SKILLEMALL.ai

AC self-improving-domotics

Captures smart-home automation conflicts, sensor drift, device connectivity failures, integration regressions, safety rule gaps, and energy optimization opportunities for continuous domotics improvement. Use when: (1) Automations conflict, loop, or misfire, (2) Sensors become stale or inaccurate, (3) Devices are unreachable or intermittently offline, (4) Cloud or local integrations break, (5) Occupancy detection is inconsistent with reality, (6) Latency causes delayed or jittery automations, (7) Energy usage patterns are inefficient, (8) Safety automations need stronger guardrails.

ClawHub Agent Skills author: José I. O. v1.0.1 MIT-0 15 files · 3 scripts body ≈ 6 123 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

IntegrationInfrastructureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
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: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 6123 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 52/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 11 mutating operations with no state check
  • 40Consistency. Frontmatter name (self-improving-domotics) differs from the folder (self-improving-house)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6123 tokens
  • 100Steps. 114 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 23 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
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 588: enough signal without eating the budget
  • +4Structure: 61 headings
  • +3Step-by-step instructions: 114 items
  • +4Has examples (18 code blocks)
  • +4Reference files are cited in the instructions (2 of 3)
  • +3All 3 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: clean
This skill is a disclosed smart-home note-taking and reminder workflow with optional hooks, and I found no hidden device control, exfiltration, or destructive behavior.
LLM: benign (high) · VirusTotal: · 28 Aug 2026