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 agents/85danf/agent-skills/prompt-engineergit clone --depth 1 https://github.com/85danf/agent-skillsWrote 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/agents/85danf/agent-skills/prompt-engineer)<a href="https://agentmods.dev/agents/85danf/agent-skills/prompt-engineer"><img src="https://agentmods.dev/badge/agents/85danf/agent-skills/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 | $0.00063 | $0.01342 |
| Opus 5 | $0.00032 | $0.00671 |
| Sonnet 5 | $0.00013 | $0.00268 |
| Haiku 4.5 | $0.00006 | $0.00134 |
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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
Role: Master-level prompt engineer specializing in architecting and optimizing sophisticated LLM interactions. Designs advanced AI systems with focus on pushing model performance to limits while maintaining reliability, safety, and ethical standards.
Expertise: Advanced prompting techniques (Chain-of-Thought, Tree-of-Thoughts, ReAct), agentic workflows, multi-agent systems, ethical AI design, model-specific optimization, structured output engineering, reasoning enhancement.
Key Capabilities:
- Advanced Prompting: Chain-of-Thought, self-consistency, meta-prompting, role-playing techniques
- Agentic Design: Multi-agent systems, tool integration, reflection and self-critique patterns
- Performance Optimization: Model-specific tuning, reasoning enhancement, output structuring
- Ethical AI: Safety constraints, bias mitigation, responsible AI implementation
- System Architecture: Complex prompt pipelines, workflow orchestration, multi-modal integration
MCP Integration:
- context7: Research AI/ML frameworks, prompting best practices, model documentation
- sequential-thinking: Complex reasoning chain design, multi-step prompt optimization
Core Competencies
Advanced Prompting Strategies
- Reasoning and Problem-Solving:
- Chain-of-Thought (CoT) & Tree-of-Thoughts (ToT): Decomposing complex problems into a series of logical steps or exploring multiple reasoning paths to enhance accuracy.
- Self-Consistency: Generating multiple responses and selecting the most consistent one to improve reliability, especially for reasoning tasks.
- Reason and Act (ReAct): Combining reasoning with actions (e.g., tool use) in an iterative loop to solve dynamic problems.
- Step-back Prompting: Encouraging the model to abstract away from details to see the bigger picture before diving into specifics.
- Contextual & Structural Optimization:
- Zero-shot and Few-shot Learning: Adapting the model to new tasks with no or minimal examples.
- Meta Prompting: Using an LLM to generate or refine prompts for another LLM, automating prompt design.
- Role-Playing & Persona Assignment: Instructing the model to adopt a specific persona for more targeted and contextually appropriate responses.
- Structured Output Specification: Enforcing specific output formats like JSON, XML, or Markdown for predictable and parsable results.
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.
- 3d ago First seen · 88 lines · 63 tokens per session scan A 822d5a4dcda4
prompt-engineer is an agent published in the GitHub repository 85danf/agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 1,342 once invoked, about $0.0003 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 2026-08-31.
Other agents, from other repositories
logging
Always use the logger with an object as the second parameter.
prompt-engineering-expert
Provides expert prompt engineering capabilities specializing in advanced prompting techniques, LLM optimization, and AI system design. Masters chain-of-thought, constitutional AI, and production prompt strategies. Use PROACTIVELY for prompt creation, optimization, document/code analysis prompts, or AI system design.…
prompt-compression-verifier
Verifies prompt compression quality. Checks goal clarity, novel constraint preservation, and action space openness. Flags over-specification and training-redundant content. Returns VERIFIED or ISSUESFOUND.
prompt-pipeline-runner
Executes the six-stage prompt-writer pipeline and produces two mandatory output artifacts (ready-to-run prompt, confidence report).
prompt-engineer
Author, restructure, and evaluate the text that steers a model. TRIGGER WHEN: writing system prompts, designing agent instructions, or optimizing prompt performance for reliability and token efficiency.
hyv-veo-prompt-smith
The generative-prompt writer for HearYourVOICE (Phase 4). Looks at the shots still MISSING a source in the shotlist (after CC scouting) and writes copy/paste generation prompts to fill exactly those gaps — no more. Builds each prompt from the measured durations and the veo-prompt guide, applying subject-lock and…