SKILLEMALL.ai
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AC crypto-lens

CryptoLens — AI-driven multi-coin crypto analysis. Compare 2-5 coins (relative performance, correlation matrix, volatility ranking), get single-coin technical analysis charts with MA(7/25/99), RSI, MACD, and Bollinger Bands, or run AI market analysis with scoring engine (0-100 composite score + actionable signals). Dark-theme PNG output. Per-call billing via SkillPay: 1 token (0.001 USDT) per call for all commands. Use when user asks for crypto comparison, portfolio analysis, technical indicators, RSI, MACD, Bollinger Bands, multi-coin analysis, or "should I buy/sell" questions.

ClawHub Agent Skills author: Altair v2.0.1 4 files body ≈ 1 284 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, running it twice

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
99
Quality 40%
90
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token SKILL.md:152
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      Example: `--user-id 0x74…D18`
      quoted

    Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 62/100

    • 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
    • 30Running it twice. 5 mutating operations with no state check
    • 60Tools and files. Uses tools (python) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 44 steps
    • 100Failures and branches. 3 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1284 tokens

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 585: enough signal without eating the budget
    • +4Structure: 10 headings
    • +3Step-by-step instructions: 44 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: suspicious
    The skill is mostly coherent, but its paid billing path has contradictory pricing evidence for the compare command that users should review before installing.
    LLM: suspicious (medium) · VirusTotal: · 29 May 2026