flexivrobotics/flexiv_rdk - Open Source PR Review Scorecard

Robotic Development Kit (RDK) for Flexiv robots. Supports C++ and Python. Compatible with Linux, macOS, Windows, and QNX.

C-Rank Grade: D (Risky) - 17/100

External PR Merge Rate: -

Response Time: -

First Timer Success: -

Frequently Asked Questions

Is flexivrobotics/flexiv_rdk welcoming to first-time open-source contributors?

flexivrobotics/flexiv_rdk has a recorded first-timer success rate of 0.0%. Repositories ranked D 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 flexivrobotics/flexiv_rdk respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 17.0 C-Rank™ score (D 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 17.0 places flexivrobotics/flexiv_rdk in the D tier.

What is the external contributor pull request merge rate for flexivrobotics/flexiv_rdk?

The external contributor pull request merge rate for flexivrobotics/flexiv_rdk is 0.0%, based on public PR activity from non-core contributors.

Are there Good First Issues available in flexivrobotics/flexiv_rdk?

flexivrobotics/flexiv_rdk currently has 5 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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flexivrobotics/flexiv_rdk

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InactiveD•Risky(17/100)Low Sample (<20 PRs)C++

Robotic Development Kit (RDK) for Flexiv robots. Supports C++ and Python. Compatible with Linux, macOS, Windows, and QNX.

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AI Maintainer Review Guidelines

Review Persona

Active Open-Source Maintainer

Warmth Score
7.8/10
Patience Score
8.2/10
Nitpick Rate
20%

Growing C++ project in flexivrobotics/flexiv_rdk welcoming community pull requests and bug fixes.

Top PR Submission Do's

  • •Ensure code complies with C++ style conventions
  • •Keep PRs scoped and well-documented

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit unlinked PRs without context
  • •Do not break existing automated test suites
Response Velocity
-
No reviewer activity recorded

Average Response Latency

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

Merge Efficiency
0.0%
Selective PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
0.0%
No first-timer merges recorded in window

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
1 core
Single maintainer review bottleneck
Diagnostic Health HUD
0.0%
Merge Gauge
0.0%
1st-Timer
Community Vibe0/100

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Active Good First Issues (5)

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With PTP enabled, timestamps returned by Robot::states() appear approximately 4.5 ms later than the host’s PTP hardware clock read immediately afterward. Environment: Control-box software: v3.11.3 Flexiv RDK: v1.9.4 Host: Jetson AGX Orin, kernel 5.15.185-tegra LinuxPTP: 3.1.1 Direct Ethernet connection tested with GENERAL and USER_1 Robot stationary during measurement Orin’s /dev/ptp0 acts as a free-running grandmaster. No phc2sys process is running. PHC was initialized to host UTC+37 seconds before measurement; the grandmaster advertises currentUtcOffset=37, currentUtcOffsetValid=1, and ptpTimescale=1. Incoming controller Signaling messages on UDP319 were filtered because an interval request containing Announce interval 127 caused LinuxPTP to stop periodic Announce transmission. Other PTP message types remained allowed. The ptp4l configuration is: [global] time_stamping hardware network_transport UDPv4 delay_mechanism E2E domainNumber 0 utc_offset 37 free_running 1 tx_timestamp_

📅 Opened Oct 9, 2026💬 0 comments
Quality: 50/100Contribute

Hi, we are using a Rizon4 with RDK v1.9.3 and FSP v3.11.2 for visual tracking. Setup: Mode: NRT_CARTESIAN_MOTION_FORCE Command and feedback polling rate: 30 Hz Linear velocity limit: 0.01 m/s Linear acceleration limit: 0.05 m/s² Our application also applies velocity/acceleration limits to target poses before sending them. API call: robot.SendCartesianMotionForce(pose, {}, {}, 0.01, 0.1, 0.05, 0.2); Measurement: We start timing immediately before the first motion API call and stop when states().tcp_pose first shows an XY displacement of at least 1 mm from the initial position. Both timestamps use the host's monotonic clock. Across four starts: Average time to observe 1 mm displacement: 333.46 ms Range: 333.43–333.49 ms Average first API call duration: 0.059 ms The 333 ms includes acceleration, physical travel and feedback sampling; we are not treating it as pure communication latency. Questions: Is this response expected with the application-side ramp and NRT trajectory generati

📅 Opened Sep 14, 2026💬 1 comment
Quality: 90/100Contribute

Hi, I would like to clarify whether flexiv::rdk::Robot::StreamCartesianMotionForce() is officially supported on Windows without using flexiv::rdk::Scheduler. I inspected the official v1.8 Windows static library: flexiv_rdk.win_amd64.lib The library's symbol table contains a compiled definition for: flexiv::rdk::Robot::StreamCartesianMotionForce( const std::array<double, 7>& pose, const std::array<double, 6>& wrench, const std::array<double, 6>& velocity, const std::array<double, 6>& acceleration); The v1.8 robot.hpp header also declares this function without a Windows-specific guard. However, I could not find implementations of flexiv::rdk::Scheduler in the Windows library. This appears to mean that the streaming API is available, while the RDK real-time scheduler is unavailable on Windows. Could you please clarify the following? Is Robot::StreamCartesianMotionForce() officially supported on Windows in RDK v1.8? Can it be used from a user-created Windows thread or loop

📅 Opened Sep 14, 2026💬 1 comment
Quality: 90/100Contribute

Is this related to a specific version of RDK? Yes. RDK 1.9.3 with the Rizon series (software package v3.11.2). RDK 2.x requires package v3E.x and is Enlight-only, so 1.9.x is the only line available to us. What is your country or region? USA Please describe your question RDK 1.9.3 ships as a static archive only (libflexiv_rdk.x86_64-linux-gnu.a). To link it, the client application has to build and link Flexiv's entire private dependency stack. That is the wrong boundary. These are Flexiv's internal implementation choices, and the static packaging pushes all of them into our build: Fast-DDS 2.6.10, Fast-CDR 1.0.28, foonathan/memory 0.7-3, Asio RBDyn 1.9.0, SpaceVecAlg 1.2.4, jrl-cmakemodules x2, Boost 1.83.0 Eigen 3.4.0, spdlog 1.9.2, yaml-cpp 0.7.0, tinyxml2 9.0.0 That is 1.1 GB of third-party source across nine builds. Boost is 888 MB of it and is used for headers only. Our application never calls DDS, but four of those entries exist solely for Fast-DDS, and Asio is fetched only to b

📅 Opened Aug 21, 2026💬 10 comments
Quality: 65/100Contribute

Hi, We are currently using RDK v1.8. Our application is relatively simple: we execute a Plan created in Element through the RDK. We are now trying to connect a sensor to the safety input (DI 3). When the sensor is triggered, the robot enters safeguard mode. We have two questions: In the Plan execution example, busy() is used to determine whether the Plan has finished. However, when the safeguard input is triggered, the robot is paused and busy() also returns false. Is there a way to reliably distinguish between: the Plan completing normally, and the robot being paused because the safeguard input was triggered? The Rizon 4 manual mentions that the robot can automatically continue operation after the safeguard signal is cleared. How can we enable this behavior through the RDK? If possible, could you provide a minimal C++ example? Thank you for your help.

📅 Opened Aug 19, 2026💬 2 comments
Quality: 50/100Contribute
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Contributor Compatibility & Review Speed Analysis for flexivrobotics/flexiv_rdk

When evaluating whether to contribute to flexivrobotics/flexiv_rdk, 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 flexivrobotics/flexiv_rdk acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 0.0% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to flexivrobotics/flexiv_rdk

01

Is flexivrobotics/flexiv_rdk welcoming to first-time open-source contributors?

flexivrobotics/flexiv_rdk has a recorded first-timer success rate of 0.0%. Repositories ranked Risky 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 ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 17.0 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 17.0 places flexivrobotics/flexiv_rdk in the Risky tier.

04

What is the external contributor pull request merge rate for flexivrobotics/flexiv_rdk?

The external pull request merge rate is 0.0%. 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 flexivrobotics/flexiv_rdk?

Yes, flexivrobotics/flexiv_rdk currently has 5 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 flexivrobotics/flexiv_rdk 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.