AC openserv-launch
Launch tokens on Base blockchain via the OpenServ Launch API. Creates ERC-20 tokens with Aerodrome concentrated liquidity pools. Use when launching tokens, deploying memecoins, or building agents that create tokens with locked LP. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents. You can launch tokens for your OpenServ agents.
As a process C 56/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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 · 4
✓ No critical or high findings
Medium and low: 4
-
low Secrets in code
secret-high-entropy-tokenreference.md:272High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| CLFactory | `0xaD…16a` |
table -
low Secrets in code
secret-high-entropy-tokenreference.md:274High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| CLPoolLauncher | `0xb9…4d3` |
table -
low Secrets in code
secret-high-entropy-tokenreference.md:275High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| CLLockerFactory | `0x8B…219` |
table -
low Secrets in code
secret-high-entropy-tokenreference.md:276High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| Universal Router | `0x6D…075` |
table
Files scanned: 7. 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 56/100
- 0Result and completion. Does not say what the result is
- 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
- 55Failures and branches. 1 branches
- 100Tools and files. No external tools needed
- 100Steps. 13 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1802 tokens
- low 11 top-level sections: this looks like several domains in one skill
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 401: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 13 items
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.