building-fdd-validation
Skill4 starsChanged 2 months agoApache-2.0View on GitHub
Implement or review building fault-detection logic against point quality, units, equipment modes, time windows, and independent fault scenarios. Not UI styling, compliance certification, or physical-control authorization.
In this repository81 other files it gives its agents.
- agent-cli-designSkill
- agent-tool-boundariesSkill
- behavior-first-implementationSkill
- bounded-prototypeSkill
- bounded-pr-sliceSkill
- context-efficiencySkill
- cross-language-contractsSkill
- cross-platform-agent-instructionsSkill
- design-interviewSkill
- diagnosis-loopSkill
- domain-languageSkill
- edge-cloud-syncSkill
- handoff-continuitySkill
- immutable-pr-reviewSkill
- memory-context-hygieneSkill
- module-designSkill
- orchestrateSkill
- owner-decision-gateSkill
- parallel-portfolioSkill
- pr-gate-loopSkill
- pyo3-async-lifecycleSkill
- pyo3-buffer-performanceSkill
- pyo3-free-threadingSkill
- pyo3-ownership-errorsSkill
- pyo3-wheel-releaseSkill
- python-api-typingSkill
- python-async-concurrencySkill
- python-ci-releaseSkill
- python-data-boundariesSkill
- python-packaging-environmentsSkill
- python-performanceSkill
- python-pytestSkill
- python-reviewSkill
- python-test-designSkill
- python-threading-workersSkill
- python-workflowSkill
- react-components-stateSkill
- react-performanceSkill
- repository-groundingSkill
- rust-api-designSkill
- rust-async-concurrencySkill
- rust-build-diagnosticsSkill
- rust-ci-releaseSkill
- rust-memory-layoutSkill
- rust-nextestSkill
- rust-numerics-simdSkill
- rust-performanceSkill
- rust-protocol-codecsSkill
- rust-python-bindingsSkill
- rust-reviewSkill
- rust-storage-durabilitySkill
- rust-test-designSkill
- rust-unsafe-ffiSkill
- rust-workflowSkill
- rust-workspace-dependenciesSkill
- shadcn-react-componentsSkill
- shadcn-svelte-componentsSkill
- simplify-existing-codeSkill
- source-verificationSkill
- svelte-components-runesSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 9
Start from the actual rule definition, equipment model, and authoritative sequence or specification supplied for the task.
- Distinctive: line 21
Do not invent universal thresholds or timers from a similar piece of equipment.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Building FDD Validation
Start from the actual rule definition, equipment model, and authoritative sequence or specification supplied for the task. The checklist below is engineering guidance, not a substitute for an ASHRAE clause, protocol standard, or validated plant model.
Establish when evaluation is meaningful
Map required points, semantic meaning, units, sign conventions, source identity, sampling assumptions, and quality states. Keep missing, invalid, stale, unavailable, and known zero distinct. A numeric input is not automatically usable evidence. Inspect operating mode, enable conditions, occupancy or schedule, commanded versus observed state, and sensor plausibility as the rule requires.
Separate “not evaluated” from “evaluated and normal.” Do not turn missing samples into a healthy result, fill a long data gap by interpolation without a contract, or compare incompatible time windows and units. Preserve provenance so an operator can trace a finding to the evidence used.
Make temporal behavior explicit
Inspect threshold direction, hysteresis, persistence duration, startup/transient exclusions, reset conditions, suppression, and recovery. Define behavior across out-of-order samples, clock changes, process restart, mode changes, and data gaps. Use deterministic event-time fixtures rather than long sleeps.
Do not invent universal thresholds or timers from a similar piece of equipment. Follow the versioned rule/sequence and surface an unresolved domain choice. Acknowledging or suppressing a finding does not prove the equipment recovered.
Validate independently
Exercise a normal trace, a clear fault, a near-threshold trace, unavailable evidence, a mode transition, and recovery. Derive expected outcomes independently from the implemented algorithm. Test the rule's preconditions as well as its inequality. For interconnected equipment, verify which asset and evidence own the finding; avoid multiplying one root cause into misleading independent counts.
For learned detectors, protect evaluation from time/site leakage and report the labeled scope, false alarms, missed faults, and uncertainty actually measured. Synthetic traces are useful regression evidence, not proof of field performance.
Energy or emissions impact requires a defensible baseline, units, time horizon, and avoidance of double counting. Never fabricate savings to populate a demo. Keep findings advisory unless an authorized control workflow explicitly accepts them.
Return preserved semantics, exercised scenarios, observed outcomes, and the specific specification or field-validation gaps.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

