apache/maka - Open Source PR Review Scorecard

Apache Maka (Incubating) is a high-performance agent workspace that keeps a complete record of everything it did.

C-Rank Grade: A (Welcoming) - 66/100

External PR Merge Rate: 14%

Response Time: -

First Timer Success: 7%

Frequently Asked Questions

Is apache/maka welcoming to first-time open-source contributors?

apache/maka has a recorded first-timer success rate of 6.9%. 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 apache/maka 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 66.3 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 66.3 places apache/maka in the A tier.

What is the external contributor pull request merge rate for apache/maka?

The external contributor pull request merge rate for apache/maka is 13.6%, based on public PR activity from non-core contributors.

Are there Good First Issues available in apache/maka?

apache/maka currently has 40 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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Apache Maka (Incubating) is a high-performance agent workspace that keeps a complete record of everything it did.

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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%

Active maintainer team in apache/maka welcoming community pull requests and bug fixes.

Top PR Submission Do's

  • •Ensure code complies with repository style conventions
  • •Keep PRs focused 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
13.6%
Selective PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
6.9%
Accepts new contributor PRs

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
99 core
Highly collaborative maintainer core
Diagnostic Health HUD
13.6%
Merge Gauge
6.9%
1st-Timer
Community Vibe8/100

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Active Good First Issues (5)

View on GitHub

Removing a key from an exact object shape silently makes every stored record carrying that key undecodable. This is not hypothetical — it shipped in #4879 and was caught in review: dropping partialOutputRetained from TURN_STATE_MESSAGE_SHAPE made 67 of 67 turn_state rows in a real user store fail decodeMessage, which would have left those Sessions unopenable after upgrade. Fixed in 3a4371a74 by adding a retired list to defineObjectShape. The invariant, stated once: an exact shape carries two contracts whose variance is opposite — what it emits may shrink freely, what it accepts may only grow. allowed expressed both as one set, so a type-level field deletion was silently translated into a persisted-data-level deletion. Why the type system made it worse rather than better: Covers<Expected, Actual> requires the optional list to exactly cover the type's optional keys, so once the field left TurnStateMessage, keeping the key in optional was a compile error. The compiler pushed toward th

📅 Opened Sep 7, 2026💬 4 comments
Quality: 50/100Contribute

What happened The Desktop E2E test a nested scroller near the history boundary does not request an earlier range (apps/desktop/e2e/transcript-scroll.spec.ts:498) fails intermittently on CI, before it exercises anything it is about. The test resizes the viewport, waits six painted frames, and then asserts its two preconditions in one shot: await page.setViewportSize({ width: 900, height: 1500 }); await waitForPaintedFrames(page, 6); const metrics = await scrollMetrics(page); expect(metrics.scrollTop).toBeLessThanOrEqual(Math.max(640, metrics.clientHeight * 2)); expect(metrics.distance).toBeLessThanOrEqual(4); // <- fails here Observed on CI: Error: expect(received).toBeLessThanOrEqual(expected) Expected: <= 4 Received: 58 at apps/desktop/e2e/transcript-scroll.spec.ts:505:28 A viewport change makes the transcript re-pin to the tail. Six painted frames is a guess at how long that takes, not a wait for it, so under CI load the assertion samples the scroller mid-pin and reads a dist

📅 Opened Sep 3, 2026💬 0 comments
Quality: 75/100Contribute

Last piece of #4283. Everything else that issue asked for is on main; this is the one lossy edit to model-visible history that still lives outside the ledger. Problem When a provider rejects a mid-turn request as too long, the runtime drops historical image Tool Results from the request and retries once. The set it dropped is kept only in memory: packages/runtime/src/ai-sdk-compaction.ts: the ContextLength branch builds state.omittedImageToolResults on the mid-turn state, and projectHistoricalImageOmissions re-applies that map to every later request in the same turn. packages/runtime/src/provider-image-overflow-recovery.ts: collectHistoricalImageToolResults and omitHistoricalImageToolResults do the selection and the rewrite. Nothing about the omission is written to the operational ledger. On the next Turn, after a restart, or on a branch, the effective-history reducer rebuilds from the durable projections, the images come back, and the request either overflows again (one more rejecte

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

Problem A committed Side Conversation inherits the parent Session's transcript so the user can ask an auxiliary question against a hot context without paying for it twice. On the wire, provider prompt caching (Anthropic's request-level cache_control; OpenAI's automatic prefix caching) is what makes that inheritance nearly free: the fork's first request should match the parent's cached prefix and hit. It cannot. The side-conversation boundary instructions are joined into the single system prompt string, so the fork's system block differs from the parent's at the first divergence point — before the inherited history. Prefix matching stops at the system block and the entire inherited transcript is re-read at full input price on the fork's first request. The feature's own economic premise (cheap context reuse) is defeated by its own implementation. Mechanism Fork creation copies the parent transcript and pins the same connection + model (session-revision-coordinator.ts, #createInput), b

📅 Opened Sep 2, 2026💬 2 comments
Quality: 50/100Contribute

What happens Open Settings → a provider connection whose provider serves a model that models.dev has started listing since this Maka build was cut. The connection detail shows that model a row asking the user to declare its capabilities by hand — reasoning, vision, thinking levels — even though the Runtime Host already knows all of them and is describing the model correctly everywhere else in the app. Why installRefreshedModelMetadata() swaps a module-level variable in the process that calls it (packages/core/src/model-metadata.ts:71). The Runtime Host calls it at startup, so the Host's activeMetadata() is the refreshed catalog. The Desktop renderer is a different process — often on a different machine — and its own activeMetadata() is, and always will be, the snapshot compiled into that build. That is by design: since #4411 clients are supposed to read Host-resolved catalogEntries rather than resolve a catalog themselves. apps/desktop/src/renderer/settings/provider-connectio

📅 Opened Sep 1, 2026💬 1 comment
Quality: 90/100Contribute
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Contributor Compatibility & Review Speed Analysis for apache/maka

When evaluating whether to contribute to apache/maka, 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 apache/maka acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 13.6% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to apache/maka

01

Is apache/maka welcoming to first-time open-source contributors?

apache/maka has a recorded first-timer success rate of 6.9%. 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 ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 66.3 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 66.3 places apache/maka in the Welcoming tier.

04

What is the external contributor pull request merge rate for apache/maka?

The external pull request merge rate is 13.6%. 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 apache/maka?

Yes, apache/maka currently has 40 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 apache/maka 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.