SKILLEMALL.ai

AF social-post-generator

Generate engaging social media content for multiple platforms. Use when users need to create posts for Twitter/X, LinkedIn, Instagram, Facebook, or other social platforms. Supports tone customization, hashtag generation, content repurposing, and platform-specific formatting.

modbender/skill-library-mcp Agent Skills author: modbender MIT 8 files body ≈ 852 tokens Open the sourcegithub.com analyzed 2 d ago

Generate engaging social media content for multiple platforms.

As a process F 30/100 · Will not run — References files that are not bundled: scripts/count_characters.py

GeneratorMarketingtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
86
Run on models
none yet
Process rating
F
30/100
Will not run
References files that are not bundled: scripts/count_characters.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. 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: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/count_characters.py

Process rating: all ten parameters 30/100

Will not run. References files that are not bundled: scripts/count_characters.py
  • 0Tools and files. 1 referenced file(s) missing: scripts/count_characters.py
  • 0Result and completion. Does not say what the result is
  • 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
  • 85Steps. 45 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 852 tokens
  • low 11 top-level sections: this looks like several domains in one skill

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
  • +2Single-language instructions
  • +3Description length 275: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 45 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.