ReddRadar
Topic: prompt-engineering
2,538 skills in this topic.
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email-campaign
jmagly/aiwg 107
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forensics-acquire
Evidence acquisition with chain of custody and hash verification
jmagly/aiwg 107
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prompt-architect
Analyzes and improves prompts using 27 research-backed frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.
ckelsoe/prompt-architect 100
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add-new-entry
Workflow and tools for adding new entries from temp.md to the section files. Includes legend format, section reference, code tools, and common pitfalls. USE FOR: Adding new resources to the knowledge base. DO NOT USE FOR: Editing existing entries or restructuring sections.
kimtth/awesome-azure-openai-llm 398
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classify-temp-entries-to-section
Classification guidelines for entries in temp_entries.md. Each entry have its own title with the markdown file name and section name in temp_entries.md. USE FOR: Classifying new entries in temp_entries.md into *.md in the section files. DO NOT USE FOR: 1) Adding new entries to temp_entries.md; 2) Moving entries between sections.
kimtth/awesome-azure-openai-llm 398
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update-app-count
Workflow for updating the popular LLM applications pool (section/x_llm_apps.md) using get_app_list_by_github_star.py. Covers full refresh, alternate exports, topic tuning, and common pitfalls. USE FOR: Refreshing the ranked GitHub applications list linked from applications.md. DO NOT USE FOR: Hand-curating application entries inside applications.md or adding GitHub star badges to the generated file.
kimtth/awesome-azure-openai-llm 398
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update-cite-count
Guidelines for updating citation counts for papers in the section files using the `update_citation_counts.py` tool. USE FOR: Updating citation counts for papers listed in the section files to keep information current. DO NOT USE FOR: 1) Adding new papers to the section files; 2) Classifying entries into sections.
kimtth/awesome-azure-openai-llm 398
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update-llm-pool
Workflow for updating the LLM landscape paper pool (section/x_llm_papers.md) using fetch_llm_papers.py. Covers full re-fetch, resume from checkpoint, and adding new topics. USE FOR: Refreshing citation counts, expanding topic coverage. DO NOT USE FOR: Adding hand-curated entries to section files (use add-new-entry), updating RAG/Agent citation sections in best_practices.md (use update-cite-count).
kimtth/awesome-azure-openai-llm 398
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dream-memory
Consolidate recent logs, sessions, and existing memory files into durable topic memories, normalize dates, prune stale entries, and keep MEMORY.md short enough for prompt use.
LearnPrompt/cc-harness-skills 200
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kairos-lite
Build a lightweight proactive mode with scheduled checks, sleep intervals, concise user briefs, and expiry safeguards so an agent can work in the background without becoming an uncontrolled daemon.
LearnPrompt/cc-harness-skills 200
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memory-extractor
Extract durable memories from recent conversation turns into user, feedback, project, and reference categories while avoiding stale code-state facts.
LearnPrompt/cc-harness-skills 200
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structured-context-compressor
Compress a long agent conversation into a nine-part continuation summary that preserves request, files, errors, user messages, current work, and the next aligned step.
LearnPrompt/cc-harness-skills 200
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swarm-coordinator
Coordinate multiple agents by splitting work into research, synthesis, implementation, and verification, assigning ownership, and keeping the coordinator focused on integration rather than raw exploration.
LearnPrompt/cc-harness-skills 200
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verification-gate
Run a read-only verification pass after implementation to check whether completion claims are real, validation actually ran, and obvious edge cases or regressions were missed.
LearnPrompt/cc-harness-skills 200
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digital-worked-example-sequence
Create an interactive digital worked example sequence with fading for online or blended delivery. Use when building e-learning modules, LMS content, or app-based instruction.
GarethManning/claude-education-skills 146
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erroneous-example-designer
Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.
GarethManning/claude-education-skills 146
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formative-assessment-loop-designer
Design an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.
GarethManning/claude-education-skills 146
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individual-spacing-algorithm-explainer
Explain and configure individual spacing algorithms using student performance data and forgetting curves. Use when personalising retention schedules in adaptive learning platforms.
GarethManning/claude-education-skills 146
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intelligent-tutoring-dialogue-designer
Script a multi-turn tutoring dialogue with branching responses for anticipated student difficulties. Use when designing AI tutors, chatbot interactions, or structured one-to-one support scripts.
GarethManning/claude-education-skills 146
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adaptive-hint-sequence-designer
Generate a cascading hint sequence for a problem type, revealing progressively without giving answers. Use when designing tutoring dialogues or scaffolded worksheets.
GarethManning/claude-education-skills 146
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ai-facilitated-collaborative-learning-designer
Design AI-supported collaborative tasks that structure group interaction and address participation problems. Use when students struggle to collaborate effectively on group tasks.
GarethManning/claude-education-skills 146
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learning-analytics-interpretation-guide
Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.
GarethManning/claude-education-skills 146
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metacognitive-monitoring-ai-contexts
Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
GarethManning/claude-education-skills 146
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ai-feedback-design-principles
Audit and redesign AI-generated feedback for pedagogical quality, timing, and learning impact. Use when building or reviewing automated feedback in digital learning tools.
GarethManning/claude-education-skills 146