BC red-note-content-topic
AI-powered Xiaohongshu (RED) topic planner. Generates trending topic ideas and title directions based on account positioning and target audience. Requires API key from wsdsocial.com.
AI-powered Xiaohongshu (RED) topic planner.
As a process C 53/100 · Has gaps — weak spots: steps, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 0. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 53/100
- 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
- 43Steps. 2 steps, 1 vague phrases
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 254 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3No numbered steps or checklist
- +1No license
- +2Single-language instructions
- +3Description length 182: enough signal without eating the budget
- +4Structure: 5 headings
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.
External checks
ClawHub: clean
The artifacts describe operational ClawHub, Axiom, Convex, and documentation skills with powerful but disclosed, task-directed behavior.
LLM: benign (medium) · VirusTotal: · 10 Jul 2026