are-you-sure
Deliberate fresh-eyes self-review and repair after making changes. Use when an agent has just written or modified code, config, tests, or docs and should pause to look for obvious bugs, regressions, missing tests, confusing behavior, or risky assumptions, fix the clear local issues it finds, and only then finalize, hand off, or commit. Supports Claude Code, Codex, and Gemini with provider notes in references/.
What this skill does
# Are You Sure Do one careful second pass on the changes you just made, and fix the clear issues you find before you declare the work done. ## Read the right reference - Read [references/workflow.md](references/workflow.md) for the provider-neutral fresh-eyes review loop. - Read [references/claude.md](references/claude.md) if this should run in Claude Code. - Read [references/codex.md](references/codex.md) if this should run in Codex. - Read [references/gemini.md](references/gemini.md) if this should run in Gemini CLI. ## Use this skill when - you just edited files and are about to send a final answer - you are about to commit or open a PR - the change involved generated or agent-written code - the edit touched behavior, configuration, tests, or interfaces - you want a quick self-check before asking for a second opinion ## Do not use this skill as - a substitute for real verification - a substitute for a dedicated security audit - a substitute for an external second-opinion review If the change is high-risk, run this skill first, then use a separate review skill. ## Fresh-eyes workflow 1. Reconstruct the exact scope of what changed. 2. Re-read the diff and changed files slowly. 3. Check the five risk buckets from the workflow reference. 4. Run the cheapest meaningful verification you can. 5. If you find a likely issue, inspect neighboring code before concluding. 6. Default to fix mode: if the issue is clear, local, and easy to verify, patch it immediately. 7. Re-run the narrowest relevant verification and repeat the review once. 8. If the fix would widen scope, change architecture, or needs human judgment, stop and report it instead of guessing. 9. Return what you fixed, what still looks risky, or say explicitly that no substantive issues were found. ## Output contract Return in this order: 1. Issues found and fixed, with file references. 2. Issues still remaining or explicitly review-only findings. 3. Verification performed. 4. Residual risk, open questions, or `No substantive issues found`. Keep the review concrete. Do not pad it with praise or a changelog.
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.