BC Moltbook Trend Analysis
Fetch, analyze, and compare trending posts from Moltbook to inform your content strategy. Generates virality reports with real statistical benchmarks from 36k+ posts.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
AnalyzerData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
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: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 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
- 40Consistency. Frontmatter name (Moltbook Trend Analysis) differs from the folder (analyze-moltbook-trending-posts)
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 31 steps
- 100Execution cost. Instruction body is 2559 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
- +1No license
- +2Single-language instructions
- +3Description length 166: enough signal without eating the budget
- +4Structure: 33 headings
- +3Step-by-step instructions: 31 items
- +4Has examples (10 code blocks)
- +3All 4 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.
External checks
ClawHub: suspicious
The skill mostly does its advertised Moltbook trend analysis, but one documented delay setting can be abused to run local Python code.
LLM: suspicious (high) · VirusTotal: · 29 May 2026