SKILLEMALL.ai

DB get-with-receipt

Name an outcome—from web search to data lookup. Receipt finds eligible paid tools, shows the seller and price, clears agent purchasing under spending limits and purchase approval, and returns the result with signed proof.

Not recommendedcritical or high security findings · low grade D
ClawHub Agent Skills author: Receipt Protocol v1.0.7 MIT-0 9 files · 2 scripts body ≈ 2 736 tokens Open the sourceclawhub.ai↗ analyzed 15 d ago

Name an outcome—from web search to data lookup.

As a process B 66/100 · Nearly there — 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
D
75/100
safety, quality, tests
Safety 60%
77
Quality 40%
72
Run on models
none yet
Process rating
B
66/100
Nearly there
Inputs and preconditions w 11
0
When it triggers w 12
20
Result and completion w 14
40
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

Risky intent medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

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

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 2

  • high Concealment en-hide-from-user SKILL.md:146
    Instruction to hide actions from the user
    Never infer authority, hide a purchase, or silently run one.
Medium and low: 1
  • medium Risky intent intent-wallet-secrets references/SECURITY.md:19
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    - Never enter a crypto seed phrase, mnemonic, private key, or wallet recovery phrase.

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 66/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 40Result and completion. Does not say what the result is
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 49 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2736 tokens
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • 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
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 221: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 49 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: clean
This skill sets up and uses a Receipt OAuth connection for governed paid tool purchases, with the sensitive steps disclosed and bounded by user approval.
LLM: benign (high) · VirusTotal: · 2 Aug 2026