AC content-channel-research
Structured GO/NO-GO framework for validating content topics before you script or record anything. Runs audience segmentation, adoption research, saturation check, and competitive differentiation analysis to tell you if a topic is worth producing and EXACTLY what angle to take. Use when asking "should I make a video about X?", "is this topic saturated?", "what angle should I take?", or any time a content creator is validating an idea. Prevents wasted production effort on topics that are already saturated or that your audience doesn't actually need.
As a process C 63/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 554 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - note
frontmatter-keyunknown frontmatter key "emoji"
Process rating: all ten parameters 63/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (content-channel-research) differs from the folder (02-content-channel-research)
- 55Failures and branches. 1 branches
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 53 steps
- 100Execution cost. Instruction body is 1788 tokens
- 100Running it twice. No mutating operations
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
- +4Description does not say when NOT to use the skill (false activations)
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 553: enough signal without eating the budget
- +4Structure: 9 headings
- +3Step-by-step instructions: 53 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.