Borrowing it
Nothing to install: this file belongs to extra-org/extra. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/extra-org/extra/main/.claude/skills/prompt-rendering/SKILL.mdgit clone --depth 1 https://github.com/extra-org/extraWrote 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/extra-org/extra/prompt-rendering)<a href="https://agentmods.dev/skills/extra-org/extra/prompt-rendering"><img src="https://agentmods.dev/badge/skills/extra-org/extra/prompt-rendering/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/extra-org/extra/prompt-rendering"><img src="https://agentmods.dev/badge/skills/extra-org/extra/prompt-rendering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00032 | $0.00475 |
| Opus 5.5 | $0.00013 | $0.00190 |
| Sonnet 5.5 | $0.00006 | $0.00095 |
| Haiku 4.5 | $0.00003 | $0.00047 |
Grade A, and why
prompt-rendering 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 2mo 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
Skill: Prompt Rendering
When to use this skill
Use this when working on prompt templates, template loading/caching, or
per-request rendering. Primary task: tasks/0005-prompt-rendering.md.
Files to read first
AGENTS.mddocs/PROMPT_RENDERING.mddocs/adr/0004-prompts-are-templates-rendered-per-request.mddocs/RUNTIME_LIFECYCLE.md
Architecture rules
- Prompt files are templates with placeholders, not finished text.
- Parsed templates may be cached; rendered prompts are never globally cached.
- Values are resolved per request from the
ExecutionContext. - Missing required variables fail loudly with a named error.
- Templates are data, not code — no execution inside a template.
- Prompt text is not a security boundary.
Implementation rules
- Implement loading/caching/rendering in
src/agentplatform/prompts. - Cache the parsed template keyed by path + version; never cache rendered output.
- Use strict rendering: undeclared/missing variables raise errors.
- Pull values only from the
ExecutionContext(which is populated by the resolver plugins), not from globals. - Keep the source declaration (YAML) and resolution (runtime) separated.
Validation checklist
- Parsed templates cached; rendered prompts not cached globally.
- Missing required variable → clear, named error.
- No executable logic in templates.
- No secrets in prompt files.
- Tests cover successful render, missing variable, and cache behavior.
-
make checkpasses.
Common mistakes to avoid
- Caching rendered prompts (data-leak risk across tenants/requests).
- Silently substituting blanks for missing variables.
- Embedding logic/secrets in templates.
- Treating prompt instructions as enforcement.
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.
- 2mo ago First seen · 61 lines · 32 tokens per session scan A 701f9e6a813e
prompt-rendering is a skill published in the GitHub repository extra-org/extra (117 stars, last pushed 5d ago), licensed MIT. It adds 32 tokens to every session and 475 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
skill-meta-prompt
Craft better prompts using proven optimization techniques — use when your prompt needs refinement.
prompt-enhancer
Transform poor or overly simple prompts with expert-level framing. Use when the user explicitly asks to improve, refine, or rewrite a prompt, or wants help framing a request for another AI system. Do NOT use for authoring, reviewing, or migrating system prompts or skills targeting a specific Claude model.
ambitious-model-use
Use when the user asks to be more ambitious with an AI task: aim at the outcome behind it, work it like a field-leading expert from raw data, or bring in other fields. Do NOT use for plain prompt rewording; use prompt-enhancer.
llm-prompting-guide
Prompt format rules for generative video and music models. Use when writing or reviewing a prompt for MiniMax H3 (text/image/reference-to-video with native audio) or MiniMax Music 3 (song generation from caption plus lyrics), in ComfyUI or elsewhere. Do NOT use for chat-assistant prompts, or for generative models not…
prompt-review
This skill should be used when the user asks to "review the prompt", "audit the system prompt", "check prompt quality", "inspect what the LLM sees", "debug prompt issues", or "find prompt engineering problems". Pulls the live rendered prompt via the API, explains how it's composed, and reviews it for issues.
prompt-optimizer
Advanced prompt optimization and composition system for all prompt types (system prompts, task-specific, creative, technical, agentic). Use when users need to refine, optimize, or transform prompts to achieve better AI outputs. Triggers include requests to improve prompts, make prompts more effective, analyze prompt…