bbartling/open-fdd - Open Source PR Review Scorecard

Fault Detection Diagnostics (FDD) for HVAC datasets

C-Rank Grade: A (Welcoming) - 65/100

External PR Merge Rate: 87%

Response Time: 8d

First Timer Success: 100%

Frequently Asked Questions

Is bbartling/open-fdd welcoming to first-time open-source contributors?

bbartling/open-fdd has a recorded first-timer success rate of 100.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in bbartling/open-fdd respond to incoming external pull requests in approximately 202.7 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 65.0 C-Rank™ score (A Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 65.0 places bbartling/open-fdd in the A tier.

What is the external contributor pull request merge rate for bbartling/open-fdd?

The external contributor pull request merge rate for bbartling/open-fdd is 86.8%, based on public PR activity from non-core contributors.

Are there Good First Issues available in bbartling/open-fdd?

bbartling/open-fdd currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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bbartling/open-fdd

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Active (30d)A•Welcoming(65/100)Rust

Fault Detection Diagnostics (FDD) for HVAC datasets

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AI Maintainer Review Guidelines

Review Persona

Strict Architecture Gatekeeper

Warmth Score
6.8/10
Patience Score
7.6/10
Nitpick Rate
45%

Strict review standards in bbartling/open-fdd. Ensure PR scope matches issue requirements closely before requesting review.

Top PR Submission Do's

  • •Ensure code complies with the project coding style
  • •Keep PRs scoped to a single concern
  • •Include context and link to the related issue

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit PRs without linking an issue
  • •Do not break existing tests without fixing them
  • •Do not mix unrelated refactors in a single PR
Response Velocity
8 days+
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
86.8%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
100.0%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
2 core
Small core review team
Diagnostic Health HUD
86.8%
Merge Gauge
100.0%
1st-Timer
Community Vibe65/100

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Active Good First Issues (5)

View on GitHub

When budget permits utilize Tenable Nessus web app and OT scanning services. https://www.tenable.com/products/nessus If anyone else knows a good tools and practices please comment. There is pretty decent security testing scripts Python all designed by Astra MAX to web app PEN test. https://github.com/bbartling/open-fdd/blob/master/.cursor/agents/openfdd-security-python-harness.md

📅 Opened Sep 24, 2026💬 14 comments
Quality: 75/100Contribute

Purpose Establish a measured operating envelope for simultaneous MQTT ingestion, scheduled AFDD, and interactive analytics on Railway. Decide from evidence whether the existing web/central/MQTT topology is sufficient, needs configuration changes, or needs a dedicated analytics worker. Keep Parquet + DataFusion as the implementation baseline while completing this qualification. A database migration is an alternative requiring a separate benchmark and architecture decision, not a prerequisite for closing this issue. Related implementation: #995. This issue is the acceptance tracker; merging an implementation PR does not close it. Use Refs #<this issue> in related PRs until the acceptance evidence is complete. Current status As of 2026-09-23, implementation and candidate qualification are in progress. The internal audit distinguishes source fixes, evaluator unit tests, deployment verification, compaction correctness, and scheduled-timer evidence. Several deployment and data-conservation c

📅 Opened Sep 23, 2026💬 0 comments
Quality: 70/100Contribute

Community assistance requested Open-FDD now includes an initial catalog of engineering calculations for HVAC energy-conservation measures (ECMs). I would appreciate technical review from energy engineers, mechanical engineers, commissioning and RCx practitioners, M&V professionals, and others with relevant field experience. Documentation: https://bbartling.github.io/open-fdd/ecm/engineering-calcs.html The current tooling includes: 40 Excel workbook ECM modules with live formulas 9 independent Python referee calculators for cross-checking results Finance helpers for simple payback, NPV, utility costs, and energy-use intensity ASHRAE Guideline 14 and IPMVP-oriented M&V helpers Optional EnergyPlus comparisons for additional validation Areas where review would be especially helpful Please review any calculation or module you are familiar with for: Formula accuracy, units, conversions, and sign conventions Reasonableness of assumptions, realization factors, and calculation boundaries HVA

📅 Opened Sep 22, 2026💬 5 comments
Quality: 90/100Contribute

Summary Open-FDD currently provides a versioned wattlab_dump_v3 engineering bundle containing FDD findings, mapped telemetry, sensor statistics, schedules, weather data, equipment metadata, EnergyPlus model-seed inputs, and other RCx analytics. The project also includes an offline eplus_clustering_v1 exporter that creates: clustering_features.csv clustering_features.parquet clustering_timeseries_long.parquet fdd_fault_vector.csv MANIFEST.json An example KMeans workflow This is a useful foundation, but it is not yet a complete, validated machine-learning handoff. The current clustering feature table contains approximately one statistical row per piece of equipment. It does not provide the time-windowed observations, target definitions, chronological train/validation/test splits, feature lineage, data-quality gates, or leakage controls needed for serious ML development. This issue should validate the existing ZIP and evolve it into a portable, reproducible, AI-agent-friendly Open-FDD E

📅 Opened Aug 21, 2026💬 2 comments
Quality: 65/100Contribute

Objective - Cloud Security Deploy and test the latest Open-FDD services on Railway to determine whether Railway is a suitable platform for professionally hosting Open-FDD demonstration and production environments. Railway: https://railway.com/ Or other? Or local builds welcome as well. Scope 1. Railway deployment validation Deploy the latest Open-FDD container images from GHCR. Configure the required environment variables, networking, storage, and service dependencies. Confirm that each service starts successfully and remains healthy. Verify communication between the Open-FDD services. Confirm that the web application is accessible through a Railway-generated HTTPS URL. Document the deployment process, configuration requirements, and any Railway-specific limitations. Test service restarts and redeployment using updated GHCR images. 2. Application security review Perform an authorized security review of the Open-FDD application deployed specifically for testing. Review areas should in

📅 Opened Aug 20, 2026💬 22 comments
Quality: 90/100Contribute
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Contributor Compatibility & Review Speed Analysis for bbartling/open-fdd

When evaluating whether to contribute to bbartling/open-fdd, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of bbartling/open-fdd acknowledge new external contributions in approximately 8 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 86.8% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to bbartling/open-fdd

01

Is bbartling/open-fdd welcoming to first-time open-source contributors?

bbartling/open-fdd has a recorded first-timer success rate of 100.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~8 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 65.0 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 65.0 places bbartling/open-fdd in the Welcoming tier.

04

What is the external contributor pull request merge rate for bbartling/open-fdd?

The external pull request merge rate is 86.8%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in bbartling/open-fdd?

Yes, bbartling/open-fdd currently has 2 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for bbartling/open-fdd are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.