Results for “auto-fix”

Automates PR review and CI auto-fix for GitHub and GitLab using the Composio CLI. Pulls diffs, fetches failing job logs, posts review comments, and loops fix commits until checks go green.

Automatically applies ESLint fixes using the ESLint Node.js API with flat config support, handling rule conflicts across TypeScript-ESLint and eslint-plugin-react, and generating fix reports in SARIF format.

Automated workflow to detect and fix missing documentation

When reviewing code, generate the complete corrected version — not just the issues, but the exact fixed code ready to copy-paste.

Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates the feedback-fix-push-wait cycle.

Incrementally fixes Gradle build and compilation errors for Android and Kotlin Multiplatform projects.

Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates the feedback-fix-push-wait cycle.

Iteratively reviews and fixes Claude Code skill quality issues by running automated fix-review cycles using the skill-reviewer agent until they meet standards.

Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates the feedback-fix-push-wait cycle.

Iterate on a PR until CI passes. Use when you need to fix CI failures, address review feedback, or continuously push fixes until all checks are green. Automates the feedback-fix-push-wait cycle.

Creates custom ESLint rules using the ESLint RuleTester API and AST Explorer patterns, generating rule implementations with auto-fix suggestions based on estree node types and scope analysis.

Autonomous error recovery — detect failures, diagnose root cause, apply fixes, and resume without stopping

Session-end auto-extraction of lessons, errors, fixes, and user feedback into structured memory. Fires at session end. Requires consent on first run.

Systematically repair a broken feature or module by scoping, tracing dependencies, diagnosing all issues, fixing them in order, and verifying end-to-end.

Audits and rewrites user-facing copy across web and mobile codebases, improving clarity, tone, and actionability of labels, errors, CTAs, and empty states.

Runs a deep cross-layer consistency audit across a codebase, tracing features from UI to database to find broken wiring, missing handlers, model mismatches, and security gaps, then auto-fixes critical and warning issues.

Autonomously repairs failed task verification by choosing retry, decompose, prune, or escalate strategies before surfacing to the user.

Repairs failed task verification by choosing a strategy: retry with adjustments, decompose into sub-tasks, prune infeasible tasks, or escalate to the user.

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Processes a PR's review comments (Copilot or human), classifies them, applies the fixes that belong, replies to each thread, and pushes. Use whenever the user says "address the review", "fix what Copilot said", "handle the comments", mentions Copilot already reviewed a PR, or passes a PR number/URL with pending reviews — or in Spanish "revisá los comentarios", "arreglá lo que dijo Copilot", "atendé el review".

Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.

Audit and fix HTML metadata including page titles, meta descriptions, canonical URLs, Open Graph tags, Twitter cards, favicons, JSON-LD structured data, and robots directives.

Use when the user asks to fix, debug, or make a specific feature/module/area work end-to-end. Triggers: 'make X work', 'fix the Y feature', 'the Z module is broken', 'focus on [area]'. Not for quick single-bug fixes — this is for systematic deep-dive repair across all files and dependencies.

Runs a structured bug-fix pipeline that scouts, diagnoses, fixes, reviews, and finalizes code changes with optional quality re-verification.

Reviews and repairs risky or unclear code with minimal validated changes, running a fixed-point loop until the diff is clean across two consecutive review cycles.

Iteratively improves a compiled LaTeX paper through two rounds of external LLM review, fix implementation, and recompilation.

Runs a structured three-question lazy-check on significant output before submission, forcing root-cause analysis, alternative consideration, and outcome verification.

Continuous self-improvement through systematic logging, pattern detection, and behavioral updates. Use when: the owner corrects you, a task fails, you discover a better approach, or you notice a recurring pattern. Store raw learnings in .learnings/, update the specific skill or workflow that caused the issue when appropriate, and avoid vague promises to do better.

Audit and fix HTML accessibility issues including ARIA labels, keyboard navigation, focus management, color contrast, and form errors.

Runs a structured bug-fix pipeline: scouts evidence, diagnoses root cause, applies minimal fixes, and reviews before committing.