helixnow/deep-student - Open Source PR Review Scorecard

An open-source, local-first AI learning workbench

C-Rank Grade: B (Solid) - 51/100

External PR Merge Rate: 30%

Response Time: 5d

First Timer Success: 67%

Frequently Asked Questions

Is helixnow/deep-student welcoming to first-time open-source contributors?

helixnow/deep-student has a recorded first-timer success rate of 66.7%. Repositories ranked B 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 helixnow/deep-student respond to incoming external pull requests in approximately 125.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 50.9 C-Rank™ score (B 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 50.9 places helixnow/deep-student in the B tier.

What is the external contributor pull request merge rate for helixnow/deep-student?

The external contributor pull request merge rate for helixnow/deep-student is 30.5%, based on public PR activity from non-core contributors.

Are there Good First Issues available in helixnow/deep-student?

helixnow/deep-student does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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helixnow/deep-studentB•Solid278
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helixnow/deep-student

278
Active (30d)B•Solid(51/100)TypeScript

An open-source, local-first AI learning workbench

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AI Maintainer Review Guidelines

Review Persona

Strict Architecture Gatekeeper

Warmth Score
7.2/10
Patience Score
8.0/10
Nitpick Rate
25%

Strict review standards in helixnow/deep-student. 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
5 days+
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
30.5%
Moderate PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
66.7%
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
30.5%
Merge Gauge
66.7%
1st-Timer
Community Vibe32/100

Embed C-Rank Badge

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

GetMerged C-Rank badge for helixnow/deep-student
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/helixnow/deep-student)](https://getmerged.abhishekco.de/helixnow/deep-student?utm_source=github&utm_medium=badge)

Active Good First Issues (1)

View on GitHub

用户画像记忆未能在普通问题中自动生效,导致回答超出用户当前阶段 问题描述 当用户提问时,如果问题中没有明确提及"根据我的阶段"等字眼,AI 倾向于直接回答,而忽略用户画像记忆,导致给出的答案超用户当前学习阶段。 对比测试 场景 A:明确提及人设 用户:线性回归是什么 根据我当前阶段 给我详解 AI:✅ 正确调用 `memory_search` → 读取用户画像 → 给出适合当前阶段的解释 场景 B:普通提问 用户:线性回归是什么? AI:不调用工具,直接回答,内容超纲 期望行为 无论用户问题是否明确提及人设,AI 都应该: 自动检索用户画像记忆 优先根据用户阶段调整回答深度 如知识库内容超出用户阶段,先说明用户基础再决定是否展开 建议修复方案 方案 1:增强技能提示(推荐,改动最小) 修改 dstu-memory-orchestrator.ts 的检索策略部分:

📅 Opened Jun 2, 2026💬 1 comment
Quality: 80/100Contribute
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Contributor Community Vibe Feedback

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Contributor Compatibility & Review Speed Analysis for helixnow/deep-student

When evaluating whether to contribute to helixnow/deep-student, 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 helixnow/deep-student acknowledge new external contributions in approximately 5 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 30.5% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to helixnow/deep-student

01

Is helixnow/deep-student welcoming to first-time open-source contributors?

helixnow/deep-student has a recorded first-timer success rate of 66.7%. Repositories ranked Solid 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 ~5 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 50.9 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 50.9 places helixnow/deep-student in the Solid tier.

04

What is the external contributor pull request merge rate for helixnow/deep-student?

The external pull request merge rate is 30.5%. 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 helixnow/deep-student?

helixnow/deep-student 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 helixnow/deep-student 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.