Results for “repair”

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

Diagnoses and fixes failing installed skills by reading the SKILL.md and error output, classifying the failure, applying the smallest fix, and attaching a verification recipe.

Expert en DIY et making (3D printing, Arduino, woodworking, electronics, repair)

Fix CI failures by fetching GitHub Actions logs, dispatching dev to fix, verifying locally, and pushing.

Design or repair a CI/CD pipeline with well-ordered parallel stages, lockfile-keyed caching, quality gates, and build-once artifact promotion.

Use when the user wants to set up, check, repair, or report the SuperCtx state for this repository. Consolidated command for global context convergence.

Plan tailored engagement for a specific stakeholder before critical conversations, milestones, or relationship repairs.

Use when paired-end reads need to be reordered so mates appear consecutively, or when preparing BAM files for featureCounts by adding dummy reads for singletons.

Generating, updating, auditing, and sync-repairing project-specific Claude Code skills from the repo stack and conventions. Use when authoring project-local skills (Micro or Full).

Discover, compare, audit, repair, adapt, craft, run, debrief, and publish repeatable AI-agent loops for engineering workflows.

Discovers, audits, repairs, adapts, and designs bounded AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs.

Create, repair, validate, and package Codex-compatible animated pets and spritesheets from character art, brand cues, or reference images.

Discover, audit, repair, adapt, and design bounded, verifiable AI-agent loops with explicit triggers, actions, stopping conditions, and guardrails.

Discovers, finds, audits, repairs, adapts, crafts, runs, debriefs, saves, and prepares repeatable AI-agent loops for publication, treating loops as bounded feedback systems.

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.

Use when entering a repository with a .ctx/ hub, or when the user asks about project context, agent instructions, tool configuration, health, candidates, broken shims, repair, or cross-tool context.

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".

Audits and repairs Markdown link health across a skills repo via a four-tier pipeline (config hardening, intra-repo file-ref fixes, external URL substitutions, residual exclusions) and enforces a Tier 3 substitution guardrail that prevents regressions of previously-passing links; designed for lychee-based GitHub Actions link checkers but generalizes to markdown-link-check and similar tools. Use when the request mentions link audit, dead links, link health, lychee, broken links, link checker, markdown link audit, link-health audit, 404 audit, check-links failing, CI link-check, or 連結健檢, 死鏈, 失效連結, 斷鏈檢查. Part of the AlterLab Academic Skills suite.

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

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.

通用多轮迭代改进：对任意研究制品反复调用 /review → 解析反馈 → 修复 → 更新 wiki，直到达标

Prioritizes and remediates endpoint vulnerabilities by importing scan results, applying patches via WSUS/SCCM/Intune, making configuration changes, and validating fixes.

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.

Finds root cause before applying fixes for errors, crashes, regressions, failing tests, broken behavior, and screenshot-reported defects. Use when users report in any language errors, crashes, broken behavior, regressions, failing tests, screenshot evidence, or something that used to work and now fails. Not for code review or new features.

Generates specific, implementable fixes for each vulnerability finding, producing code patches, configuration changes, dependency updates, and IaC fixes with before/after code and verification steps.

Rehearse

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

Run a reproduce → isolate → verify debugging workflow for concrete bugs, regressions, flaky failures, and environment-specific behavior. Use when the user already has a failing command, test, request, UI flow, or narrowed symptom and needs root-cause diagnosis or fix verification rather than raw log-line selection, broad test-policy design, PR review, or generic performance tuning.

Reproduce a bug as a failing regression test, fix until green, review, and commit via a delegated pipeline.

Use when investigating a bug, runtime error, unexpected behavior, test failure, or crash. For systematic root cause analysis, error message interpretation, stack trace reading, reproduction steps, and targeted fixes. Activate when the user says "it's broken", "this doesn't work", "I'm getting an error", or "help me debug".