AC defi-position-tracker
Monitor and analyze DeFi positions across protocols and chains. Track LP (liquidity provider) positions, staking rewards, yield farming returns, impermanent loss calculations, and cost basis per position. Outputs structured data for portfolio reporting, tax handoff to crypto-tax-agent, and treasury dashboards. Supports Uniswap v2/v3, Curve, Aave, Compound, Balancer, Lido, and other major protocols. Use when: tracking active DeFi positions, calculating IL on LP pairs, monitoring yield across farms, preparing DeFi data for tax reporting, or building treasury dashboards for DAOs/funds. NOT for: executing DeFi transactions (buy/sell/stake), bridging assets, swapping tokens, or generating on-chain payroll (use on-chain-payroll). NOT for: real-time price alerts on spot holdings without active DeFi positions. NOT for: NFT portfolio tracking.
As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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 · 3
✓ No critical or high findings
Medium and low: 3
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low Secrets in code
secret-high-entropy-tokenSKILL.md:245High-entropy token-like string (may be an id, hash or a credential)cast call 0xC3…E88 \
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low Secrets in code
secret-high-entropy-tokenSKILL.md:250High-entropy token-like string (may be an id, hash or a credential)cast call 0x87…4E2 \
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low Secrets in code
secret-high-entropy-tokenSKILL.md:396High-entropy token-like string (may be an id, hash or a credential)cast call 0xC3…E88 \
Files scanned: 2. 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 50/100
- 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
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 25 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3558 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
- +3Description length 846: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +4Structure: 17 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (14 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.