Results for “prompt”

Prompt for generating an AGENTS.md file for a repository

Call OpenAI chat completions API with a prompt and system message

Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).

Generate cinematic videos with Seedance 2.0 using director-level prompts, camera grammar, and multi-mode generation across Chinese, Global, and VIP tiers.

Reads brand identity, ICP, and messaging files to produce on-brand social video with storyboard, reference images, and Seedance 2.0-optimized prompts.

Reduces AI API costs by 30-75% by classifying task complexity, checking prompt quality, and routing tasks to the most cost-efficient model tier before execution.

Define every visual attribute as structured VGL JSON for deterministic, reproducible image generation with Bria FIBO models, covering objects, lighting, camera settings, composition, and style.

Route research prompts to evidence, retrieval, scraping, data, market, or scientific research skills. Use when prompts mention deep research, current facts, citations, Exa, iterative retrieval, search-first, scraping, data pipelines, PubMed, USPTO, gget, literature review, or scholar evaluation.

Route security, compliance, privacy, crypto/Web3, framework security, vulnerability review, and risk prompts. Use when prompts mention security-review, security scans, bounty hunting, HIPAA, PHI, compliance, Web3 risk, smart contracts, x402, LLM trading risk, or framework-specific security.

After a Content Factory run completes successfully, harvest prompts, character references, cost data, and quality scores; diff them against the canonical prompt_registry and character_identity store; open improvement PRs (prompt updates + new character references). Runs ONCE per successful run, then closes. Also files findings into a CF_IMPROVER_ISSUES.csv (Intelliverse audit framework pattern) for the librarian's weekly roll-up.

Run multiple AI agents against a product, SDK, or docs site to score setup friction, speed, efficiency, error recovery, and doc quality from a minimal prompt.

Generates structured VGL JSON prompts for Bria's FIBO image generation models, covering text-to-image, editing, inpainting, outpainting, and captioning with a deterministic schema.

Route business growth prompts to the right skills. Use when prompts mention fundraising, investors, market research, campaigns, SEO, lead intelligence, social graph ranking, connection optimization, sales outreach, or when the user wants an easy group invocation for growth skills.

Plan customer discovery conversations with hypotheses, prompts, probes, and capture fields.

Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.

Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.

Rewrite any prompt, instruction, task description, or spec into a "genie-proof" version — instructions so explicit, literal, and loophole-free that even a maliciously literal genie (or an LLM, contractor, or junior dev) could not misinterpret them. Use this skill whenever the user asks to genie-proof, tighten, harden, de-ambiguate, or "make bulletproof" a prompt or instruction; whenever they complain that an AI/model/person "didn't do what I meant," "took me too literally," or "found a loophole"; or whenever they hand over a vague prompt and ask to make it precise, explicit, unambiguous, or idiot-proof. Also trigger on phrases like "wish to a genie," "monkey's paw," "lawyer-proof this prompt," or "leave nothing to interpretation."

Reads a plan document, decomposes it into steps, designs a per-step agent chain from the ECC catalogue, and emits ready-to-paste /orchestrate custom prompts without executing them.

Use this skill when reviewing Model Context Protocol (MCP) servers, clients, connectors, tool manifests, hosted MCP deployments, OAuth flows, prompt/tool boundaries, or MCP marketplace choices for security, least privilege, data exposure, prompt injection, and destructive-action risk.

Trains, evaluates, exports, quantizes, and runs inference for a Mask Grounding DINO model for open-set instance segmentation guided by text prompts.

Generates AI images via Ideogram, Leonardo, or Flux using plugin scripts, or delivers an optimized prompt when no API key is configured. Requires explicit cost confirmation before each paid generation.

Generates a focused style foundation prompt for AI Studio, building exactly three exemplary sections—hero, typography, and card grid—to establish a reusable visual language for any subsequent development.

Ready-to-use prompts for comprehensive .NET framework upgrade analysis and execution

Give your AI agents something more useful than a prompt. Velocity through clarity.

Declarative programming framework for optimizing LLM prompts through compilation and automatic tuning

Plan structured workshops with goals, agenda, activities, prompts, decision rules, and follow-ups.

AI Manipulation Defense System with self-learning prompt injection detection and adaptive mitigation

Assess AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, and agent tool abuse, with MITRE ATLAS mapping and guardrail recommendations.

Assigns behavioral trust grades (A–F) to MCP servers by running probes for prompt injection, permission overreach, data leaks, and adversarial-input handling, and publishes reproducible onchain attestations.

Deploy Llama Guard, NeMo Guardrails, and LLM Guard as runtime input/output scanners to block jailbreaks, prompt injection, and toxic content in production LLM applications.

Conduct authorized security assessments of LLM applications and AI agents, covering prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.

Integrates WordPress 7.0's WP AI Client for provider-agnostic AI generation, covering prompt building, model preferences, structured JSON, media generation, and safe feature gating.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

Automatically measures whether coding agents follow skills, rules, or agent definitions by generating scenarios at multiple prompt strictness levels, running agents, classifying tool calls, and reporting compliance rates with full timelines.