SKILLEMALL.ai

AD dollar-platoon

Peer-to-peer task payroll marketplace on Base L2. Clients create USDC-funded gigs, distribute tasks to gigworkers via email/webhook mailboxes, review proofs of work, and pay out on-chain. Reputation-driven with no dispute resolution. Use when: (1) Creating or joining gigs, (2) Submitting or reviewing proofs, (3) Managing wallets and payouts, (4) Understanding pricing or marketplace dynamics, (5) Integrating via webhook or public submit link. Triggers: dollar platoon, gig payroll, micro-gig, proof review, rollup payout, volunteer mailbox, task distribution, reputation system, treasury contract, recommended prices, how it works.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 9 354 tokens Open the sourcegithub.com analyzed 2 d ago

Peer-to-peer task payroll marketplace on Base L2.

As a process D 48/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationCommerceFinancetype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
48/100
Unfinished process
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

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 9354 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 48/100

  • 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. 40 mutating operations with no state check
  • 40Execution cost. Instruction body is 9354 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (web) that frontmatter does not declare
  • 85Steps. 86 steps, 3 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 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 634: enough signal without eating the budget
  • +4Structure: 73 headings
  • +3Step-by-step instructions: 86 items
  • +4Has examples (35 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.