SKILLEMALL.ai

AC resy

Manage Resy restaurant reservations via MCP — search venues, book tables, list and cancel reservations, manage favorites, and subscribe to Priority Notify. Triggers on phrases like "book a table at", "find me a reservation", "what reservations do I have", "cancel my Resy", "add to my Resy hit list", or any request involving restaurant reservations on Resy. Requires resy-mcp installed and the resy server registered (see Setup below).

ClawHub Agent Skills author: chrischall v0.14.2 MIT-0 2 files body ≈ 1 940 tokens Open the sourceclawhub.ai analyzed 2 d ago

Manage Resy restaurant reservations via MCP — search venues, book tables, list and cancel reservations, manage favorites, and subscribe to Priority Notify.

As a process C 58/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
Run on models
none yet
Process rating
C
58/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 0

✓ No critical or high findings

Files scanned: 2. 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")

Process rating: all ten parameters 58/100

  • 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
  • 30Running it twice. 4 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 12 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1940 tokens

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

  • +4Description does not say when NOT to use the skill (false activations)
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 5 example trigger phrases
  • +3Description length 436: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 12 items
  • +3Output format is stated explicitly
  • +4Has examples (7 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.

External checks

ClawHub: suspicious
The skill is purpose-aligned for Resy reservations, but it asks users to run an unpinned npm MCP server with Resy credentials and can book or cancel reservations.
LLM: suspicious (high) · 10 Sept 2026