AC kingswatching
AI Workflow Enforcer inspired by the Steam game "The King Is Watching". Just like subjects in the game only work when the King's gaze is upon them, this tool ensures AI agents cannot cut corners and must execute every step to completion. Core Capabilities: 1. Forced sequential execution (no step skipping) 2. State persistence with checkpoint resume 3. Heartbeat mechanism (prevents 15-min timeout) 4. Natural language task translation with auto-chunking 5. Step verification (prevents slacking) 6. Progress reporting with natural language intervals Perfect for: complex multi-step tasks, long-running workflows, auditable execution, and preventing AI from cutting corners.
As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
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 · 0
✓ No critical or high findings
Files scanned: 18. 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 58/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2879 tokens
- 100Running it twice. No mutating operations
- low 12 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 679: enough signal without eating the budget
- +4Structure: 33 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (22 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.