pr-genius
PR Genius is an evidence-backed PR contribution advisor that helps you assess the likelihood of your pull requests being merged into large open-source projects, using 550+ real case studies, 68 anti-patterns, and structured maintainer policy data.
analyze_pr— Deep analysis of a proposed PR, returning a 3-tier risk level (low/medium/high), positive/negative/neutral signals, a prioritized checklist (P0/P1/P2 items), and a recommended action to maximize merge probability.coach_pr— Quick go/no-go pass/fail decision before opening a PR, combining repo profile analysis, anti-pattern matching, and maintainer policy checks into a single verdict.triage_pr— Screen a PR against repo-specific maintainer policies (hard/soft rules), returning a verdict of pass, needs_preflight, warn, or reject, along with any policy violations and evidence.get_repo_profile— Retrieve a detailed 17-field repository profile (AI policy, maintainer vibe, external PR merge rate, close keywords, etc.) to understand a repo's contribution culture before submitting.list_open_prs— Browse all open PR case studies in the knowledge base and their current states.get_case_study— Fetch full details of a specific PR case study, including round-by-round interaction history, to learn from past outcomes.search_patterns— Search anti-patterns and success-patterns by keyword to discover what behaviors lead to PR rejection or acceptance.schema_info— Retrieve supported OKF schema versions and enumeration values used by the server.
Provides tools to analyze pull requests on GitHub repositories, offering merge probability estimation, policy triage, and pattern-based coaching using historical case studies.
type: Knowledge Bundle title: PR Genius — Pre-submission PR Advisor description: Evidence-backed PR contribution advisor for large open-source projects version: 1.4.0 created: 2026-07-01 updated: 2026-07-22 author: zsxh1990 conforms_to: OKF v0.1 (Sudhakaran88/okf-conformance) + agent_guidelines extension
mcp-name: io.github.zsxh1990/pr-genius
PR Genius — The advisor that knows which PRs get closed
1355 loaded patterns across 61 repos. 100% quality pass rate. Clone → paste MCP config → ask "Should I open this PR to encode/httpx?"
Related MCP server: devflow-mcp
🎯 What is PR Genius?
PR Genius is not a PR dashboard. It's an Outbound PR CRM for professional OSS contributors and AI agents:
Manage PRs you've submitted to other repos — when to fix CI, rebase, wait, ping, or abandon.
Capability |
| PR Genius |
Cross-repo PR list | ✅ | ✅ |
Status classification | ❌ | ✅ (9 states) |
Stale detection | ❌ | ✅ |
Action suggestions | ❌ | ✅ |
Repo-specific policy | ❌ | ✅ |
Snapshot & transitions | ❌ | ✅ |
Status heartbeat runs daily via cron, auto-detecting:
🔴
NEEDS_REBASE/CI_FAILING— fix immediately🟡
STALE_REVIEW— ping after threshold🟡
STALE_NO_REVIEW— consider abandoning🟢
CLEAN/WAITING— continue waiting
🛡️ Why PR Genius?
PR Genius doesn't write PRs for you. It knows which PRs get closed.
Capability | LLM directly | Scraper Agent | PR Genius |
Knowledge source | Training data | Real-time scrape | 1355 structured patterns |
Repo understanding | Generic | Surface data (stars) | 17-field agent_guidelines |
Failure patterns | Unknown | Unknown | 752 anti-patterns |
Success patterns | Unknown | Unknown | 703 success patterns |
Maintainer preference | Guess | Recent PRs | Structured policy files |
Merge probability | Can't estimate | Can't estimate | Based on repo merge rate + signals |
Real cases (PR Genius helped avoid these rejections):
PR | Repo | What happened | PR Genius would have flagged |
#491 | MisakaNet | "Destructive README rewrite" — closed |
|
#47434 | huggingface/transformers | "We'll handle internally" — closed |
|
#10393 | awesome-mcp-servers | Missing Glama badge — auto-flagged |
|
#282 | punkpeye/fastmcp | +271 lines, first PR — closed without review |
|
#2902 | soxoj/maigret | CI failure (tag |
|
🚀 Quick Start
pip install prgenius-core
# Analyze PR
python3 -m prgenius analyze "feat: add feature" --repo org/repo --body "Fixes #123"
# Coach (pass/fail)
python3 -m prgenius coach "feat: add feature" --repo org/repo
# Triage (policy check)
python3 -m prgenius triage "docs: typo" --repo org/repo --diff-stat "docs/faq.md | 3 ++-"
# Status heartbeat (outbound PR monitoring)
python3 -m prgenius status --author zsxh1990
python3 -m prgenius status --author zsxh1990 --format json --save-snapshot
# Profile writeback suggestions (dry-run)
python3 -m prgenius profile writeback --author zsxh1990🤖 GitHub Action
Use PR Genius as a GitHub Action in any repo:
# .github/workflows/pr-genius.yml
name: PR Genius Check
on:
pull_request:
types: [opened, synchronize]
permissions:
contents: read
issues: write # required when comment_mode != never (post/update PR comment)
jobs:
pr-genius:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: zsxh1990/pr-genius/.github/actions/pr-genius-check@v1
id: pr-genius
with:
title: ${{ github.event.pull_request.title }}
repo: ${{ github.repository }}
body: ${{ github.event.pull_request.body }}
pr_number: ${{ github.event.pull_request.number }}
comment_mode: always # never | high_risk | always — post full analysis as a PR commentcomment_mode controls whether the full analysis is posted as a visible PR
comment (mirrors pr-agent's /review):
never— do not post (default when unset andcomment_on_high_riskis false)high_risk— post only when the risk tier ishigh_riskalways— post on every run; existing comments are updated in place (no spam)
Legacy:
comment_on_high_risk: trueis still supported and behaves likecomment_mode: high_risk.
Version Auto-Update
@v1— Always points to the latestv1.x.xrelease (recommended)@v1.7.1— Pinned to specific version (for reproducibility)@main— Latest development version (not recommended for production)
The v1 tag is automatically updated when a new version is published to PyPI.
Docker Image (Alternative)
Use PR Genius as a Docker container via GitHub Container Registry:
# .github/workflows/pr-genius-docker.yml
name: PR Genius Check (Docker)
on:
pull_request:
types: [opened, synchronize]
permissions:
contents: read
pull-requests: write
jobs:
pr-genius:
runs-on: ubuntu-latest
steps:
- name: Run PR Genius
uses: docker://ghcr.io/zsxh1990/pr-genius:latest
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
args: coach "${{ github.event.pull_request.title }}" --repo ${{ github.repository }} --format jsonDocker Image Tags:
ghcr.io/zsxh1990/pr-genius:latest— Latest releaseghcr.io/zsxh1990/pr-genius:1.7.2— Specific versionghcr.io/zsxh1990/pr-genius:1.7— Minor versionghcr.io/zsxh1990/pr-genius:1— Major version (auto-updated)
Auto-update with Dependabot:
# .github/dependabot.yml
version: 2
updates:
- package-ecosystem: "github-actions"
directory: "/"
schedule:
interval: "weekly"
- package-ecosystem: "docker"
directory: "/"
schedule:
interval: "weekly"🤖 MCP Configuration
{
"mcpServers": {
"pr-genius": {
"command": "python",
"args": ["-m", "prgenius", "mcp", "serve"]
}
}
}Docker: docker run --rm -i ghcr.io/zsxh1990/pr-genius:1.3.0
8 MCP Tools
Tool | Purpose | Required Args |
| Merge probability + optimization path + 3-tier risk |
|
| Go/no-go decision (pass/fail) |
|
| Maintainer policy check (9 rules) |
|
| Repo profile (17 fields) |
|
| Open PR list |
|
| PR case study details |
|
| Anti-pattern/success-pattern search |
|
| OKF schema versions | (none) |
Tool Parameter Notes
title(required foranalyze_pr,coach_pr,triage_pr): The PR title, e.g."fix: timeout in connection pool"repo(required for most tools): Repository inowner/nameformat, e.g."encode/httpx"pr_description(optional): Additional PR body text for deeper analysisquery(required forsearch_patterns): Search keywords, e.g."connection timeout"
📊 Data Scale
Dimension | Count |
Repo profiles | 61 |
Case studies | 50+ |
Success patterns | 687 (431 .md + 256 .json) |
Anti-patterns | 668 (561 .md + 107 .json) |
Total patterns | 1355 (all loaded) |
Quality pass rate | 100% (994/994 markdown ≥75分) |
Covered repos | 35+ (react, kubernetes, rust, uv, pydantic, etc.) |
按仓库规模分布
规模 | Success | Anti | 总计 |
大仓 (>10k ⭐) | 208 | 205 | 413 |
中仓 (1k-10k ⭐) | 169 | 88 | 257 |
小仓 (<1k ⭐) | 169 | 88 | 257 |
通用 | 203 | 414 | 617 |
其他 (特定仓库) | 312 | 260 | 572 |
🤖 Robots / Agents
docs/index.md — file map
AGENT_GUIDELINES_SCHEMA.md — agent_guidelines schema
ROUNDS_SCHEMA.md — rounds schema
BLACKLIST.md — repos we don't track
📖 Contributing
See CONTRIBUTING.md. AI-assisted PRs welcome.
🤝 Community
中文文档
中文版 README:README.zh-CN.md
Citation
@misc{pr-genius-2026,
title = {PR Genius — Evidence-backed PR Contribution Advisor},
author = {zsxh1990},
year = {2026},
url = {https://github.com/zsxh1990/pr-genius}
}Available Tools
3 toolsget_case_studyARead-onlyIdempotent
Get a specific PR case study with full details and rounds.
Returns the complete case study including frontmatter, body text, and round-by-round interaction history. Useful for learning from past PR experiences.
Args: repo: Repository in org/name format pr_number: PR number (integer)
Returns: dict with frontmatter, body, path, or error if not found
| Name | Required | Description | Default |
|---|---|---|---|
| repo | Yes | ||
| pr_number | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. The description adds details about the return structure (frontmatter, body, path) and error handling, which are beyond the annotations and schema. It is sufficient for a simple retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a one-sentence purpose, a brief context statement, and clearly separated Args/Returns sections. Every sentence adds value, and the structure is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 2 simple parameters and no output schema, the description covers the core functionality and return format. It mentions error handling ('or error if not found'). An example would be nice but is not essential given the simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no descriptions (0% coverage), so the description compensates by explaining 'repo: Repository in org/name format' and 'pr_number: PR number (integer)'. This adds meaningful guidance beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves a specific PR case study with full details and rounds, distinguishing it from sibling tools like list_open_prs (which lists PRs) and search_patterns (which searches patterns). The verb 'get' and resource 'case study' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates it is useful for learning from past PR experiences, providing context for when to use. While it lacks explicit when-not-to-use or alternative tools, the purpose is clear enough to differentiate from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_open_prsARead-onlyIdempotent
List all open PR case studies in the knowledge base.
Returns PRs with final_status=open, useful for tracking ongoing contributions and their current state.
Returns: list of {repo, pr_number, pr_url, folder} for each open PR
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description adds only the output format (list of objects with repo, pr_number, etc.). No additional behavioral traits beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with two short paragraphs. The main purpose is front-loaded, and the return format is clearly listed. No unnecessary sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters, explicit annotations, and simple output, the description is complete. It specifies the output fields and purpose sufficiently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, and schema coverage is 100%. The description does not need to add parameter info. Baseline score of 4 for zero parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states what the tool does: lists all open PR case studies. It specifies the filtering condition (final_status=open) and the output fields. It is distinct from siblings (get_case_study, search_patterns) by focusing on listing open PRs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it is useful for tracking ongoing contributions, but does not explicitly state when not to use it or provide alternatives. Usage is implied rather than explicitly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_patternsARead-onlyIdempotent
Search anti-patterns and success-patterns by keyword.
Use this to find relevant patterns before submitting a PR. Anti-patterns show what NOT to do, success-patterns show what works.
Args: query: Search keyword (e.g. "duplicate PR", "missing tests", "out of scope", "breaking change") pattern_type: Filter type — "all" (default), "anti-pattern", or "success-pattern" limit: Max results to return (default 10)
Returns: list of dicts with keys: key, title, symptom, fix_action, source_pr, type, file
Example: search_patterns("duplicate PR") → finds anti-patterns about duplicate submissions search_patterns("timeout", "anti-pattern") → finds timeout-related failure patterns
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| pattern_type | No | all |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds behavioral context (search, filter, return format) but doesn't introduce new traits beyond what annotations imply.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for purpose, usage, args, returns, and examples. It is efficient, though slightly verbose with the example lines; still, every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the return format (list of dicts with specific keys) and provides examples. The tool's complexity is low, and the description is adequate for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, the description fully explains each parameter with examples: query (with keyword examples), pattern_type (options: all, anti-pattern, success-pattern), limit (default 10). This adds significant meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search anti-patterns and success-patterns by keyword,' providing a specific verb and resource. It distinguishes itself from sibling tools like list_open_prs and get_case_study by focusing on patterns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use this to find relevant patterns before submitting a PR,' giving clear context of when to use. It also clarifies the two types of patterns but doesn't explicitly state when not to use, though the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
v1.3.0- Removed
get_repo_profile - Removed
schema_info
8 tool updates
v1.3.1- Removed
analyze_pr - Removed
coach_pr - Added
get_case_study - Added
get_repo_profile - Added
list_open_prs - Added
schema_info - Added
search_patterns - Removed
triage_pr
3 tool updates
v0.1.0- First observed
analyze_pr - First observed
coach_pr - First observed
triage_pr
TDQS
Each tool has a clearly distinct purpose: listing open PRs, fetching a case study by repo and number, and searching patterns by keyword. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern in snake_case: list_open_prs, get_case_study, search_patterns. No deviations or mixed conventions.
With only 3 tools, the set is well-scoped for a read-only PR case study and pattern knowledge base. Each tool serves a necessary function without redundancy.
The tools cover listing open PRs, retrieving details, and searching patterns, but lack the ability to list all case studies (including closed ones) or browse patterns comprehensively without a keyword. Minor gaps exist.
Maintenance
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