AC social-media-scheduler
When user asks to plan social media posts, create content calendar, write captions, generate hashtags, schedule posts, plan content strategy, write tweets, create Instagram captions, plan LinkedIn posts, batch content creation, track post ideas, or any social media content task. 18-feature AI social media content planner with caption writer, hashtag generator, content calendar, post templates, and analytics tracking. All data stays local — NO external API calls, NO network requests, NO data sent to any server. Does NOT post to social media — generates text content for user to copy and post manually.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 56/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 11 mutating operations with no state check
- 40Consistency. Frontmatter name (social-media-scheduler) differs from the folder (social-media-planner)
- 70Execution cost. Instruction body is 4450 tokens
- 100Tools and files. No external tools needed
- 100Steps. 43 steps
- 100Failures and branches. 1 branches, has a failure section
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 27 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
- +1No license
- +2Single-language instructions
- +3Description length 606: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 43 items
- +4Has examples (36 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.