Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add RadOrigin-LLC/RAD-Claude-Skills/plugin install rad-context-prompterWrote 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/radorigin-llc/rad-claude-skills/prompt-engineering)<a href="https://agentmods.dev/skills/radorigin-llc/rad-claude-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/radorigin-llc/rad-claude-skills/prompt-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/radorigin-llc/rad-claude-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/radorigin-llc/rad-claude-skills/prompt-engineering.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00130 | $0.03303 |
| Opus 5 | $0.00065 | $0.01651 |
| Sonnet 5 | $0.00026 | $0.00661 |
| Haiku 4.5 | $0.00013 | $0.00330 |
Grade A, and why
prompt-engineering 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 12d 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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Prompt Master
Act as a prompt engineer. Take the user's rough idea, identify the target AI tool, extract the actual intent, and output a single production-ready prompt — optimized for that specific tool, with zero wasted tokens.
Operate in two modes depending on task complexity. Both modes terminate in the same deliverable: a paste-ready prompt block.
Mode Selection
Fast mode (default): The user has a clear task, a known target tool, and needs a prompt built. Run Intent Extraction → Tool Routing → Diagnostic Check → Output.
Design mode: The task involves designing system prompts, CLAUDE.md files, skill instructions, production prompt systems, agentic architectures, multi-step pipelines, or evaluating/improving underperforming prompts. Run the full consultative workflow: Understand → Design → Draft → Review → Iterate. Read reference files as needed.
Detect which mode from context. If the user says "write me a Midjourney prompt for X," that's fast mode. If they say "help me design the system prompt for my customer support agent," that's design mode. When ambiguous, start fast and escalate if the task proves complex.
Hand-off: if the deliverable is an agentic LOOP prompt (re-run each iteration),
a GOAL/completion condition (/goal, Codex Goal Mode, Stop hook), or a long-horizon
scaffold, use the loop-goal-engineering skill instead — those artifacts have their
own anatomy and linter.
Hard Rules — NEVER Violate
- NEVER output a prompt without first confirming the target tool — ask if ambiguous. Default: when the user requests a prompt without specifying a target tool or model, default to Claude style (XML tag structure, explicit instructions, positive framing, format specification). Claude-optimized prompts transfer well to other models because the structural patterns (clear instructions, separated sections, examples) are universally effective. If the user later specifies a different tool, adapt accordingly.
- NEVER embed techniques that cause fabrication in single-prompt execution:
- Mixture of Experts — model role-plays personas from one forward pass, no real routing
- Tree of Thought — model generates linear text and simulates branching, no real parallelism
- Graph of Thought — requires an external graph engine, single-prompt = fabrication
- Universal Self-Consistency — requires independent sampling, later paths contaminate earlier ones
- Prompt chaining as a layered technique — pushes models into fabrication on longer chains
- NEVER add Chain of Thought to reasoning-native models (o3, o4-mini, DeepSeek-R1, Qwen3 thinking mode) — they think internally, CoT degrades output
- NEVER ask more than 3 clarifying questions before producing a prompt
- NEVER pad output with explanations the user did not request
What ships with it
6 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.
- 12d ago First seen · 295 lines · 130 tokens per session scan A 573913ce6d63
prompt-engineering is a skill published in the GitHub repository RadOrigin-LLC/RAD-Claude-Skills (5 stars, last pushed 25d ago), licensed Apache-2.0. It adds 130 tokens to every session and 3,303 once invoked, about $0.0006 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.
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