Borrowing it
Nothing to install: this file belongs to swingerman/ha-dual-smart-thermostat. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/swingerman/ha-dual-smart-thermostat/master/.claude/skills/config-flow-integration/SKILL.mdgit clone --depth 1 https://github.com/swingerman/ha-dual-smart-thermostatWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/swingerman/ha-dual-smart-thermostat/config-flow-integration)<a href="https://agentmods.dev/skills/swingerman/ha-dual-smart-thermostat/config-flow-integration"><img src="https://agentmods.dev/badge/skills/swingerman/ha-dual-smart-thermostat/config-flow-integration/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/swingerman/ha-dual-smart-thermostat/config-flow-integration"><img src="https://agentmods.dev/badge/skills/swingerman/ha-dual-smart-thermostat/config-flow-integration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00748 |
| Opus 5 | $0.00022 | $0.00374 |
| Sonnet 5 | $0.00009 | $0.00150 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
config-flow-integration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Configuration flow integration
The mandate and the rule of thumb are in CLAUDE.md; this is the procedure.
See also docs/config_flow/step_ordering.md.
Which flow(s) to update
-
Initial Configuration Flow (
config_flow.py):- New system types or HVAC modes
- New required entities (heater, cooler, sensors)
- New features that should be configured during initial setup
- Core system behavior changes
-
Reconfigure Flow (
config_flow.py- reconfigure handlers):- Changes to existing system configuration that require reconfiguration
- System type switching
- Entity replacement or updates
- Any change that affects the initial configuration flow
-
Options Flow (
options_flow.py):- Feature toggles (enabling/disabling features)
- Feature-specific settings (thresholds, timeouts, behaviors)
- Preset configurations
- Advanced settings that don't require reconfiguration
- Any setting that users might want to change after initial setup
Rule of Thumb: If users need to configure it during initial setup, add it to config/reconfigure flows. If users might want to adjust it later, add it to options flow. Often, you'll need to add to both.
Worked example - adding a floor-temperature option
When adding a new floor temperature feature:
# 1. Add to const.py
CONF_MAX_FLOOR_TEMP = "max_floor_temp"
# 2. Add to schemas.py
FLOOR_TEMP_SCHEMA = vol.Schema({
vol.Optional(CONF_MAX_FLOOR_TEMP): vol.Coerce(float),
})
# 3. Add step in feature_steps/floor_heating_steps.py
async def async_step_floor_heating(self, user_input=None):
"""Configure floor heating options."""
if user_input is not None:
# Validate and store
return self.async_create_entry(...)
# Show form with floor temp options
return self.async_show_form(...)
# 4. Update navigation in config_flow.py
def _determine_next_step(self):
if self._has_floor_sensor():
return "floor_heating" # Add to flow sequence
return "next_step"
# 5. Add tests in tests/config_flow/test_floor_heating_integration.py
async def test_floor_heating_config_flow():
"""Test floor heating configuration in flow."""
# Test implementation
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 95 lines · 44 tokens per session scan A 9f25484ddd1a
config-flow-integration is a skill published in the GitHub repository swingerman/ha-dual-smart-thermostat (233 stars, last pushed 10d ago), licensed Apache-2.0. It adds 44 tokens to every session and 748 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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