Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/solatis/claude-config/prompt-engineernpx skills add solatis/claude-config --skill prompt-engineergit clone --depth 1 https://github.com/solatis/claude-configWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/solatis/claude-config/prompt-engineer)<a href="https://agentmods.dev/skills/solatis/claude-config/prompt-engineer"><img src="https://agentmods.dev/badge/skills/solatis/claude-config/prompt-engineer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00029 | $0.00289 |
| Opus 5 | $0.00015 | $0.00144 |
| Sonnet 5 | $0.00006 | $0.00058 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
prompt-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Prompt Engineer
When this skill activates, IMMEDIATELY invoke the script. The script IS the workflow.
Invocation
Start with step 1 (triage) to determine scope:
Then continue with determined scope:
| Argument | Required | Description |
|---|---|---|
--step |
Yes | Current step (1 = triage, 2-6 = workflow) |
--scope |
For 2+ | Required for steps 2-6. Determined by step 1. |
Scopes
- single-prompt: One prompt file, general optimization
- ecosystem: Multiple related prompts that interact
- greenfield: No existing prompt, designing from requirements
- problem: Existing prompt(s) with specific issue to fix
Do NOT analyze or explore first. Run the script and follow its output.
What ships with it
60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- CLAUDE.md 696 B
- papers/.gitattributes 45 B
- papers/CATEGORY_DESCRIPTIONS.md 22 KB
- papers/CLAUDE.md 1.2 KB
- papers/context/augmentation/2021-01-17 What Makes Good In-Context Examples for GPT-3.md 35 KB
- papers/context/augmentation/2022-09-28 Generated Knowledge Prompting for Commonsense Reasoning.md 30 KB
- papers/context/augmentation/2023-07-14 Unified Demonstration Retriever for In-Context Learning.md 46 KB
- papers/context/augmentation/2023-10-21 Universal Self-Adaptive Prompting.md 46 KB
- papers/context/augmentation/2023-12-10 Finding Support Examples for In-Context Learning.md 40 KB
- papers/context/augmentation/2024-06-23 Diverse Demonstrations Improve In-context Compositional Generalization.md 51 KB
- papers/context/augmentation/CLAUDE.md 169 B
- papers/context/CLAUDE.md 529 B
- papers/context/reframing/2022-10-20 Rethinking the Role of Demonstrations - What Makes In-Context Learning Work.md 43 KB
- papers/context/reframing/2023-10-23 Context-faithful Prompting for Large Language Models.md 30 KB
- papers/context/reframing/2023-11-12 Large Language Models Understand and Can Be Enhanced by Emotional Stimuli.md 11 KB
- papers/context/reframing/2024-02-18 An Empirical Categorization of Prompting Techniques for Large Language Models - A Practitioner's Guide.md 50 KB
- papers/context/reframing/2024-03-14 Better Zero-Shot Reasoning with Role-Play Prompting.md 31 KB
- papers/context/reframing/2024-04-08 Customizing Language Model Responses with Contrastive In-Context Learning.md 28 KB
- papers/context/reframing/2024-04-18 Rephrase and Respond - Let Large Language Models Ask Better Questions for Themselves.md 40 KB
- papers/context/reframing/2024-06-20 Devil's Advocate - Anticipatory Reflection for LLM Agents.md 39 KB
- papers/context/reframing/2024-06-24 Mirror - A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning.md 52 KB
- papers/context/reframing/2024-08-08 Conversational Prompt Engineering.md 32 KB
- papers/context/reframing/2024-09-28 Code Prompting Elicits Conditional Reasoning Abilities in Text and Code LLMs.md 11 KB
- papers/context/reframing/2024-10-09 When A Helpful Assistant Is Not Really Helpful - Personas in System Prompts Do Not Improve Performances of Large Language Models.md 47 KB
- papers/context/reframing/2024-10-16 Lets Argue Both Sides - Argument Generation Can Force Small Models to Utilize Previously Inaccessible Reasoning Capabilities.md 47 KB
- papers/context/reframing/2024-11-01 Multi-expert Prompting Improves Reliability Safety and Usefulness of Large Language Models.md 57 KB
- papers/context/reframing/2024-12-03 Take a Step Back - Evoking Reasoning via Abstraction in Large Language Models.md 32 KB
- papers/context/reframing/2025-02-17 Large Language Models are Contrastive Reasoners.md 54 KB
- papers/context/reframing/2025-03-05 ExpertPrompting - Instructing Large Language Models to be Distinguished Experts.md 14 KB
- papers/context/reframing/2025-08-27 Principled Personas - Defining and Measuring the Intended Effects of Persona Prompting on Task Performance.md 22 KB
- papers/context/reframing/claude-prompt-engineering.md 1.7 KB
- papers/context/reframing/CLAUDE.md 156 B
- papers/correctness/CLAUDE.md 614 B
- papers/correctness/refinement/2024-06-01 Prompt Chaining or Stepwise Prompt - Refinement in Text Summarization.md 21 KB
- papers/correctness/refinement/2024-10-01 Iteration of Thought - Leveraging Inner Dialogue for Autonomous Large Language Model Reasoning.md 37 KB
- papers/correctness/refinement/2024-10-07 Progressive-Hint Prompting Improves Reasoning in Large Language Models.md 98 KB
- papers/correctness/refinement/2024-10-17 Think Thrice Before You Act - Progressive Thought Refinement in Large Language Models.md 32 KB
- papers/correctness/refinement/2025-02-11 Self-Harmonized Chain of Thought.md 37 KB
- papers/correctness/refinement/2025-03-25 Think Twice- Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking.md 21 KB
- papers/correctness/refinement/CLAUDE.md 148 B
- papers/correctness/sampling/2022-03-03 Fantastically Ordered Prompts and Where to Find Them - Overcoming Few-Shot Prompt Order Sensitivity.md 21 KB
- papers/correctness/sampling/2022-07-02 Rationale-Augmented Ensembles in Language Models.md 44 KB
- papers/correctness/sampling/2023-01-30 Complexity-Based Prompting for Multi-Step Reasoning.md 40 KB
- papers/correctness/sampling/2023-02-01 Synthetic Prompting - Generating Chain-of-Thought Demonstrations for Large Language Models.md 27 KB
- papers/correctness/sampling/2023-03-07 Self-Consistency Improves Chain of Thought Reasoning in Language Models.md 60 KB
- papers/correctness/sampling/2023-03-31 Fairness-guided Few-shot Prompting for Large Language Models.md 18 KB
- papers/correctness/sampling/2023-04-12 Boosted Prompt Ensembles for Large Language Models.md 40 KB
- papers/correctness/sampling/2023-08-23 PREFER - Prompt Ensemble Learning via Feedback-Reflect-Refine.md 35 KB
- papers/correctness/sampling/2023-08-24 Answering Questions by Meta-Reasoning over Multiple Chains of Thought.md 93 KB
- papers/correctness/sampling/2023-10-20 Getting MoRE out of Mixture of Language Model Reasoning Experts.md 10 KB
- papers/correctness/sampling/2023-10-23 Self-ICL - Zero-Shot In-Context Learning with Self-Generated Demonstrations.md 38 KB
- papers/correctness/sampling/2023-11-29 Universal Self-Consistency for Large Language Model Generation.md 25 KB
- papers/correctness/sampling/2023-12-03 Tree of Thoughts- Deliberate Problem Solving with Large Language Models.md 44 KB
- papers/correctness/sampling/2024-02-23 Diversity of Thought Improves Reasoning Abilities of LLMs.md 56 KB
- papers/correctness/sampling/2024-05-23 Reprompting - Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling.md 38 KB
- papers/correctness/sampling/2024-07-02 Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency.md 37 KB
- papers/correctness/sampling/2024-07-21 Active Prompting with Chain-of-Thought for Large Language Models.md 68 KB
- papers/correctness/sampling/2024-08-19 PEDAL - Enhancing Greedy Decoding with Large Language Models using Diverse Exemplars.md 21 KB
- papers/correctness/sampling/2024-10-19 Persona is a Double-edged Sword - Mitigating the Negative Impact of Role-playing Prompts in Zero-shot Reasoning Tasks.md 23 KB
- papers/correctness/sampling/2025-04-10 Refining Answer Distributions for Improved Large Language Model Reasoning.md 61 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 34 lines · 29 tokens per session scan A 3d6c3c26aff7
prompt-engineer is a skill published in the GitHub repository solatis/claude-config (904 stars, last pushed 5mo ago), licensed MIT. It adds 29 tokens to every session and 289 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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