SKILLEMALL.ai

AC cofferline

Manage an autonomous agent's on-chain treasury and prediction-market risk via Cofferline — keep a machine wallet funded, gassed, converted, spend-controlled, and accounted for, non-custodially over a REST API; for prediction-market agents (Polymarket) it also vaults trade-scoped credentials write-only, enforces hard risk policies (per-market caps, daily loss stops) server-side from the real books, routes policy-gated LIMIT orders, and journals fills. Use when an agent needs to hold/convert/spend crypto, trade prediction markets under risk limits, set a spending policy, grant scoped revocable signing authority, pay for services in USDC (x402), or produce an audit trail. Cofferline is non-custodial of your wallet and its keys (prepaid fee balances and optional venue credentials are enumerated exceptions in the authority/custody matrix).

ClawHub Agent Skills author: guscoffer v1.0.1 MIT-0 2 files body ≈ 2 452 tokens Open the sourceclawhub.ai analyzed 36 h ago

Manage an autonomous agent's on-chain treasury and prediction-market risk via Cofferline — keep a machine wallet funded, gassed, converted, spend-controlled…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    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: 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 51/100

    • 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
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 25 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2452 tokens
    • 100Running it twice. Mutating operations check current state
    • 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)
    • +3Description length 846: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 25 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: clean
    This skill is a disclosed integration for managing crypto treasury and prediction-market workflows, with high-impact financial authority that is clearly tied to its purpose.
    LLM: benign (high) · VirusTotal: · 19 Aug 2026