bmad-advanced-elicitation
De la autor
Push the LLM to reconsider, refine, and improve its recent output. Use when user asks for deeper critique or mentions a known deeper critique method, e.g. socratic, first principles, pre-mortem, red team
Instalare
npx skills add alvin-reyes/better-agentic-ide --skill bmad-advanced-elicitation -a claude-code
Se instalează în .claude/skills/bmad-advanced-elicitation/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 77
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- prompt-optimizationproflead/codex-skills-libraryImprove and rewrite user prompts to reduce ambiguity and improve LLM output quality. Use when a user asks to optimize, refine, clarify, or rewrite a prompt for better results, or when the request is about prompt optimization or prompt rewriting.Altele★ 167
- bmad-reviewalvin-reyes/better-agentic-ideRuns one or more installed review lenses — adversarial critique, edge cases, verification gaps, structure, prose — and reports triaged findings. Use when, and only when, the user asks you to review a diff, a pull request, or an artifact — code or documents, one or many — and actually says "review"; an explicit skill:bmad-review directive from another skill counts as that ask. A request to act on feedback from an earlier review is a change, not a review. Never invoke this uninvited, including on edits you just made.Inginerie★ 85
- mltelagod/code-abyssMachine learning and LLM engineering judgment, distilled from a stronger model - invoke when DECIDING whether/how to use ML or an LLM for a task (prompt vs RAG vs fine-tune vs classical); working with training/eval data or labels; building or reviewing evals for models and LLM features; designing RAG, structured output, or agent pipelines; or diagnosing why a model/LLM feature underperforms. Method-selection ladder, data and leakage discipline, eval-as-spec rules, LLM-era craft, and a trap catalog.Planificare★ 244
- blender-iterative-refinementifBars/blender-agent-studioRun an opt-in evidence-backed second pass on a complete Blender deliverable. Use when the user explicitly asks to critique, improve, repair, iterate, or apply a practice-makes-perfect loop; when a benchmark fixture explicitly requires retained before-and-after evidence; or when a completed candidate must be improved without regressing its existing technical or visible qualities.Altele★ 99
- senior-prompt-engineerborghei/Claude-SkillsPrompt engineering and LLM evaluation. Use when optimizing prompts, designing prompt templates, evaluating LLM outputs, building agentic systems, implementing RAG, creating few- shot examples, or designing structured-output workflows.Altele★ 1K
- prompt-optimizerwasintoh/toh-frameworkAdvanced prompt optimization and composition system for all prompt types (system prompts, task-specific, creative, technical, agentic). Use when users need to refine, optimize, or transform prompts to achieve better AI outputs. Triggers include requests to improve prompts, make prompts more effective, analyze prompt quality, create prompts from scratch, optimize existing prompts for specific goals, or optimize for Claude 4.x models. Also use when users share prompts that could be improved or ask for prompt engineering guidance.Planificare★ 97
Note și recenzii
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