Prompt Improver
severity1/claude-code-prompt-improver
This skill enriches vague prompts with targeted research and clarification before execution.
Universal prompt engineering techniques for any LLM. An agent skill from CodeAlive-AI/ai-driven-development.
$ npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development prompt-engineering --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-engineering .claude/skills/prompt-engineering && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "prompt-engineering" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/prompt-engineering into .claude/skills/prompt-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineering", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/prompt-engineeringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development prompt-engineering --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/prompt-engineering .agents/skills/prompt-engineering && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-engineering" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/prompt-engineering into .agents/skills/prompt-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineering", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development prompt-engineering --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/prompt-engineering .cursor/skills/prompt-engineering && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "prompt-engineering" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/prompt-engineering into .cursor/skills/prompt-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineering", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CodeAlive-AI/ai-driven-development.git --path skills/prompt-engineering--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development prompt-engineering --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/prompt-engineering .gemini/skills/prompt-engineering && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "prompt-engineering" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/prompt-engineering into .gemini/skills/prompt-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineering", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CodeAlive-AI/ai-driven-development prompt-engineeringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/prompt-engineering .github/skills/prompt-engineering && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "prompt-engineering" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/prompt-engineering into .github/skills/prompt-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineering", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CodeAlive-AI/ai-driven-development prompt-engineering --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CodeAlive-AI/ai-driven-development.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/prompt-engineering .opencode/skills/prompt-engineering && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "prompt-engineering" agent skill from https://github.com/CodeAlive-AI/ai-driven-development/tree/main/skills/prompt-engineering into .opencode/skills/prompt-engineering/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-engineering", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
prompt-engineeringUniversal prompt engineering techniques for any LLM. An agent skill from CodeAlive-AI/ai-driven-development.
Prompt Engineering is an agent skill from CodeAlive-AI/ai-driven-development. Universal 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.
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including reference files (for example `README.md`, `references/claude-fable5-prompting.md` and `references/claude-fable51-prompting.md`).
It sits in AI & LLM Engineering, covering Prompt engineering. The repository describes itself as: Practices, protocols, and skills for AI-driven software development. Skills and safety hooks for Claude Code, Codex, OpenCode, Cursor, Antigravity, and any agent supporting the… The licence is MIT.
5 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 25b7b1d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are xml).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Prompt Engineering loads about 4.1k tokens when it runs, and up to ~77k if it reads all its reference files. Until then it costs about 93 tokens; SKILL.md has 1,220 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from CodeAlive-AI/ai-driven-development at commit 25b7b1d, republished under its MIT licence (© CodeAlive-AI). 1,220 words, ~4,085 tokens.
.claude/skills/prompt-engineering/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.Universal techniques for crafting effective prompts across any LLM.
Use XML tags to create clear, parseable prompts:
<context>Background information here</context>
<instructions>
1. First step
2. Second step
</instructions>
<examples>Sample inputs/outputs</examples>
<output_format>Expected structure</output_format>Benefits:
Best practices:
<instructions>, not sometimes <steps>)<context> tags..."<examples><example id="1">...</example></examples><thinking> for chain-of-thought, <answer> for final outputSpecify explicit constraints on length, format, and structure:
<output_spec>
- Default: 3-6 sentences or ≤5 bullets
- Simple yes/no questions: ≤2 sentences
- Complex multi-step tasks:
- 1 short overview paragraph
- ≤5 bullets: What changed, Where, Risks, Next steps, Open questions
- Use Markdown with headers, bullets, tables when helpful
- Avoid long narrative paragraphs; prefer compact structure
</output_spec>Explicitly constrain what the model should NOT do:
<constraints>
- Implement EXACTLY and ONLY what is requested
- No extra features, components, or embellishments
- If ambiguous, choose the simplest valid interpretation
- Do NOT invent values, make assumptions, or add unrequested elements
</constraints>Prevent hallucinations and overconfidence:
<uncertainty_handling>
- If the question is ambiguous:
- Ask 1-3 precise clarifying questions, OR
- Present 2-3 plausible interpretations with labeled assumptions
- When facts may have changed: answer in general terms, state uncertainty
- Never fabricate exact figures or references when uncertain
- Prefer "Based on the provided context..." over absolute claims
</uncertainty_handling>For inputs >10k tokens, add re-grounding instructions:
<long_context_handling>
- First, produce a short internal outline of key sections relevant to the request
- Re-state user constraints explicitly before answering
- Anchor claims to sections ("In the 'Data Retention' section...")
- Quote or paraphrase fine details (dates, thresholds, clauses)
</long_context_handling><tool_usage>
- Prefer tools over internal knowledge for:
- Fresh or user-specific data (tickets, orders, configs)
- Specific IDs, URLs, or document references
- Parallelize independent reads when possible
- After write operations, restate: what changed, where, any validation performed
</tool_usage><user_updates>
- Send brief updates (1-2 sentences) only when:
- Starting a new major phase
- Discovering something that changes the plan
- Avoid narrating routine operations
- Each update must include a concrete outcome ("Found X", "Updated Y")
- Do not expand scope beyond what was asked
</user_updates><self_check>
Before finalizing answers in sensitive contexts (legal, financial, safety):
- Re-scan for unstated assumptions
- Check for ungrounded numbers or claims
- Soften overly strong language ("always", "guaranteed")
- Explicitly state assumptions
</self_check>For data extraction tasks, always provide a schema:
<extraction_spec>
Extract data into this exact schema (no extra fields):
{
"field_name": "string",
"optional_field": "string | null",
"numeric_field": "number | null"
}
- If a field is not present in source, set to null (don't guess)
- Re-scan source for missed fields before returning
</extraction_spec><research_guidelines>
- Browse the web for: time-sensitive topics, recommendations, navigational queries, ambiguous terms
- Include citations after paragraphs with web-derived claims
- Use multiple sources for key claims; prioritize primary sources
- Research until additional searching won't materially change the answer
- Structure output with Markdown: headers, bullets, tables for comparisons
</research_guidelines>Without structure:
You're a financial analyst. Generate a Q2 report for investors. Include Revenue, Margins, Cash Flow. Use this data: {{DATA}}. Make it professional and concise.With structure:
You're a financial analyst at AcmeCorp generating a Q2 report for investors.
<context>
AcmeCorp is a B2B SaaS company. Investors value transparency and actionable insights.
</context>
<data>
{{DATA}}
</data>
<instructions>
1. Include sections: Revenue Growth, Profit Margins, Cash Flow
2. Highlight strengths and areas for improvement
3. Use concise, professional tone
</instructions>
<output_format>
- Use bullet points with metrics and YoY changes
- Include "Action:" items for areas needing improvement
- End with 2-3 bullet Outlook section
</output_format>When adapting prompts across models or versions:
| Technique | Tag Pattern | Use Case |
|---|---|---|
| Separate sections | <context>, <instructions>, <data> | Any complex prompt |
| Control length | <output_spec> with word/bullet limits | Prevent verbosity |
| Prevent drift | <constraints> with explicit "do NOT" | Feature creep |
| Handle uncertainty | <uncertainty_handling> | Factual queries |
| Chain of thought | <thinking>, <answer> | Reasoning tasks |
| Extraction | <schema> with JSON structure | Data parsing |
| Research | <research_guidelines> | Web-enabled agents |
| Self-check | <self_check> | High-risk domains |
| Tool usage | <tool_usage_rules> | Agentic systems |
| Eagerness control | <persistence>, <context_gathering> | Agent autonomy |
| Persona | <role> + behavioral constraints | Tone & style |
Comprehensive catalog of prompting techniques. Full details, examples, and academic references in references/prompting-techniques.md.
| Technique | Use Case |
|---|---|
| Zero-Shot Prompting | Direct task execution without examples; classification, translation, summarization |
| Few-Shot Prompting | In-context learning via exemplars; format control, label calibration, style matching |
| Chain-of-Thought (CoT) | Step-by-step reasoning; arithmetic, logic, commonsense reasoning tasks |
| Meta Prompting | LLM as orchestrator delegating to specialized expert prompts; complex multi-domain tasks |
| Self-Consistency | Sample multiple CoT paths, pick majority answer; boost accuracy on math & reasoning |
| Generated Knowledge | Generate relevant knowledge first, then answer; commonsense & factual QA |
| Prompt Chaining | Break complex tasks into sequential subtasks; document analysis, multi-step workflows |
| Tree of Thoughts (ToT) | Explore multiple reasoning branches with lookahead/backtracking; planning, puzzles |
| RAG | Retrieve external documents before generating; knowledge-intensive tasks, fresh data |
| ART (Auto Reasoning + Tools) | Auto-select and orchestrate tools with CoT; tasks requiring calculation, search, APIs |
| APE (Auto Prompt Engineer) | LLM generates and scores candidate prompts; prompt optimization at scale |
| Active-Prompt | Identify uncertain examples, annotate selectively for CoT; adaptive few-shot |
| Directional Stimulus | Add a hint/keyword to guide generation direction; summarization, dialogue |
| PAL (Program-Aided LM) | Generate code instead of text for reasoning; math, data manipulation, symbolic tasks |
| ReAct | Interleave reasoning traces with tool actions; search, QA, decision-making agents |
| Reflexion | Agent self-reflects on failures with verbal feedback; iterative improvement, debugging |
| Multimodal CoT | Two-stage: rationale generation then answer with text+image; visual reasoning tasks |
| Graph Prompting | Structured graph-based prompts; node classification, relation extraction, graph tasks |
LLM settings, prompt elements, formatting, and practical examples — see references/prompting-introduction.md. Covers:
Adversarial attacks, factuality issues, and bias mitigation — see references/prompting-risks.md. Covers:
When asked to audit, review, or improve a prompt, follow this workflow. Full checklist with per-check references: prompt-audit-checklist.md.
| # | Dimension | What to Check |
|---|---|---|
| 1 | Clarity & Specificity | Task definition, success criteria, audience, output format, conflicting constraints |
| 2 | Structure & Formatting | Section separation (XML tags), prompt smells (monolithic, mixed layers, negative bias) |
| 3 | Safety & Security | Control/data separation, secrets in prompt, injection resilience, tool permissions |
| 4 | Hallucination & Factuality | Role framing, grounding, citation-without-sources, uncertainty handling |
| 5 | Context Management | Info placement (not buried in middle), context size, RAG doc count, re-grounding |
| 6 | Maintainability & Debt | Hardcoded values, regenerated logic, model pinning, testability |
| 7 | Model-Specific Fit | Model-specific params and gotchas (see Model-Specific Guides below) |
| 8 | Evaluation Readiness | Eval criteria, adversarial test cases, schema enforcement, monitoring |
Three complementary layers — use the one matching your need:
Deep-dives by category — root causes, mechanisms, prevention checklists (from "The Architecture of Instruction", 2026):
| Mistake Category | Key Issues | Reference |
|---|---|---|
| Hallucinations & Logic | Ambiguity-induced confabulation, automation bias, overloaded prompts, logical failures in verification tasks, no role framing | mistakes-hallucinations.md |
| Structural Fragility | Formatting sensitivity (up to 76pp variance), reproducibility crisis, prompt smells catalog (6 anti-patterns), deliberation ladder | mistakes-structure.md |
| Context Rot | "Lost in the middle" U-shaped attention, RAG over-retrieval, naive data loading, context engineering shift | mistakes-context.md |
| Prompt Debt | Token tax of regenerative code, debt taxonomy (prompt/hyperparameter/framework/cost), multi-agent solutions, automated repair | mistakes-debt.md |
| Security | Direct/indirect injection, jailbreaking, system prompt leakage (OWASP LLM07:2025), RAG poisoning, multimodal injection, adversarial suffixes | mistakes-security.md |
Quick reference — 18-category taxonomy with MRPs, risk scores, case studies, action items: failure-taxonomy.md. Start here for an overview or to prioritize which categories to address first. Covers: control-plane vs data-plane model, heuristic risk scoring, real-world incidents (EchoLeak CVE-2025-32711, Mata v. Avianca, Samsung shadow AI).
How to measure & test — eval metrics, CI gating, red-teaming, tooling: evaluation-redteaming.md. Covers: TruthfulQA, FActScore, SelfCheckGPT, PromptBench, AILuminate, LLM-as-judge pitfalls, guardrail libraries, open research questions.
Each model family has unique parameters, gotchas, and patterns. Consult the reference for your target model:
reasoning_effort, text.verbosity, named tools, agentic prompting, completeness/verification contracts, compaction, and migration pathsmax effort and Pro mode, Programmatic Tool Calling, persisted reasoning, explicit caching, retrieval budgets, long-running state, frontend and visual verification, and migration workflowthinking_budget vs thinking_level, constraint placement (end of prompt), persona priority, function calling, structured output, multimodal, image generation© CodeAlive-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 20 other files (references) in skills/prompt-engineering of CodeAlive-AI/ai-driven-development.
Open the folder on GitHubat commit 25b7b1d
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in CodeAlive-AI/ai-driven-development, which our catalogue first saw on October 7, 2026.
Prompt Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Prompt Engineering this skillCodeAlive-AI/ai-driven-development | 155 | 1 repos | ~4.1k | Automated safety check: Pass | MIT | |
| Prompt Improverseverity1/claude-code-prompt-improver | 1.9k | 2 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Prompt Engineering Patternsynulihao/AgentSkillOS | 617 | 14 repos | ~1.7k | Automated safety check: Pass | None | |
| Patch CreationPiebald-AI/tweakcc | 2.5k | — | ~1.6k | Automated safety check: Pass | MIT | |
| LLM Application DevMoizIbnYousaf/ai-agent-skills | 1.1k | 2 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Senior Prompt Engineermaslennikov-ig/claude-code-orchestrator-kit | 259 | 4 repos | ~1.4k | Automated safety check: Pass | Custom licence |
severity1/claude-code-prompt-improver
This skill enriches vague prompts with targeted research and clarification before execution.
ynulihao/AgentSkillOS
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production.
Piebald-AI/tweakcc
Create and register new patches for tweakcc. An agent skill from Piebald-AI/tweakcc.
MoizIbnYousaf/ai-agent-skills
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration.
maslennikov-ig/claude-code-orchestrator-kit
Provides reference guides and Python scripts for prompt optimization, RAG evaluation, and agent orchestration when building or tuning LLM systems.
baskduf/FableCodex
Apply a Claude Fable 5 inspired operating style inside Codex.
CodeAlive-AI/ai-driven-development
Investigate GitHub repository history before risky code changes using git blame/log, GitHub PRs, review comments, squash/rebase/cherry-pick/rename heuristics, and cited evidence.
CodeAlive-AI/ai-driven-development
Create, publish, delete, and submit plugins for coding agents (Claude Code, OpenCode, Devin CLI/Desktop).
CodeAlive-AI/ai-driven-development
Deep research over the Semantic Scholar Graph API. An agent skill from CodeAlive-AI/ai-driven-development.
CodeAlive-AI/ai-driven-development
A skill your agent uses when testing Windows 11 desktop apps (WinForms/WPF/UWP) via UFO UIA/Win32 automation MCP.
CodeAlive-AI/ai-driven-development
Audit and improve repositories for reliable agentic work across Codex and Codex App, Claude Code, and OpenCode.
CodeAlive-AI/ai-driven-development
Manage hooks and automation for coding agents (Claude Code, Codex CLI, OpenCode, Devin CLI/Desktop).
Categories
Universal prompt engineering techniques for any LLM. An agent skill from CodeAlive-AI/ai-driven-development. Prompt Engineering is an agent skill from CodeAlive-AI/ai-driven-development. Universal prompt engineering techniques for any LLM.
Prompt Engineering fits situations like: reviewing prompts for AI models; requests like improve this prompt; write a system prompt; optimize my instructions.
Run `npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a claude-code`. Or copy the skill folder (skills/prompt-engineering in CodeAlive-AI/ai-driven-development) into .claude/skills/prompt-engineering in your project. Claude Code loads it when a task matches its description.
Run `npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a codex`. Or copy the skill folder (skills/prompt-engineering in CodeAlive-AI/ai-driven-development) into .agents/skills/prompt-engineering in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CodeAlive-AI/ai-driven-development --skill prompt-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-engineering, .gemini/skills/prompt-engineering, .github/skills/prompt-engineering and .opencode/skills/prompt-engineering in your project.
SKILL.md names no scripts, command-line tools or credentials: Prompt Engineering is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Prompt Engineering is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.1k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 73k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Prompt Engineering: Prompt Improver (severity1/claude-code-prompt-improver, 1.9k stars), Prompt Engineering Patterns (ynulihao/AgentSkillOS, 617 stars), Patch Creation (Piebald-AI/tweakcc, 2.5k stars) and LLM Application Dev (MoizIbnYousaf/ai-agent-skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
CodeAlive-AI (a GitHub organization) maintains it in CodeAlive-AI/ai-driven-development, which has 155 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 6, 2026.
Source: CodeAlive-AI/ai-driven-development on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.