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contributing

Contribute to the RubyLLM AI framework - set up the repo, run and record specs, work on conversations, individual AI operations, providers, protocols, Rails integration, and docs. Use when fixing a bug, building a feature, writing specs, or changing documentation in the RubyLLM c

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价格未确认★ 4,365 GitHub Stars目录更新于 · 2026年9月15日agent-skill

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Contributing to RubyLLM

Read AGENTS.md at the repo root first: it has the ground rules, the command table, and the architecture constraints that archspec check enforces. This skill adds the step-by-step recipes.

Choose the layer

The public API covers conversations (Chat, Message, Tool, Agent, structured output, streaming, and loop control) and individual operations (paint, animate, speak, transcribe, ocr, moderate, embed, and rerank). Both connect to services through providers and protocols: providers supply endpoints, authentication, catalogs, and protocol selection; protocols implement formats and their dialects.

Model resolution, configuration, accounting, instrumentation, batches, and provider resources support the framework as a whole. Keep independent operations independent of Chat. Rails integration adds Active Record, Active Storage, Hotwire, jobs, and generators around the same Ruby API; it does not define a second conversation API.

The fast loop

bundle exec rspec --tag ~live          # unit tests only, no keys, seconds not minutes
bundle exec rspec spec/ruby_llm/chat_spec.rb:42   # one example
overcommit --run                       # what the commit hook will run

Run the fast loop while developing. Run overcommit --run before declaring anything done: it runs RuboCop (auto-correct), Flay (duplication), archspec (architecture), and the unit suite, and the commit fails if any of them do.

Working with cassettes

Specs tagged :live replay recorded provider traffic from spec/fixtures/vcr_cassettes. The cassette name derives from the example's full description, so renaming an example orphans its cassette.

To re-record after changing request shapes:

rake vcr:record[openai]            # deletes that provider's cassettes, reruns the suite
rake vcr:record[openai,anthropic]  # several providers
rake vcr:record[all]               # everything (long, needs many keys)

Recording needs real API keys in .env. To re-record a single spec, delete its cassette file and run the spec with the provider's key set.

Rules that bite:

A failing :live example deletes its own cassette on purpose. Do not "fix" a red spec by restoring the cassette; fix the code, then re-record.
Review every new or changed cassette for leaked keys and personal data before committing. The pre-commit hook runs gitleaks, but eyes first.
A spec that can neither replay (no cassette) nor record (no key) skips with a message naming the env var it needs. That is expected on a fork without keys.

Adding or changing a chat option

Add with_x to lib/ruby_llm/chat.rb (chainable, returns self).
Add the matching bare x class macro to lib/ruby_llm/agent.rb. The archspec build fails without it.
Thread the option through the Provider contract, never by referencing a concrete provider or protocol from Chat.
Implement per protocol in lib/ruby_llm/protocols/* (render_* for request payloads, parse_* for responses).
Spec it in spec/ruby_llm/chat_<x>_spec.rb; add live coverage over the matrix in spec/support/models_to_test.rb when providers differ.
Document it on the matching page in docs/_core_features/.

Working on an individual AI operation

Start with the public operation and its typed result. Keep the shared RubyLLM names consistent across providers.
Route service calls through the Provider contract and registered protocols. Keep request rendering, response parsing, and format quirks in protocols.
Reuse model resolution, configuration, usage accounting, and instrumentation where applicable. Preserve the operation's streaming or job lifecycle.
Test public behavior and protocol translation at their respective layers. Document the operation in its feature guide and keep its Getting Started example short.

Adding a provider

For smaller or emerging providers, ship a community gem instead of a core PR (the core bar is high, see CONTRIBUTING.md):

bundle exec ruby_llm provider-gem Acme --api-base https://api.acme.ai/v1

That scaffolds a complete gem with specs and CI. For an approved core provider:

script/generate-provider acme

Then make it real:

lib/ruby_llm/providers/acme.rb declares auth, API base, and which protocols it speaks (protocol :chat_completions, ...). If the provider has its own wire format, that format is a new protocol under lib/ruby_llm/protocols/, not code inside the provider.
Replace the scaffold's example capabilities with the provider's real ones, and identify the model-catalog source.
Register the provider in lib/ruby_llm.rb (the entrypoint is the only place concrete providers get wired in).
Record cassettes covering normal chat and streaming chat at minimum.
Document configuration in docs/_getting_started/configuration-providers.md.

Rails work

Rails specs run against the dummy app in spec/dummy; acts_as_chat and acts_as_message live in lib/ruby_llm/active_record/.
The Rails integration builds on the domain layer and the Provider contract only. It converts records with to_llm/from_llm; plain-Ruby objects never define those.
Preserve the Ruby conversation API on records, Active Storage attachment support, and the persisted message lifecycle used by Hotwire streaming and background jobs. Individual operations remain callable directly from Rails services and jobs.
Generators live in lib/generators/ruby_llm/ (install, upgrade, chat_ui, agent, tool, schema, provider). Their specs are tagged :generator and excluded from the pre-commit run; run them explicitly with bundle exec rspec --tag generator.
Check Rails-version compatibility across the matrix: bundle exec appraisal rails-7.1 rspec through rails-8.1.

Docs work

Pages live in docs/ under _getting_started, _core_features, _advanced, and _reference. Preview with docs/bin/serve.sh.
Voice: Rails guides. Second person, present tense, show the code before explaining it, motivate each feature with the problem it solves in one sentence. No em dashes, no hype, no "simply".
Lead with short, working public API examples. Show how features combine when it helps the reader build something. Keep the title, description, and "After reading this guide" opening; introduce concepts before adding a summary table.
Front-matter description becomes the page's llms.txt entry and social card text: one compelling sentence, no &, <, or >.
Cross-link with {% link _collection/page.md %}, never hard-coded URLs.
docs/_reference/available-models.md is generated; never edit it.

Before opening the PR

overcommit --run passes.
New behavior has specs; changed provider behavior has re-recorded cassettes, reviewed for secrets.
Public API changes are documented in docs/ and have RDoc.
The PR does one thing, references its approved issue, and explains the problem before the solution.

查看来源 ↗

文件元数据

name: contributing
description: Contribute to the RubyLLM AI framework - set up the repo, run and record specs, work on conversations, individual AI operations, providers, protocols, Rails integration, and docs. Use when fixing a bug, building a feature, writing specs, or changing documentation in the RubyLLM codebase.

查看原始文本

---
name: contributing
description: Contribute to the RubyLLM AI framework - set up the repo, run and record specs, work on conversations, individual AI operations, providers, protocols, Rails integration, and docs. Use when fixing a bug, building a feature, writing specs, or changing documentation in the RubyLLM codebase.
---

# Contributing to RubyLLM

Read AGENTS.md at the repo root first: it has the ground rules, the command table, and the architecture constraints that `archspec check` enforces. This skill adds the step-by-step recipes.

## Choose the layer

The public API covers conversations (`Chat`, `Message`, `Tool`, `Agent`, structured output, streaming, and loop control) and individual operations (`paint`, `animate`, `speak`, `transcribe`, `ocr`, `moderate`, `embed`, and `rerank`). Both connect to services through providers and protocols: providers supply endpoints, authentication, catalogs, and protocol selection; protocols implement formats and their dialects.

Model resolution, configuration, accounting, instrumentation, batches, and provider resources support the framework as a whole. Keep independent operations independent of `Chat`. Rails integration adds Active Record, Active Storage, Hotwire, jobs, and generators around the same Ruby API; it does not define a second conversation API.

## The fast loop

```bash
bundle exec rspec --tag ~live # unit tests only, no keys, seconds not minutes
bundle exec rspec spec/ruby_llm/chat_spec.rb:42 # one example
overcommit --run # what the commit hook will run
```

Run the fast loop while developing. Run `overcommit --run` before declaring anything done: it runs RuboCop (auto-correct), Flay (duplication), archspec (architecture), and the unit suite, and the commit fails if any of them do.

## Working with cassettes

Specs tagged `:live` replay recorded provider traffic from `spec/fixtures/vcr_cassettes`. The cassette name derives from the example's full description, so renaming an example orphans its cassette.

To re-record after changing request shapes:

```bash
rake vcr:record[openai] # deletes that provider's cassettes, reruns the suite
rake vcr:record[openai,anthropic] # several providers
rake vcr:record[all] # everything (long, needs many keys)
```

Recording needs real API keys in `.env`. To re-record a single spec, delete its cassette file and run the spec with the provider's key set.

Rules that bite:

- A failing `:live` example deletes its own cassette on purpose. Do not "fix" a red spec by restoring the cassette; fix the code, then re-record.
- Review every new or changed cassette for leaked keys and personal data before committing. The pre-commit hook runs gitleaks, but eyes first.
- A spec that can neither replay (no cassette) nor record (no key) skips with a message naming the env var it needs. That is expected on a fork without keys.

## Adding or changing a chat option

1. Add `with_x` to `lib/ruby_llm/chat.rb` (chainable, returns `self`).
2. Add the matching bare `x` class macro to `lib/ruby_llm/agent.rb`. The archspec build fails without it.
3. Thread the option through the `Provider` contract, never by referencing a concrete provider or protocol from `Chat`.
4. Implement per protocol in `lib/ruby_llm/protocols/*` (`render_*` for request payloads, `parse_*` for responses).
5. Spec it in `spec/ruby_llm/chat_<x>_spec.rb`; add live coverage over the matrix in `spec/support/models_to_test.rb` when providers differ.
6. Document it on the matching page in `docs/_core_features/`.

## Working on an individual AI operation

1. Start with the public operation and its typed result. Keep the shared RubyLLM names consistent across providers.
2. Route service calls through the `Provider` contract and registered protocols. Keep request rendering, response parsing, and format quirks in protocols.
3. Reuse model resolution, configuration, usage accounting, and instrumentation where applicable. Preserve the operation's streaming or job lifecycle.
4. Test public behavior and protocol translation at their respective layers. Document the operation in its feature guide and keep its Getting Started example short.

## Adding a provider

For smaller or emerging providers, ship a community gem instead of a core PR (the core bar is high, see CONTRIBUTING.md):

```bash
bundle exec ruby_llm provider-gem Acme --api-base https://api.acme.ai/v1
```

That scaffolds a complete gem with specs and CI. For an approved core provider:

```bash
script/generate-provider acme
```

Then make it real:

1. `lib/ruby_llm/providers/acme.rb` declares auth, API base, and which protocols it speaks (`protocol :chat_completions, ...`). If the provider has its own wire format, that format is a new protocol under `lib/ruby_llm/protocols/`, not code inside the provider.
2. Replace the scaffold's example capabilities with the provider's real ones, and identify the model-catalog source.
3. Register the provider in `lib/ruby_llm.rb` (the entrypoint is the only place concrete providers get wired in).
4. Record cassettes covering normal chat and streaming chat at minimum.
5. Document configuration in `docs/_getting_started/configuration-providers.md`.

## Rails work

- Rails specs run against the dummy app in `spec/dummy`; `acts_as_chat` and `acts_as_message` live in `lib/ruby_llm/active_record/`.
- The Rails integration builds on the domain layer and the `Provider` contract only. It converts records with `to_llm`/`from_llm`; plain-Ruby objects never define those.
- Preserve the Ruby conversation API on records, Active Storage attachment support, and the persisted message lifecycle used by Hotwire streaming and background jobs. Individual operations remain callable directly from Rails services and jobs.
- Generators live in `lib/generators/ruby_llm/` (install, upgrade, chat_ui, agent, tool, schema, provider). Their specs are tagged `:generator` and excluded from the pre-commit run; run them explicitly with `bundle exec rspec --tag generator`.
- Check Rails-version compatibility across the matrix: `bundle exec appraisal rails-7.1 rspec` through `rails-8.1`.

## Docs work

- Pages live in `docs/` under `_getting_started`, `_core_features`, `_advanced`, and `_reference`. Preview with `docs/bin/serve.sh`.
- Voice: Rails guides. Second person, present tense, show the code before explaining it, motivate each feature with the problem it solves in one sentence. No em dashes, no hype, no "simply".
- Lead with short, working public API examples. Show how features combine when it helps the reader build something. Keep the title, description, and "After reading this guide" opening; introduce concepts before adding a summary table.
- Front-matter `description` becomes the page's llms.txt entry and social card text: one compelling sentence, no `&`, `<`, or `>`.
- Cross-link with `{% link _collection/page.md %}`, never hard-coded URLs.
- `docs/_reference/available-models.md` is generated; never edit it.

## Before opening the PR

1. `overcommit --run` passes.
2. New behavior has specs; changed provider behavior has re-recorded cassettes, reviewed for secrets.
3. Public API changes are documented in `docs/` and have RDoc.
4. The PR does one thing, references its approved issue, and explains the problem before the solution.

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许可证: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
缺少 AI 审查批准
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

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仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: crmne/ruby_llm
许可证: MIT
版本: Unknown
最近 GitHub 推送: 2026年9月14日
目录更新于: 2026年9月15日
技能指令路径: .claude/skills/contributing/SKILL.md @ 08d273f1f011

版本来自目录元数据，使用前请核实来源发布记录。

质量

78/100

强

信任

67/100

仅限沙盒

审计

80/100

需审查

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Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
缺少 AI 审查批准
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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结果: —

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      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 80,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "AI review approval is missing",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 78,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "26d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "AI review approval is missing"
  ],
  "agent_contract": {
    "task_input": "Use contributing in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 75/100 Strong shortlist",
      "Audit: 80/100 Needs review",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "crmne-contributing (contributing)",
      "install_command": "npx skills add crmne/ruby_llm --skill contributing",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "crmne-contributing",
      "task": "Use contributing in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/crmne-contributing",
    "api": "https://www.openagentskill.com/api/agent/skills/crmne-contributing",
    "audit": "https://www.openagentskill.com/skills/crmne-contributing/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=crmne-contributing&task=Use%20contributing%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20contributing%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20contributing%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/crmne-contributing/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/crmne-contributing"
  }
}

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创作者: crmne
来源: crmne/ruby_llm
收录方: OpenAgentSkill 社区索引

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