement triage. Also use when the user mentions 'LinkedIn post,' 'Twitter thread,' 'social media,' 'content calendar,' 'social scheduling,' 'engagement,' 'viral content,' 'what should I post,' 'repurpose this content,' 'tweet ideas,' 'LinkedIn carousel,' 'social media strategy,' 'grow my following,' 'TikTok video,' 'Reels,' 'Shorts,' 'video script,' 'video hook,' 'short-form video,' 'create a reel,' 'social listening,' 'brand mentions,' 'competitor monitoring,' 'top posts to comment on,' 'find people asking for,' 'carousel,' 'slide-by-slide,' or 'document post.' Use this for social content, repurposing, scheduling, video scripts, and listening. Posts avoid AI tells like 'it's not X, it's Y' reveals and broetry. For broader content strategy, see content-strategy. For paid ads, see ad-creative. For earned media, see public-relations.

coreyhaines31/marketingskills Agent Skills author: coreyhaines31 MIT 11 files body ≈ 4 290 tokens Open the sourcegithub.com↗ analyzed 5 d ago

When the user wants help creating, scheduling, or optimizing social media content for LinkedIn, Twitter/X, Instagram, TikTok, or Facebook, or wants to do…

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorYouTubeMarketingMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 10. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 11): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 12 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Execution cost. Instruction body is 4290 tokens
    • 100Steps. 130 steps
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • low 14 top-level sections: this looks like several domains in one skill
    • 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 1021: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 50 headings
    • +3Step-by-step instructions: 130 items
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (8 of 9)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.