Rank #10 of 13 in AI Coding Agents
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See what an agent can do with cubic before you ever sign up. Pick a story: recorded sessions replay real probe-harness transcripts; commands tagged live-capable can re-run against the real endpoint from our edge, right now (▶ run live — the exact same request, live and recorded lines always labeled); sandboxed self-drive sessions are designed and gated (docs/TRY-IT.md).
$curl -s https://docs.cubic.dev/llms.txt | head -6recorded session — replayed, not liveVerified integrations
No integration evidence found in our corpus for this product yet — that means none was found, never that it doesn’t integrate.
By theme — the product's score on each story themeBy theme
Agenticness — how well agents can access and operate the productAgenticnessevidence →
How well agents can access and operate the product
Automation depth — how much of the product can run unattendedAutomation depthevidence →
How much of the product can run unattended
Autonomy agents — stories about autonomy agents in this arenaAutonomy agentsevidence →
Stories about autonomy agents in this arena
Code generation — quality of generated code — correctness, style, fit to the codebaseCode generationevidence →
Quality of generated code — correctness, style, fit to the codebase
Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understandingevidence →
How deeply the tool maps your repo — cross-file context, architecture awareness, history
Ecosystem — integrations, plugins, and third-party ecosystem storiesEcosystemevidence →
Integrations, plugins, and third-party ecosystem stories
Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integrationevidence →
Meeting you in the IDE and terminal — extensions, inline flows, context
Openness — open source, data portability, and self-hosting storiesOpennessevidence →
Open source, data portability, and self-hosting stories
Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limitsevidence →
Free-tier ceilings, usage caps, and rate limits before you have to pay
Privacy posture — data-handling and privacy storiesPrivacy postureevidence →
Data-handling and privacy stories
Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safetyevidence →
Keeping generated changes safe — diffs, approvals, guardrails
Story verdicts — every judged story with its evidenceStory verdicts
What’s free: 0 free · 1 paid · 0 enterprise · 35 not stated in evidence
Follow the green: where the map greys out is where cubic stops today. ✓ full · ~ partial · ! disputed · — none · n/a not applicable.
Agenticness — how well agents can access and operate the productAgenticness
How well agents can access and operate the product
API surface
Drive the product through a documented public API
~6/10
unlocks → Webhooks · Official SDKs · Scoped API keys · Machine-readable spec · Versioning policy · Have a cloud agent build, test, and demo a feature end-to-end for my review · Launch fleets of autonomous agents that work in parallel on different tasks for hours or days · View interactive diffs and share selected code as context from within my JetBrains IDE
Subscribe to events via webhooks
—–
Build against official SDKs
—0/10
Issue scoped/least-privilege API credentials for an agent
—0/10
Connect an agent via an official MCP server
✓8/10
Download a machine-readable API spec (OpenAPI or equivalent)
—0/10
Rely on versioned APIs with a documented deprecation policy
—0/10
Test against a sandbox environment without touching production data
n/an/a
Explore an interactive API reference with runnable examples
—0/10
Agentic features
Delegate tasks to a built-in AI assistant inside the product
✓7/10
unlocks → MCP client
Operate the product with natural-language commands
✓7/10
Plug MCP servers into this product so it can use their tools
—0/10
Get AI-generated insights and suggestions from my data inside the product
~6/10
Set up automations that run autonomously in the background
✓8/10
Automation depth — how much of the product can run unattendedAutomation depth
How much of the product can run unattended
Autonomy agents — stories about autonomy agents in this arenaAutonomy agents
Stories about autonomy agents in this arena
Background execution
Parallel agents
Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomously
~6/10
Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation
Quality of generated code — correctness, style, fit to the codebase
Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understanding
How deeply the tool maps your repo — cross-file context, architecture awareness, history
Ecosystem — integrations, plugins, and third-party ecosystem storiesEcosystem
Integrations, plugins, and third-party ecosystem stories
Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration
Meeting you in the IDE and terminal — extensions, inline flows, context
Start a task on one device and continue it later from another device or browser
~3/10
Ide integration
Session management
Openness — open source, data portability, and self-hosting storiesOpenness
Open source, data portability, and self-hosting stories
Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits
Free-tier ceilings, usage caps, and rate limits before you have to pay
Privacy posture — data-handling and privacy storiesPrivacy posture
Data-handling and privacy stories
Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety
Keeping generated changes safe — diffs, approvals, guardrails
Sorted by importance (agentic first) (high → low) · 74/74 stories · click a row’s chevron for the rationale and evidence
Connect an agent via an official MCP server G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | full | 8/10 | Tprobed | |
Delegate tasks to a built-in AI assistant inside the product G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | full | 7/10 | Cclaimed | |
Drive the product through a documented public API G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | partial | 6/10 | Tprobed | |
Plug MCP servers into this product so it can use their tools G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 3 | none | 0/10 | ||
Point an agent at llms.txt or agent-oriented docs G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 9/10 | Tprobed | |
Set up automations that run autonomously in the background G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 8/10 | Cclaimed | |
Use an official CLI G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 8/10 | Tprobed | |
Operate the product with natural-language commands G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | full | 7/10 | Cclaimed | |
Get AI-generated insights and suggestions from my data inside the product G Agentic features | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 6/10 | Xcommunity | |
Run the product headlessly / in CI for automation G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | partial | 6/10 | Tprobed | |
Build against official SDKs G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Download a machine-readable API spec (OpenAPI or equivalent) G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Explore an interactive API reference with runnable examples G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Issue scoped/least-privilege API credentials for an agent G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Rely on versioned APIs with a documented deprecation policy G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | 0/10 | ||
Subscribe to events via webhooks G Agent access | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 2 | none | untested | none yet | |
Test against a sandbox environment without touching production data G Api quality | ai-native user | Agenticness — how well agents can access and operate the productAgenticness | 1 | n/a | untested | none yet | |
Inspect diffs and run checks to catch problems before merging C Pr review | developer | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 3 | full | 8/10 | Xcommunity | |
Add a project instructions file to set coding standards and conventions the agent follows C Context management | developer | Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understanding | 3 | partial | 6/10 | Cclaimed | |
Define rules that trigger actions automatically on events G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 3 | partial | 6/10 | Cclaimed | |
Get automatic code review with contextual feedback on every pull request C Pr review | developer | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 3 | partial | 6/10 | Xcommunity | |
Have the agent map and explain an entire unfamiliar codebase without manually selecting context files C Codebase mapping | developer | Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understanding | 3 | partial | 6/10 | Cclaimed | |
Understand how a codebase fits together to find where to start making changes C Codebase mapping | developer | Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understanding | 3 | partial | 6/10 | Cclaimed | |
Chat with the coding assistant directly inside my IDE for contextual help C Ide integration | developer | Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration | 3 | partial | 5/10 | Cclaimed | |
Run a coding agent locally from my terminal C Terminal workflow | developer | Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration | 3 | partial | 5/10 | Tprobed | |
Export all of my data in open formats and leave G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 3 | partial | 4/10 | Cclaimed | |
Have the agent stage changes, write commit messages, create branches, and open pull requests C Pr review | developer | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 3 | partial | 4/10 | Cclaimed | |
Reproduce issues, narrow down root causes, and verify fixes C Issue diagnosis | developer | Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understanding | 3 | partial | 4/10 | Cclaimed | |
Describe a feature or bug in plain language and have the agent implement or fix it across multiple files C Feature implementation | developer | Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation | 3 | partial | 3/10 | Cclaimed | |
Have the agent write tests, fix lint errors, resolve merge conflicts, and update dependencies for me C Maintenance automation | developer | Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation | 3 | partial | 3/10 | Cclaimed | |
Connect the agent to workflow tools like Jira, Slack, and Google Drive to extend its context C Tool integration | developer | Ecosystem — integrations, plugins, and third-party ecosystem storiesEcosystem | 3 | none | 0/10 | ||
Delegate longer-running coding tasks to run in the background in an isolated cloud environment C Background execution | developer | Autonomy agents — stories about autonomy agents in this arenaAutonomy agents | 3 | n/a | 0/10 | ||
Self-host the core product G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 3 | none | 0/10 | ||
Turn a tracked issue into a complete pull request end-to-end C Feature implementation | developer | Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation | 3 | n/a | 0/10 | ||
Prevent my data from being used to train AI models G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 3 | none | untested | none yet | |
Receive inline code completions and next-edit suggestions as I type C Code completion | developer | Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation | 3 | n/a | untested | none yet | |
Get contextual explanations and automatic fixes for security vulnerabilities C Security checks | developer | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 2 | partial | 7/10 | Xcommunity | |
Debug issues and troubleshoot using natural-language queries C Debugging | developer | Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation | 2 | partial | 6/10 | Xcommunity | |
Perform bulk operations across many items at once G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 2 | partialpaid | 6/10 | Cclaimed | |
Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomously C Scheduled automation | ai-native user | Autonomy agents — stories about autonomy agents in this arenaAutonomy agents | 2 | partial | 6/10 | Cclaimed | |
Sign in with my existing product subscription plan to use the coding agent C Authentication | developer | Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits | 2 | partial | 6/10 | Cclaimed | |
Choose which underlying AI model powers my session from multiple providers C Model choice | developer | Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits | 2 | partial | 5/10 | Cclaimed | |
Run the agent non-interactively in scripts for workflow automation C Terminal workflow | developer | Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration | 2 | partial | 5/10 | Tprobed | |
Do everything through the API that I can do in the UI G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 2 | partial | 4/10 | Tprobed | |
Include multiple project directories in a single session for broader context C Context management | developer | Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understanding | 2 | partial | 4/10 | Cclaimed | |
Kick off agent tasks directly from GitHub, GitLab, Linear, or Slack C Tool integration | developer | Ecosystem — integrations, plugins, and third-party ecosystem storiesEcosystem | 2 | partial | 4/10 | Xcommunity | |
Have the agent build and recall memory automatically across sessions C Context management | developer | Codebase understanding — how deeply the tool maps your repo — cross-file context, architecture awareness, historyCodebase understanding | 2 | partial | 3/10 | Cclaimed | |
Start a task on one device and continue it later from another device or browser C Cross device continuity | developer | Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration | 2 | partial | 3/10 | Cclaimed | |
Authenticate through an enterprise identity or cloud platform for compliance and scalability G Authentication | engineering-lead | Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits | 2 | none | 0/10 | ||
Authenticate with an API key instead of an account login G Authentication | developer | Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits | 2 | none | 0/10 | ||
Control which external tools and integrations the agent is allowed to access C Safe execution | engineering-lead | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 2 | none | 0/10 | ||
Have a cloud agent build, test, and demo a feature end-to-end for my review C Background execution | ai-native user | Autonomy agents — stories about autonomy agents in this arenaAutonomy agents | 2 | none | 0/10 | ||
Launch fleets of autonomous agents that work in parallel on different tasks for hours or days C Parallel agents | ai-native user | Autonomy agents — stories about autonomy agents in this arenaAutonomy agents | 2 | none | 0/10 | ||
Choose where my data is stored (region/residency) G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | untested | none yet | |
Configure a reproducible cloud environment with the dependencies and setup steps my repository needs C Background execution | developer | Autonomy agents — stories about autonomy agents in this arenaAutonomy agents | 2 | n/a | untested | none yet | |
Control data retention and deletion G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | untested | none yet | |
Generate a working app from a sketch, image, or PDF design C Multimodal generation | ai-native user | Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation | 2 | n/a | untested | none yet | |
Have the agent operate inside a sandbox when interacting with code, tools, and network resources C Safe execution | engineering-lead | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 2 | n/a | untested | none yet | |
Manage multiple agent-driven coding sessions from one unified workspace C Session management | engineering-lead | Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration | 2 | n/a | untested | none yet | |
Opt out of telemetry and usage tracking G | ai-native user | Privacy posture — data-handling and privacy storiesPrivacy posture | 2 | none | untested | none yet | |
Read the product's source under an open license G | ai-native user | Openness — open source, data portability, and self-hosting storiesOpenness | 2 | none | untested | none yet | |
Review diffs visually and run multiple sessions side by side in a desktop app C Session management | developer | Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration | 2 | n/a | untested | none yet | |
Schedule recurring jobs or workflows G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 2 | none | untested | none yet | |
Integrate third-party partner-built agent apps into my workflows C Marketplace | engineering-lead | Ecosystem — integrations, plugins, and third-party ecosystem storiesEcosystem | 1 | partial | 6/10 | Tprobed | |
Create a shared workspace from my docs and repos as a common source of truth for the team C Team knowledge | engineering-lead | Ecosystem — integrations, plugins, and third-party ecosystem storiesEcosystem | 1 | partial | 5/10 | Cclaimed | |
Equip the agent with custom skills to perform specialized tasks C Marketplace | developer | Ecosystem — integrations, plugins, and third-party ecosystem storiesEcosystem | 1 | partial | 4/10 | Cclaimed | |
Version, review, and roll back my automations G | ai-native user | Automation depth — how much of the product can run unattendedAutomation depth | 1 | partial | 4/10 | Cclaimed | |
Let the tool automatically pick the best model for each task C Model choice | developer | Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits | 1 | none | 0/10 | ||
View interactive diffs and share selected code as context from within my JetBrains IDE C Ide integration | developer | Ide terminal integration — meeting you in the IDE and terminal — extensions, inline flows, contextIde terminal integration | 1 | none | 0/10 | ||
Debug a live running web application directly from my coding assistant C Debugging | developer | Code generation — quality of generated code — correctness, style, fit to the codebaseCode generation | 1 | n/a | untested | none yet | |
Opt out of having my code and prompts used for AI model training C Data governance | engineering-lead | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 1 | none | untested | none yet | |
Run several task attempts in parallel and compare results before choosing one C Parallel agents | developer | Autonomy agents — stories about autonomy agents in this arenaAutonomy agents | 1 | n/a | untested | none yet | |
See license and public-code matching references for AI-suggested code C Security checks | engineering-lead | Review safety — keeping generated changes safe — diffs, approvals, guardrailsReview safety | 1 | none | untested | none yet | |
Sign in with a personal account to get free-tier access without managing API keys G Authentication | developer | Pricing limits — free-tier ceilings, usage caps, and rate limits before you have to payPricing limits | 1 | none | untested | none yet |
Opportunities — the stories that would move this product's scores, from its own judged verdictsOpportunitiestop 8 of 56 stories with headroom
What would move cubic’s scores — derived from its own judged verdicts, biggest headroom first. Each line quotes what the judge found missing; shipping it (or evidencing it publicly) is the fix.
Agenticness — how well agents can access and operate the productPlug MCP servers into this product so it can use their tools
nonemoves agent-readyimpact 45
All MCP-related evidence describes cubic acting as an MCP *server* that other coding agents/clients connect to (cubic-docs-15, cubic-docs-3, cubic-probe-3) — the reverse of this story, which asks whether a user can plug external MCP servers into cubic so it can consume their tools.
Ecosystem — integrations, plugins, and third-party ecosystem storiesConnect the agent to workflow tools like Jira, Slack, and Google Drive to extend its context
nonemoves PA Scoreimpact 30
cubic's documented integrations are limited to GitHub, an MCP server for coding agents, and ChatGPT/Claude Code subscriptions for local reviews; there is no evidence of connectors to Jira, Slack, or Google Drive for extending context.
Openness — open source, data portability, and self-hosting storiesSelf-host the core product
nonemoves PA Scoreimpact 30
cubic is presented as a hosted SaaS code review platform (GitHub app, cloud dashboard, analytics, flex capacity billing) with no documentation of a self-hostable core, on-prem deployment, or open-source release.
Privacy posture — data-handling and privacy storiesPrevent my data from being used to train AI models
nonemoves PA Scoreimpact 30
No evidence in the pack addresses data usage for AI model training, opt-out policies, or data privacy commitments — cubic's docs focus entirely on code review features with no mention of training data controls.
Agenticness — how well agents can access and operate the productIssue scoped/least-privilege API credentials for an agent
nonemoves agent-readyimpact 30
Cubic documents role-based access control for team subscription/settings (cubic-docs-47) and exposes an MCP server, Analytics API, and CLI that agents can connect to, but there is no evidence of issuing scoped or least-privilege API credentials/tokens specifically for an agent's use — no API key scoping, OAuth scope, or agent-specific credential mechanism is documented.
Agenticness — how well agents can access and operate the productBuild against official SDKs
nonemoves agent-readyimpact 30
cubic documents an MCP server, CLI, and an Analytics API, but there is no evidence of official language SDKs (e.g., Python/JS client libraries) for building against cubic programmatically; the OpenAPI probe also returned 404s across candidate paths, suggesting no formal SDK/API spec is published.
Agenticness — how well agents can access and operate the productSubscribe to events via webhooks
nonemoves agent-readyimpact 30
Missing: any documentation of webhook endpoints, event types, or subscription setup.
Agenticness — how well agents can access and operate the productExplore an interactive API reference with runnable examples
nonemoves API qualityimpact 30
cubic documents an Analytics API but the evidence pack shows explicit probe failures for OpenAPI/swagger specs (404s) and no mention of an interactive API reference or runnable examples anywhere in the docs.
Showing the top 8 of 56 — every none/partial verdict in the story verdicts table is headroom.
Think a verdict is wrong? Every verdicts-table row has a Flag link — see the methodology.
Coverage map — which docs area, API section, or community source covers which judged storiesCoverage map14 surfaces · 38 covered stories
Where the cited evidence behind each covered verdict came from — the same citations the verdicts table shows, no extra judging.
AI review docs29 stories
- Run the product headlessly / in CI for automation
- Use an official CLI
- Get AI-generated insights and suggestions from my data inside the product
- Set up automations that run autonomously in the background
- Delegate tasks to a built-in AI assistant inside the product
- Operate the product with natural-language commands
- Perform bulk operations across many items at once
- Define rules that trigger actions automatically on events
- Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomously
- Debug issues and troubleshoot using natural-language queries
- Describe a feature or bug in plain language and have the agent implement or fix it across multiple files
- Have the agent write tests, fix lint errors, resolve merge conflicts, and update dependencies for me
- Understand how a codebase fits together to find where to start making changes
- Have the agent map and explain an entire unfamiliar codebase without manually selecting context files
- Include multiple project directories in a single session for broader context
- Reproduce issues, narrow down root causes, and verify fixes
- Integrate third-party partner-built agent apps into my workflows
- Create a shared workspace from my docs and repos as a common source of truth for the team
- Kick off agent tasks directly from GitHub, GitLab, Linear, or Slack
- Start a task on one device and continue it later from another device or browser
- Chat with the coding assistant directly inside my IDE for contextual help
- Run a coding agent locally from my terminal
- Run the agent non-interactively in scripts for workflow automation
- Do everything through the API that I can do in the UI
- Choose which underlying AI model powers my session from multiple providers
- Have the agent stage changes, write commit messages, create branches, and open pull requests
- Get automatic code review with contextual feedback on every pull request
- Inspect diffs and run checks to catch problems before merging
- Get contextual explanations and automatic fixes for security vulnerabilities
Ide docs20 stories
- Point an agent at llms.txt or agent-oriented docs
- Run the product headlessly / in CI for automation
- Connect an agent via an official MCP server
- Use an official CLI
- Drive the product through a documented public API
- Operate the product with natural-language commands
- Debug issues and troubleshoot using natural-language queries
- Describe a feature or bug in plain language and have the agent implement or fix it across multiple files
- Have the agent write tests, fix lint errors, resolve merge conflicts, and update dependencies for me
- Understand how a codebase fits together to find where to start making changes
- Have the agent map and explain an entire unfamiliar codebase without manually selecting context files
- Integrate third-party partner-built agent apps into my workflows
- Start a task on one device and continue it later from another device or browser
- Chat with the coding assistant directly inside my IDE for contextual help
- Run a coding agent locally from my terminal
- Run the agent non-interactively in scripts for workflow automation
- Do everything through the API that I can do in the UI
- Sign in with my existing product subscription plan to use the coding agent
- Choose which underlying AI model powers my session from multiple providers
- Inspect diffs and run checks to catch problems before merging
docs.cubic.dev14 stories
- Run the product headlessly / in CI for automation
- Get AI-generated insights and suggestions from my data inside the product
- Set up automations that run autonomously in the background
- Operate the product with natural-language commands
- Define rules that trigger actions automatically on events
- Version, review, and roll back my automations
- Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomously
- Add a project instructions file to set coding standards and conventions the agent follows
- Reproduce issues, narrow down root causes, and verify fixes
- Equip the agent with custom skills to perform specialized tasks
- Kick off agent tasks directly from GitHub, GitLab, Linear, or Slack
- Run the agent non-interactively in scripts for workflow automation
- Have the agent stage changes, write commit messages, create branches, and open pull requests
- Get automatic code review with contextual feedback on every pull request
Codebase scan docs8 stories
- Get AI-generated insights and suggestions from my data inside the product
- Set up automations that run autonomously in the background
- Perform bulk operations across many items at once
- Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomously
- Understand how a codebase fits together to find where to start making changes
- Have the agent map and explain an entire unfamiliar codebase without manually selecting context files
- Reproduce issues, narrow down root causes, and verify fixes
- Get contextual explanations and automatic fixes for security vulnerabilities
Wiki docs8 stories
- Get AI-generated insights and suggestions from my data inside the product
- Set up automations that run autonomously in the background
- Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomously
- Understand how a codebase fits together to find where to start making changes
- Have the agent map and explain an entire unfamiliar codebase without manually selecting context files
- Have the agent build and recall memory automatically across sessions
- Create a shared workspace from my docs and repos as a common source of truth for the team
- Export all of my data in open formats and leave
Hacker News6 stories
- Get AI-generated insights and suggestions from my data inside the product
- Debug issues and troubleshoot using natural-language queries
- Kick off agent tasks directly from GitHub, GitLab, Linear, or Slack
- Get automatic code review with contextual feedback on every pull request
- Inspect diffs and run checks to catch problems before merging
- Get contextual explanations and automatic fixes for security vulnerabilities
Changelog docs6 stories
- Connect an agent via an official MCP server
- Operate the product with natural-language commands
- Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomously
- Have the agent build and recall memory automatically across sessions
- Do everything through the API that I can do in the UI
- Inspect diffs and run checks to catch problems before merging
Analytics docs5 stories
Code review platform docs4 stories
Configure docs4 stories
OpenAPI spec2 stories
Account docs2 stories
Probe proofs — replayable recordings from the probe harnessProbe proofs
Replayable recordings from our probe harness — see the Prove-It protocol to submit one.
$curl -s https://docs.cubic.dev/llms.txt | head -6reproduced$ curl -s https://docs.cubic.dev/llms.txt | head -6 # cubic documentation > cubic reviews code on GitHub and in local coding workflows. These docs cover review setup, coding agents, repository context, analytics, and account management. Older stacking and archived platform guides are listed separately below. ## How to use these docs
$curl -si -X POST https://www.cubic.dev/api/mcp -H 'Content-Type: application/json' -d '<jsonrpc initialize>'reproduced$ curl -si -X POST https://www.cubic.dev/api/mcp -H 'Content-Type: application/json' -d '<jsonrpc initialize>'
HTTP/2 401
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Claims vs evidence — vendor claims reconciled against independent verdictsClaims vs evidence
6 of 14 testable claims verified · 0 contradicted → integrity 43/100
24 distinct capability claims found in cubic’s own claimed-docs/GitHub materials, reconciled against our judge’s independent verdicts.
6
Verified
8
Unverified
0
Contradicted
24
Undersold
Verified (11)
“Re-reviews new commits after a force-push by safely diffing against the prior reviewed version”
Get automatic code review with contextual feedback on every pull requestpartialproof ↗
“Lets a coding agent check subscription status and manage team seats/roles via MCP”
“Automatically reviews new pull requests once installed”
Get automatic code review with contextual feedback on every pull requestpartialproof ↗
“Can trigger a review on an existing PR via an @-mention comment command”
Get automatic code review with contextual feedback on every pull requestpartialproof ↗
“Supports replying to a review comment to ask for clarification”
Get automatic code review with contextual feedback on every pull requestpartialproof ↗
“Can auto-generate and push a fix directly to the PR branch from a review comment”
Get contextual explanations and automatic fixes for security vulnerabilitiespartialproof ↗
“Provides a CLI command to review uncommitted changes locally before pushing”
Inspect diffs and run checks to catch problems before mergingfullproof ↗
“Can auto-approve clean PRs when policy allows, with a shadow mode to preview before going live”
Get automatic code review with contextual feedback on every pull requestpartialproof ↗
“Provides an MCP server so coding agents can read review findings, request reviews, and triage issues”
“Analytics dashboard surfaces AI coding usage, review impact, and delivery speed insights”
Get AI-generated insights and suggestions from my data inside the productpartialproof ↗
“Provides an Analytics API with PR-level data on flagged/fixed issues and AI-authored code”
Drive the product through a documented public APIpartialproof ↗
Unverified (8)
“Chat can give a step-by-step guided tour of a PR's changes”
Delegate tasks to a built-in AI assistant inside the productfullproof ↗
“Can select code and add it to AI chat with diff and codebase as context”
Chat with the coding assistant directly inside my IDE for contextual helppartialproof ↗
“Custom agents can be configured to enforce team coding standards”
Equip the agent with custom skills to perform specialized taskspartialproof ↗
“Can connect an existing ChatGPT Plus/Pro or Claude Code subscription to power local reviews”
Sign in with my existing product subscription plan to use the coding agentpartialproof ↗
“Uses a cubic.yaml config file as the source of truth for review behavior, ignore patterns, and custom agents”
Add a project instructions file to set coding standards and conventions the agent followspartialproof ↗
“Can export team member analytics data as CSV”
Export all of my data in open formats and leavepartialproof ↗
“Runs codebase-wide scans using many AI agents to find bugs and vulnerabilities”
Have the agent map and explain an entire unfamiliar codebase without manually selecting context filespartialproof ↗
“Auto-generates a searchable AI wiki of the codebase with architecture diagrams and source links”
Understand how a codebase fits together to find where to start making changespartialproof ↗
Undersold (24)
Point an agent at llms.txt or agent-oriented docsfullproof ↗
Run the product headlessly / in CI for automationpartialproof ↗
Set up automations that run autonomously in the backgroundfullproof ↗
Operate the product with natural-language commandsfullproof ↗
Perform bulk operations across many items at oncepartialproof ↗
Define rules that trigger actions automatically on eventspartialproof ↗
Set up always-on agents that run on schedules or triggers to maintain and fix my software autonomouslypartialproof ↗
Debug issues and troubleshoot using natural-language queriespartialproof ↗
Describe a feature or bug in plain language and have the agent implement or fix it across multiple filespartialproof ↗
Have the agent write tests, fix lint errors, resolve merge conflicts, and update dependencies for mepartialproof ↗
Have the agent build and recall memory automatically across sessionspartialproof ↗
Include multiple project directories in a single session for broader contextpartialproof ↗
Reproduce issues, narrow down root causes, and verify fixespartialproof ↗
Integrate third-party partner-built agent apps into my workflowspartialproof ↗
Create a shared workspace from my docs and repos as a common source of truth for the teampartialproof ↗
Kick off agent tasks directly from GitHub, GitLab, Linear, or Slackpartialproof ↗
Start a task on one device and continue it later from another device or browserpartialproof ↗
Run the agent non-interactively in scripts for workflow automationpartialproof ↗
Do everything through the API that I can do in the UIpartialproof ↗
Choose which underlying AI model powers my session from multiple providerspartialproof ↗
Have the agent stage changes, write commit messages, create branches, and open pull requestspartialproof ↗
Claims outside our story set (5)
Real capability claims found in cubic’s own materials, but no story in this arena’s taxonomy covers them yet — that’s feedback on the taxonomy, not a mark against the product.
“Can hide test files from PR diff/file tree to focus review on implementation changes”
source ↗“Offers an 'ultrareview' mode with more capable models for risky or complex changes”
source ↗“Supports an org-wide default config repo (cubic-config) applied to repos without their own settings”
source ↗“Exports the wiki as markdown into the repo and keeps it updated via a rolling PR”
source ↗“Can set a monthly spend limit and auto-purchase extra review capacity only as needed”
source ↗
Business model
Per-developer plans: Team $40/dev/mo (40k reviewed LOC) and Pro $99/dev/mo (80k LOC, scans, coding-agent fixes); flex capacity adds usage-based reviewed-line pricing.
pricing ↗Score trend
How this product’s scores have moved as evidence and verdicts are re-derived — a point per change, not per day.
Try Experimental
Run it in the microterminal →Recorded agent sessions — and a live MCP handshake where the vendor ships one.
Flag
⚑ Flag a verdictThink a verdict is wrong? Opens a prefilled GitHub issue — or use the ⚑ next to any verdict above.
For agents
Agent surface uptime MCP up · llms.txt up (tracking since Sep 11 '26)
