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BD nansen-token-transfer-analysis

Where is this token moving and why? Large transfers, flow trends over time, and breakdown by wallet label.

ClawHub Agent Skills author: Nansen AI v0.1.0 MIT-0 2 files body ≈ 274 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 36/100 · Unfinished process — weak spots: steps, result and completion, when it triggers

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
B
84/100
safety, quality, tests
Safety 60%
98
Quality 40%
62
Run on models
none yet
Process rating
D
36/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token SKILL.md:25
    High-entropy token-like string (may be an id, hash or a credential)
    # Examples: UNI on ethereum (0x1f…984)
  • low Secrets in code secret-high-entropy-token SKILL.md:26
    High-entropy token-like string (may be an id, hash or a credential)
    #           BONK on solana (DezX…263)

Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 36/100

  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 100Tools and files. Tools declared in frontmatter
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 274 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)
  • +3Description length 106: 120–800 characters recommended
  • +4Structure: 1 headings, hard to scan
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: clean
This skill is a straightforward Nansen CLI helper for token-transfer research, with disclosed API-key and package-install requirements.
LLM: benign (high) · VirusTotal: · 29 May 2026