LearningCircuit/local-deep-research - Open Source PR Review Scorecard

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

C-Rank Grade: A (Welcoming) - 70/100

External PR Merge Rate: 81%

Response Time: 1d

First Timer Success: 65%

Frequently Asked Questions

Is LearningCircuit/local-deep-research welcoming to first-time open-source contributors?

LearningCircuit/local-deep-research has a recorded first-timer success rate of 64.7%. 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 LearningCircuit/local-deep-research respond to incoming external pull requests in approximately 27.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 69.7 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 69.7 places LearningCircuit/local-deep-research in the A tier.

What is the external contributor pull request merge rate for LearningCircuit/local-deep-research?

The external contributor pull request merge rate for LearningCircuit/local-deep-research is 81.5%, based on public PR activity from non-core contributors.

Are there Good First Issues available in LearningCircuit/local-deep-research?

LearningCircuit/local-deep-research currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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LearningCircuit/local-deep-research

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Active (30d)A•Welcoming(70/100)Python

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

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AI Maintainer Review Guidelines

Review Persona

Strict Architecture Gatekeeper

Warmth Score
6.7/10
Patience Score
7.6/10
Nitpick Rate
40%

Strict review standards in LearningCircuit/local-deep-research. 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
1 days+
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
81.5%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
64.7%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
21 core
Highly collaborative maintainer core
Diagnostic Health HUD
81.5%
Merge Gauge
64.7%
1st-Timer
Community Vibe62/100

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Active Good First Issues (3)

View on GitHub

Tracking issue. Bundles the low-risk, no-behavior-change follow-ups from #5597 (fixes #5589) that are best done as one PR rather than separately. Both remaining items are mechanical, verified against main at 11d244e90, and touch no runtime logic. #5664 — make _validate_imported_setting_value public and update the import in api/settings_utils.py. Today the API package depends on an underscore-private symbol across a package boundary, so a rename inside settings/manager.py breaks it at import time. Mirror how DYNAMIC_SETTINGS was handled (public name, documented single source of truth, identity test). #5675 — collapse the schema-aware-import rationale comment, currently duplicated at api/settings_utils.py:223, settings/manager.py:1511, and settings/manager.py:1560. The helper's docstring already documents the trusted-vs-untrusted distinction; reduce the in-function copies to one-line pointers. #5676 — test module docstring cited brave's default_params.time_period as having gain

📅 Opened Aug 25, 2026💬 2 comments
Quality: 90/100Contribute

Follow-up to #5597 (fixes #5589). The module docstring of tests/settings/test_import_settings_validation.py:6 cites the motivating example as: a free-text search.engine.web.brave.default_params.time_period value from before the field gained an options list But that key has not gained an options list. In the current defaults: key ui_element options search.engine.web.brave.default_params.time_period text null search.engine.web.serpapi.default_params.time_period text null search.engine.web.serper.default_params.time_period select day / week / month / year / null search.engine.web.tavily.default_params.time_period select d / w / m / y Impact: a maintainer tracing the regression to the cited key finds brave's time_period is still free-text, which undercuts the documented motivation and sends reproduction effort to the wrong setting. Scope: docstring only. The tests themselves correctly exercise search.time_period and the validation logic is unaffected. Suggested fix: point

📅 Opened Aug 22, 2026💬 1 comment
Quality: 100/100Contribute

Summary The Zotero integration (#4723, #4960) works end-to-end, but parts of the page UX are still clunky — especially how collections are chosen. Plenty of self-contained, UI-only improvements here; a good place to make a first contribution. Current behavior (what feels clunky) Choosing collections is fiddly. Collections live in a plain comma-separated text field of opaque keys (e.g. W63I9K8W) inside the "Advanced options" section. The picker is a separate card at the bottom of the page: clicking a key appends it to the text field back at the top. Removing a collection means hand-editing the comma list. There are no checkboxes, no names next to the selected keys, no visual link between the picker and the field. The sync-status table shows raw collection keys, not names — "W63I9K8W" instead of "test". Users have to remember what a key means. The group picker has the same shape: a separate bottom card that silently rewrites two fields in Advanced. Feedback is split across two chann

📅 Opened Jul 4, 2026💬 5 comments
Quality: 80/100Contribute
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Contributor Compatibility & Review Speed Analysis for LearningCircuit/local-deep-research

When evaluating whether to contribute to LearningCircuit/local-deep-research, 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 LearningCircuit/local-deep-research acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 81.5% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to LearningCircuit/local-deep-research

01

Is LearningCircuit/local-deep-research welcoming to first-time open-source contributors?

LearningCircuit/local-deep-research has a recorded first-timer success rate of 64.7%. 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 ~1 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 69.7 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 69.7 places LearningCircuit/local-deep-research in the Welcoming tier.

04

What is the external contributor pull request merge rate for LearningCircuit/local-deep-research?

The external pull request merge rate is 81.5%. 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 LearningCircuit/local-deep-research?

Yes, LearningCircuit/local-deep-research currently has 1 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 LearningCircuit/local-deep-research 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.