SKILLEMALL.ai

BC Pareto

Maps any topic's zeitgeist and frontier — sweeps YouTube, GitHub, the web and the Research skill, ranks everything down to the vital few by relevance, reach, recency and outlier-ness, then reports the past meta, the current mainstream meta, and where the forward thinkers are heading, every frontier claim linked. USE WHEN pareto, pareto this, run pareto on, zeitgeist, what's the zeitgeist on, what's the meta on, what is the current meta, where is thinking on X heading, who are the forward thinkers on, who is ahead of the curve on, what are the big thinkers saying about, top content on a topic, vital few. NOT FOR answering a specific factual question (Research), analyzing one piece of content (ExtractWisdom), or AI industry news digests (Upgrade NewsDigest; security news SecurityUpdates).

danielmiessler/Personal_AI_Infrastructure Agent Skills author: danielmiessler 9 files · 3 scripts body ≈ 1 083 tokens Open the sourcegithub.com↗ analyzed 3 h ago

Maps any topic's zeitgeist and frontier — sweeps YouTube, GitHub, the web and the Research skill, ranks everything down to the vital few by relevance, reach…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerYouTubeGitHubData and analyticsMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

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

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "evals"

    Process rating: all ten parameters 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 12 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1083 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 797: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 12 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.