SKILLEMALL.ai

BC AI Marketing Agent — SEO, Leads & Social

Full-stack AI marketing toolkit — scout X/Twitter and Reddit for trending topics, discover and deep-analyze competitors, find content gaps, publish SEO- and GEO-optimized articles with AI illustrations and voice-over in 55 languages, create social media adaptations for X, LinkedIn, Facebook, Reddit, Threads, Instagram, Instagram Reels, YouTube Shorts, and Shopify, generate lead magnets (checklists, swipe files, frameworks), ingest any URL (YouTube videos, web articles, PDFs, audio files) into structured content, ultra-cheap turbo articles from 2 credits, generate short-form AI avatar videos with subtitles, and run fully automated content autopilot. Powered by Citedy.

modbender/skill-library-mcp Agent Skills author: modbender MIT 2 files body ≈ 10 672 tokens Open the sourcegithub.com analyzed 2 d ago

Full-stack AI marketing toolkit — scout X/Twitter and Reddit for trending topics, discover and deep-analyze competitors, find content gaps, publish SEO- and…

As a process C 59/100 · Has gaps — weak spots: inputs and preconditions, consistency, execution cost

ProcedureYouTubeShopifyWriting and documentsMedia and videoMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Running it twice w 4
30
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning body-long SKILL.md body ≈ 10672 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "privacy_policy_url"
  • note frontmatter-key unknown frontmatter key "security_notes"

Process rating: all ten parameters 59/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 44 mutating operations with no state check
  • 40Consistency. Frontmatter name (AI Marketing Agent — SEO, Leads & Social) differs from the folder (citedy-seo-agent)
  • 40Execution cost. Instruction body is 10672 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 85Steps. 210 steps, 1 vague phrases
  • 100Failures and branches. 3 branches, has a failure section
  • 100Progress reporting. Reports progress
  • low 14 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (5 tags): a typed call is more reliable

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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 675: enough signal without eating the budget
  • +4Structure: 64 headings
  • +3Step-by-step instructions: 210 items
  • +3Output format is stated explicitly
  • +4Has examples (54 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.