SKILLEMALL.ai

BB tetrac-perp-trader

Multi-exchange perpetuals trading CLI for 15+ venues (Orderly, Bybit, Binance, Hyperliquid, dYdX, OKX, Bitget, BloFin, AsterDEX, and more) via the TTC Box API. Place orders, manage positions, run TWAP/DCA ladders, scan technical signals, build trailing-stop loops, and run an agentic /loop. Bundles a prebuilt Rust binary for darwin-arm64 and linux-x64 — no Rust toolchain required on the host.

ClawHub Agent Skills author: tetrac-official v0.1.5 MIT-0 6 files body ≈ 7 183 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process B 68/100 · Nearly there — weak spots: inputs and preconditions

IntegrationSoftware developmentAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
B
68/100
Nearly there
Inputs and preconditions w 11
0
When it triggers w 12
50
Tools and files w 18
60
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.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 6. 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")
  • warning body-long SKILL.md body ≈ 7183 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 68/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 7183 tokens
  • 100Steps. 110 steps
  • 100Failures and branches. 13 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 18 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (17 tags): a typed call is more reliable

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)
  • +2Single-language instructions
  • +3Description length 394: enough signal without eating the budget
  • +4Structure: 52 headings
  • +3Step-by-step instructions: 110 items
  • +3Output format is stated explicitly
  • +4Has examples (38 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +1License stated

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

External checks

ClawHub: suspicious
This real-money crypto trading skill has useful safety instructions, but it needs Review because its package claims missing bundled executables and its API docs show unsafe handling of exchange credentials.
LLM: suspicious (high) · VirusTotal: · 29 May 2026