lintlang
De la autor
Use when writing or reviewing AI agent configs, system prompts, or tool definitions (JSON/YAML/Python) and you need to catch ambiguous tool descriptions, missing stop conditions, schema/description mismatches, or embedded prompts before they reach runtime. Deterministic static analysis, no LLM or network calls.
Instalare
npx skills add hermes-labs-ai/lintlang --skill lintlang -a claude-code
Se instalează în .claude/skills/lintlang/ · 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
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De ce 84
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.
- lintlang-audithermes-labs-ai/lintlangAudit a named AI agent config, system prompt, tool-definition or instruction file (YAML, JSON, Markdown, text, or Python) with the released LintLang CLI, on request. Use when the user asks to audit, lint, scan or review such a file for ambiguous tool descriptions, missing stop conditions, schema/description mismatches, or prompts embedded in Python, and names the file. Deterministic offline static analysis, no model call and no network call. Do not use for general code review, for prose documentation, or when no file has been named.Redactare★ 143
- missing-descriptionhermes-labs-ai/lintlangStatic analysis for AI agent configs, tool descriptions, and system prompts — catches vague tool descriptions, missing stop conditions, and schema gaps before they reach runtime. Zero-LLM, deterministic checks, built for CI.Altele★ 143
- prompt-engineerJeffallan/claude-skillsWrites, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.Testare★ 12K
- lint-tasksUiPath/coder_evalReview existing coder-eval task YAML — find criteria that cannot fail, prompts that leak the answer, and near-duplicate tasks, each with a fix. Read-only. Use when the user wants existing tasks reviewed, linted, audited, or checked for gaps.Productivitate★ 154
- prompt-engineeringCodeAlive-AI/ai-driven-developmentUniversal prompt engineering techniques for any LLM. Use when crafting, optimizing, or reviewing prompts for AI models. Triggers on requests like "improve this prompt", "write a system prompt", "optimize my instructions", "help me prompt engineer", "audit this prompt", "review my prompt", or when building agentic systems that need structured prompts.Redactare★ 165
- stop-slopbrycewang-stanford/Auto-Empirical-Research-SkillsRemove AI writing patterns from prose. Use when drafting, editing, or reviewing text to eliminate predictable AI tells.Redactare★ 4.8K
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