BC tokenbroker
AI Agent Skill for GitHub project analysis and nad.fun token launch. Analyzes repos, generates token identity/promo, and launches on nad.fun.
As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.
Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.
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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medium Risky intent
intent-wallet-secretsSETUP.md:123Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target| `nadfun` | Wallet private key, API keys | See [nad.fun/skill.md](https://nad.fun/skill.md) |
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medium Risky intent
intent-wallet-secretsSKILL.md:58Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target- Wallet private key management (handled by host)
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low Exfiltration
net-credential-useSETUP.md:109Credential used in a network call (verify the destination is the intended service) (destination is a well-known publishing service)curl -H "Authorization: token $GITHUB_TOKEN" https://api.github.com/user
known service
Files scanned: 17. 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 51/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 25 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1462 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 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 141: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.