Armur-Ai/Pentest-Swarm-AI - Open Source PR Review Scorecard

Autonomous penetration testing using a swarm of AI agents. Orchestrates recon, classification, exploitation, and reporting specialists with ReAct reasoning — supports bug bounty, continuous monitoring, and CTF modes. Built with Go and 7+ native security tools.

C-Rank Grade: B (Solid) - 57/100

External PR Merge Rate: 75%

Response Time: 3h

First Timer Success: 100%

Frequently Asked Questions

Is Armur-Ai/Pentest-Swarm-AI welcoming to first-time open-source contributors?

Armur-Ai/Pentest-Swarm-AI has a recorded first-timer success rate of 100.0%. 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 Armur-Ai/Pentest-Swarm-AI respond to incoming external pull requests in approximately 2.7 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 57.5 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 57.5 places Armur-Ai/Pentest-Swarm-AI in the B tier.

What is the external contributor pull request merge rate for Armur-Ai/Pentest-Swarm-AI?

The external contributor pull request merge rate for Armur-Ai/Pentest-Swarm-AI is 75.0%, based on public PR activity from non-core contributors.

Are there Good First Issues available in Armur-Ai/Pentest-Swarm-AI?

Armur-Ai/Pentest-Swarm-AI currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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Armur-Ai/Pentest-Swarm-AI

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Active (30d)B•Solid(57/100)Low Sample (<20 PRs)Capped at B (Bus Factor: 1)Go

Autonomous penetration testing using a swarm of AI agents. Orchestrates recon, classification, exploitation, and reporting specialists with ReAct reasoning — supports bug bounty, continuous monitoring, and CTF modes. Built with Go and 7+ native security tools.

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AI Maintainer Review Guidelines

Review Persona

Empathetic Technical Mentor

Warmth Score
7.9/10
Patience Score
8.4/10
Nitpick Rate
20%

Highly welcoming maintainers in Armur-Ai/Pentest-Swarm-AI. Prompt code reviews with positive guidance for new contributors.

What Contributors Actually Say

Discussions in Armur-Ai/Pentest-Swarm-AI 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
2 hours
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
75.0%
Moderate PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
100.0%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
CAPS TIER AT B
1 core
Single maintainer review bottleneck
Diagnostic Health HUD
75.0%
Merge Gauge
100.0%
1st-Timer
Community Vibe88/100

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Active Good First Issues (4)

View on GitHub

Newcomers keep asking "which model should I use?" Let's answer it in one page — no Go required, just clear writing. Task: write docs/models.md — a "which model?" matrix — and link it from the README's LLM Providers section. What to cover A comparison table across the providers we support, with honest trade-offs: Claude — best quality, zero setup; may refuse some offensive-security prompts OpenAI-compatible (Together AI / DeepSeek / Groq) — cheaper, great for iteration OrcaRouter — one endpoint, many frontier models Gemini Ollama / LM Studio — 100% local, free, private; needs a GPU; lower scores Security-tuned open models (e.g. Pentest-R1) — purpose-built for offensive security. Pentest Swarm is the harness for these — bring the model, we give it hands. Suggested columns: quality · cost · privacy · offensive-prompt refusal · setup effort · best-for. Acceptance docs/models.md exists with the table + a short "our recommendation" paragraph Linked from the README

📅 Opened Aug 24, 2026💬 0 comments
Quality: 50/100Contribute

feroxbuster is a fast, recursive content-discovery tool (an alternative to gobuster/ffuf). Discovered paths become endpoints on the blackboard for the classifier and exploit agents to work. Why this is a great first issue: same small adapter pattern as the rest; a clean way to learn the codebase. What to do Copy internal/tools/nikto.go as your template: FeroxbusterTool struct + NewFeroxbusterTool() + Name() → "feroxbuster" IsAvailable() → IsCommandAvailable("feroxbuster") Run(ctx, target, opts) — scope-guard, run with --json --output <tmpfile> (feroxbuster emits JSON lines), parse each into a finding (one per discovered path with status + size; info/low). Expose wordlist, threads, depth options. Register in internal/tools/coordinator.go Dockerfile install line Tests mirroring internal/tools/nikto_test.go Tick it in IMPLEMENTATION_PLAN.md Acceptance go build ./..., go vet ./..., go test ./internal/tools/... green; visible in pentestswarm doctor. Questions welcome in Disco

📅 Opened Aug 24, 2026💬 0 comments
Quality: 50/100Contribute

wafw00f detects and fingerprints Web Application Firewalls. Knowing a WAF is in front of a target changes how the swarm should probe it — a useful signal on the blackboard. Why this is a great first issue: it follows the same ~30-line adapter pattern as every other tool; no swarm internals required. What to do Copy internal/tools/nikto.go as your template: Wafw00fTool struct + NewWafw00fTool() + Name() → "wafw00f" IsAvailable() → IsCommandAvailable("wafw00f") Run(ctx, target, opts) — scope-guard, run with -f json -o <tmpfile>, parse into []map[string]any (one finding: WAF detected + vendor, or "no WAF detected"; info severity) Register in internal/tools/coordinator.go Dockerfile install line (pip: wafw00f) Tests mirroring internal/tools/nikto_test.go Tick it in IMPLEMENTATION_PLAN.md Acceptance go build ./..., go vet ./..., go test ./internal/tools/... green; visible in pentestswarm doctor. Stuck? Discord. 🐝

📅 Opened Aug 24, 2026💬 0 comments
Quality: 50/100Contribute

whatweb fingerprints web technologies — CMS, frameworks, servers, JS libraries, analytics. It's high-signal recon: the classifier agent can correlate discovered versions against CVEs. Why this is a great first issue: every tool adapter follows one small, consistent pattern (~30 lines of real logic + a parser + tests). You don't touch the swarm internals. What to do Copy internal/tools/nikto.go as your template and adapt it: WhatWebTool struct + NewWhatWebTool() + Name() → "whatweb" IsAvailable() → IsCommandAvailable("whatweb") Run(ctx, target, opts) — scope-guard with getScopeFromContext, run with --log-json=<tmpfile>, parse the JSON into []map[string]any findings (one per detected technology; info/low severity — whatweb doesn't rate risk) Register NewWhatWebTool() in internal/tools/coordinator.go Add the install line to the Dockerfile Unit tests mirroring internal/tools/nikto_test.go (Name, IsAvailable, parse-with-findings, parse-empty, parse-malformed) Tick the adap

📅 Opened Aug 24, 2026💬 0 comments
Quality: 50/100Contribute
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Contributor Compatibility & Review Speed Analysis for Armur-Ai/Pentest-Swarm-AI

When evaluating whether to contribute to Armur-Ai/Pentest-Swarm-AI, 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 Armur-Ai/Pentest-Swarm-AI acknowledge new external contributions in approximately 2 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 75.0% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to Armur-Ai/Pentest-Swarm-AI

01

Is Armur-Ai/Pentest-Swarm-AI welcoming to first-time open-source contributors?

Armur-Ai/Pentest-Swarm-AI has a recorded first-timer success rate of 100.0%. 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 ~2 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 57.5 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 57.5 places Armur-Ai/Pentest-Swarm-AI in the Solid tier.

04

What is the external contributor pull request merge rate for Armur-Ai/Pentest-Swarm-AI?

The external pull request merge rate is 75.0%. 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 Armur-Ai/Pentest-Swarm-AI?

Yes, Armur-Ai/Pentest-Swarm-AI currently has 2 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 Armur-Ai/Pentest-Swarm-AI 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.