verifywise-ai/verifywise - Open Source PR Review Scorecard

Complete AI governance and LLM Evals platform with support for EU AI Act, ISO 42001, NIST AI RMF and 20+ more AI frameworks and regulations. Join our Discord channel: https://discord.com/invite/d3k3E4uEpR

C-Rank Grade: A (Welcoming) - 68/100

External PR Merge Rate: 71%

Response Time: 2d

First Timer Success: 83%

Frequently Asked Questions

Is verifywise-ai/verifywise welcoming to first-time open-source contributors?

verifywise-ai/verifywise has a recorded first-timer success rate of 83.3%. 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 verifywise-ai/verifywise respond to incoming external pull requests in approximately 40.5 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 67.5 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 67.5 places verifywise-ai/verifywise in the A tier.

What is the external contributor pull request merge rate for verifywise-ai/verifywise?

The external contributor pull request merge rate for verifywise-ai/verifywise is 71.2%, based on public PR activity from non-core contributors.

Are there Good First Issues available in verifywise-ai/verifywise?

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

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verifywise-ai/verifywise

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Active (30d)A•Welcoming(68/100)TypeScript

Complete AI governance and LLM Evals platform with support for EU AI Act, ISO 42001, NIST AI RMF and 20+ more AI frameworks and regulations. Join our Discord channel: https://discord.com/invite/d3k3E4uEpR

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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 verifywise-ai/verifywise 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
1 days+
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
71.2%
Moderate PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
83.3%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
5 core
Small core review team
Diagnostic Health HUD
71.2%
Merge Gauge
83.3%
1st-Timer
Community Vibe53/100

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Active Good First Issues (5)

View on GitHub

Summary There is no dedicated intended use / purpose field on a model. The intended purpose of an AI model/solution is a standard governance data point (it frames how the model should and shouldn't be used), but today it can only be squeezed into free-text capabilities / limitations or the use-case goal/description, none of which is a distinctly-labeled "intended purpose" field. Current state model_inventories (Servers/domain.layer/models/modelInventory/modelInventory.model.ts) has capabilities, limitations, biases (all STRING) but no intended-use/purpose field. projects (use case) has goal and description (both required STRING) — general-purpose text, not a labeled intended-purpose statement. Proposed change Add a nullable intended_use TEXT column, following the established add-column pattern: Migration: ALTER TABLE verifywise.model_inventories ADD COLUMN intended_use TEXT; (multi-tenant verifywise. prefix per existing convention). Model: @Column({ type: DataType.TEXT, allowNull:

📅 Opened Sep 1, 2026💬 1 comment
Quality: 50/100Contribute

Summary AI incident status only supports the linear lifecycle Open → Investigating → Mitigated → Closed. There's no way to express that a system has been suspended or that an emergency action is in effect while an incident is active. Adding these states lets responders reflect real containment actions on the incident record. Current state AIIncidentManagementStatus (Servers/domain.layer/enums/ai-incident-management.enum.ts): export enum AIIncidentManagementStatus { OPEN = "Open", INVESTIGATING = "Investigating", MITIGATED = "Mitigated", CLOSED = "Closed", } The backing column is a plain VARCHAR(20) on ai_incident_managements (status VARCHAR(20) DEFAULT 'Open', migration 20260226234302-tenant-tables.js) — not a Postgres enum type. So new values do not require an ALTER TYPE migration. Proposed change Add value(s) to the TS enum, e.g.: SUSPENDED = "Suspended", EMERGENCY_ACTION = "Emergency action", (Exact wording/set open to discussion.) Update the incident status dropdow

📅 Opened Sep 1, 2026💬 1 comment
Quality: 50/100Contribute

Summary The model inventory has no field describing the architecture / solution type of a registered model or solution — e.g. traditional ML, GenAI, RAG, or Agentic AI. This classification is a common governance need: it drives how a model is reviewed and which controls/assessments are relevant, and it's a natural filter/grouping dimension in the inventory. Current state model_inventories (Servers/domain.layer/models/modelInventory/modelInventory.model.ts) has no type, category, model_type, or equivalent column. Confirmed columns today: id, provider_model, provider, model, version, approver, capabilities, security_assessment, status, status_date, reference_link, biases, limitations, hosting_provider, security_assessment_data, is_demo, external_key, mrm_tier, mrm_materiality_drivers, mrm_tiered_at, mrm_tiered_by, mrm_revalidation_flagged, mrm_revalidation_flagged_at, mrm_revalidation_reason, created_at, updated_at. Proposed change New enum, following the one-file-per-enum convention

📅 Opened Sep 1, 2026💬 0 comments
Quality: 50/100Contribute

Summary Model inventory tracks a model's operational status but has no terminal Retired state. A model that has been decommissioned / taken out of service can only be marked Restricted, Blocked, or Rejected — none of which correctly express "this model has reached end-of-life and is retired." Adding a Retired status closes the model lifecycle (initiation → operation → retirement). Current state ModelInventoryStatus today (Servers/domain.layer/enums/model-inventory-status.enum.ts): export enum ModelInventoryStatus { APPROVED = "Approved", RESTRICTED = "Restricted", PENDING = "Pending", BLOCKED = "Blocked", REJECTED = "Rejected", } The status column on model_inventories uses the Postgres enum type verifywise.enum_model_inventories_status (created in migration 20260226234300-base-enums-and-roles.js), defaulting to Pending. Proposed change Add RETIRED = "Retired" to ModelInventoryStatus. New migration to extend the DB enum: ALTER TYPE verifywise.enum_model_inventories_stat

📅 Opened Sep 1, 2026💬 0 comments
Quality: 50/100Contribute

What happened? Check deployment documentation for any gaps. Deployment should be fully repeatable and executable end-to-end without blockers or undocumented manual steps. Sub-tasks Go over deployment phases — Reproduce the deployment from scratch, validate the build, CI/CD pipeline, environment variables, infra dependencies, and post-deploy verification. Check deployment documentation — Compare documented steps vs actual steps, update the deployment guide, and remove anything outdated or missing. Definition of Done Deployment runs end-to-end without blockers Documentation matches the actual flow Verified in a clean environment

📅 Opened Feb 27, 2026💬 1 comment
Quality: 70/100Contribute
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Contributor Compatibility & Review Speed Analysis for verifywise-ai/verifywise

When evaluating whether to contribute to verifywise-ai/verifywise, 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 verifywise-ai/verifywise acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 71.2% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to verifywise-ai/verifywise

01

Is verifywise-ai/verifywise welcoming to first-time open-source contributors?

verifywise-ai/verifywise has a recorded first-timer success rate of 83.3%. 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 67.5 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 67.5 places verifywise-ai/verifywise in the Welcoming tier.

04

What is the external contributor pull request merge rate for verifywise-ai/verifywise?

The external pull request merge rate is 71.2%. 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 verifywise-ai/verifywise?

verifywise-ai/verifywise 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 verifywise-ai/verifywise 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.