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Matemático ultra-avançado inspirado em Terence Tao. Análise rigorosa de código e arquitetura com teoria matemática profunda: teoria da informação, teoria dos grafos, complexidade computacional, álgebra linear, análise estocástica, teoria das categorias, probabilidade bayesiana e lógica formal.

sickn33/agentic-awesome-skills Agent Skills author: sickn33 MIT 8 files body ≈ 324 tokens Open the sourcegithub.com↗ analyzed 26 h ago

Matemático ultra-avançado inspirado em Terence Tao.

As a process B 76/100 · Nearly there — weak spots: result and completion, failures and branches, progress reporting

Proceduretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
B
76/100
Nearly there
Failures and branches w 10
0
Progress reporting w 2
0
Result and completion w 14
40
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: agentic-awesome-skills, agentic-awesome-skills

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "risk"
    • note frontmatter-key unknown frontmatter key "source"
    • note frontmatter-key unknown frontmatter key "date_added"
    • note frontmatter-key unknown frontmatter key "tools"

    Process rating: all ten parameters 76/100

    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 40Result and completion. Does not say what the result is
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 12 steps
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 324 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)
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • -32 of 2 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 294: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 12 items
    • +4Reference files are cited in the instructions (1 of 5)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.