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/opencoven/coven/prompt-engineernpx skills add OpenCoven/coven --skill prompt-engineergit clone --depth 1 https://github.com/OpenCoven/covenWrote 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/opencoven/coven/prompt-engineer)<a href="https://agentmods.dev/skills/opencoven/coven/prompt-engineer"><img src="https://agentmods.dev/badge/skills/opencoven/coven/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 5.1 | $0.00168 | $0.01049 |
| Opus 5 | $0.00084 | $0.00524 |
| Sonnet 5 | $0.00034 | $0.00210 |
| Haiku 4.5 | $0.00017 | $0.00105 |
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 6d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
Craft and optimize LLM prompts for maximum effectiveness, consistency, and cost efficiency.
Core Workflow
1. Requirements Analysis
Before writing or editing any prompt:
- Identify the use case — what task the prompt must accomplish
- Define success criteria — accuracy target, format requirements, tone
- Understand constraints — token budget, latency ceiling, cost limit, model choice
- Review existing prompts and their failure modes (if any)
- Determine safety requirements — input validation, output filtering, injection defense
2. Prompt Design
Select the appropriate pattern based on task complexity. See references/patterns.md for detailed guidance on each.
| Complexity | Pattern | When to Use |
|---|---|---|
| Simple | Zero-shot | Clear task, model already knows the domain |
| Medium | Few-shot | Specific format or style needed |
| Complex | Chain-of-thought | Multi-step reasoning required |
| Branching | Tree-of-thought | Multiple valid approaches to explore |
| Agentic | ReAct | Tool use + reasoning interleaved |
| Safety | Constitutional AI | Output must pass ethical/policy filters |
Design principles:
- Instruction clarity — state the task, constraints, and output format explicitly
- Minimal tokens — every token must earn its place; compress without losing meaning
- Modular structure — separate system prompt, context, instructions, and examples
- Variable placeholders — use
{{variable}}for dynamic content injection - Error recovery — include fallback instructions for ambiguous or invalid input
3. Optimization
Iterate on prompts to reduce cost and improve quality:
- Token reduction — remove redundant phrasing, compress examples, use abbreviations the model understands
- Context compression — summarize long context; only include what the model needs for the current step
- Output constraints — specify format (JSON, markdown, list) to reduce parsing overhead
- Caching — identify static prompt sections that can be cached across calls
- Batch processing — group similar requests when possible
What ships with it
3 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.
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.
- 6d ago First seen · 110 lines · 168 tokens per session scan A d9448b6c6c55
prompt-engineer is a skill published in the GitHub repository OpenCoven/coven (46 stars, last pushed yesterday), licensed MIT. It adds 168 tokens to every session and 1,049 once invoked, about $0.0008 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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