SKILLEMALL.ai
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BF tiktok-app-marketing

Automate TikTok slideshow marketing for any app or product. Researches competitors, generates AI images, adds text overlays, posts via Postiz, tracks analytics, and iterates on what works. Use when setting up TikTok marketing automation, creating slideshow posts, analyzing post performance, optimizing app marketing funnels, or when a user mentions TikTok growth, slideshow ads, or social media marketing for their app. Covers competitor research (browser-based), image generation, text overlays, TikTok posting (Postiz API), cross-posting to Instagram/YouTube/Threads, analytics tracking, hook testing, CTA optimization, conversion tracking with RevenueCat, and a full feedback loop that adjusts hooks and CTAs based on views vs conversions.

modbender/skill-library-mcp Agent Skills author: modbender MIT 13 files body ≈ 11 711 tokens Open the sourcegithub.com↗ analyzed 33 h ago

Automate TikTok slideshow marketing for any app or product.

As a process F 48/100 · Will not run — References files that are not bundled: scripts/rc-api.sh

ProcedureYouTubeMarketingtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
F
48/100
Will not run
References files that are not bundled: scripts/rc-api.sh
Tools and files w 18
0
Result and completion w 14
0
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.
  2. The text references files that are not there: add them or drop the references.
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: 13. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 11711 tokens (recommended < 5000); move details to references/
  • warning missing-ref reference to a missing file: scripts/rc-api.sh

Process rating: all ten parameters 48/100

Will not run. References files that are not bundled: scripts/rc-api.sh
  • 0Tools and files. 1 referenced file(s) missing: scripts/rc-api.sh
  • 0Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (tiktok-app-marketing) differs from the folder (larry)
  • 40Execution cost. Instruction body is 11711 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 194 steps, 3 vague phrases
  • 100Failures and branches. 7 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +3Output format is not stated: the model decides each time
  • -213 emoji in the instructions: noise for the model
  • -31 of 7 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 743: enough signal without eating the budget
  • +4Structure: 34 headings
  • +3Step-by-step instructions: 194 items
  • +4Has examples (17 code blocks)
  • +4Reference files are cited in the instructions (3 of 5)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.