BC gekko-strategist
AI-powered DeFi strategy development agent. Design, backtest, adapt, and evaluate yield farming strategies based on market conditions, risk profiles, and capital allocation goals. The brain of the Gekko system.
AI-powered DeFi strategy development agent.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
How to improve
- 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 · 6
✓ No critical or high findings
Medium and low: 6
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:115High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Seamless USDC | `0x61…738` |
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:116High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Moonwell USDC | `0xc1…2Ca` |
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:117High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Spark USDC | `0x7b…34A` |
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:118High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Gauntlet USDC Prime | `0xe8…b61` |
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:119High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Yo USDC | `0x00…a65` |
table -
low Risky intent
intent-offensive-securitySKILL.md:130Offensive-security / dual-use content (legitimate for authorised testing; review intended use)All strategy allocations target audited, open-source vault contracts. Strategist generates allocation recommendations only — actual execution requires explicit wallet signing through the Executor agen
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 51/100
- 0Result and completion. Does not say what the result is
- 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. 2 mutating operations with no state check
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 18 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1069 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 210: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.