Prescott-Data/jarviscore-framework - Open Source PR Review Scorecard

JarvisCore is a runtime where AI agents operate as a fleet of equal peers. Agents discover one another by capability, execute tasks from a shared ledger, and authenticate to every external service through a zero-trust broker, never with their own keys.

C-Rank Grade: D (Risky) - 8/100

External PR Merge Rate: -

Response Time: -

First Timer Success: -

Frequently Asked Questions

Is Prescott-Data/jarviscore-framework welcoming to first-time open-source contributors?

Prescott-Data/jarviscore-framework 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 Prescott-Data/jarviscore-framework 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 7.5 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 7.5 places Prescott-Data/jarviscore-framework in the D tier.

What is the external contributor pull request merge rate for Prescott-Data/jarviscore-framework?

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

Are there Good First Issues available in Prescott-Data/jarviscore-framework?

Prescott-Data/jarviscore-framework does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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Prescott-Data/jarviscore-frameworkD•Risky256
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Prescott-Data

Prescott-Data/jarviscore-framework

256
InactiveD•Risky(8/100)Low Sample (<20 PRs)Python

JarvisCore is a runtime where AI agents operate as a fleet of equal peers. Agents discover one another by capability, execute tasks from a shared ledger, and authenticate to every external service through a zero-trust broker, never with their own keys.

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AI Maintainer Review Guidelines

Review Persona

Empathetic Technical Mentor

Warmth Score
7.5/10
Patience Score
8.2/10
Nitpick Rate
20%

Collaborative maintainer environment in Prescott-Data/jarviscore-framework. Reviews community pull requests with focus on project quality.

What Contributors Actually Say

Discussions in Prescott-Data/jarviscore-framework focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.

Hidden Friction Signals

Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.

Unwritten Rules

  • •1) Keep PR scope strictly aligned with the linked issue.
  • •2) Ensure local linters pass before opening a review.
  • •3) Maintain full test coverage for modified logic.

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
-
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
CRITICAL BUS FACTOR
Single maintainer
No active GitHub maintainers found (may review internally on Gerrit or internal tools)
Diagnostic Health HUD
0.0%
Merge Gauge
0.0%
1st-Timer
Community Vibe0/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for Prescott-Data/jarviscore-framework
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/Prescott-Data/jarviscore-framework)](https://getmerged.abhishekco.de/Prescott-Data/jarviscore-framework?utm_source=github&utm_medium=badge)

Active Good First Issues (0)

View on GitHub

No cached good first issues currently tracked for Prescott-Data/jarviscore-framework.

View all good first issues directly on GitHub

Looking for more Python beginner tasks?Explore Python GFI

Contributor Community Vibe Feedback

Rate what actually matters after opening a pull request here.

Have you contributed to this repo?

Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.

3 ratings required
Maintainer helpfulness
Review speed
Beginner friendliness

Contributor Compatibility & Review Speed Analysis for Prescott-Data/jarviscore-framework

When evaluating whether to contribute to Prescott-Data/jarviscore-framework, 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 Prescott-Data/jarviscore-framework 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 Prescott-Data/jarviscore-framework

01

Is Prescott-Data/jarviscore-framework welcoming to first-time open-source contributors?

Prescott-Data/jarviscore-framework 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 7.5 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 7.5 places Prescott-Data/jarviscore-framework in the Risky tier.

04

What is the external contributor pull request merge rate for Prescott-Data/jarviscore-framework?

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 Prescott-Data/jarviscore-framework?

Prescott-Data/jarviscore-framework does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for Prescott-Data/jarviscore-framework 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.