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 skills add saffron-health/libretto --skill promptinggit clone --depth 1 https://github.com/saffron-health/librettoWrote 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/saffron-health/libretto/prompting)<a href="https://agentmods.dev/skills/saffron-health/libretto/prompting"><img src="https://agentmods.dev/badge/skills/saffron-health/libretto/prompting/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/saffron-health/libretto/prompting"><img src="https://agentmods.dev/badge/skills/saffron-health/libretto/prompting.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 | $0.00031 | $0.00455 |
| Opus 5 | $0.00015 | $0.00228 |
| Sonnet 5 | $0.00006 | $0.00091 |
| Haiku 4.5 | $0.00003 | $0.00046 |
Grade A, and why
prompting 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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- prompting — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompting
Philosophy
LLMs are intelligent by default. System prompts set direction and impose constraints, not explain reasoning.
Start minimal. Observe failures. Add targeted fixes. Every instruction must justify its token cost by solving a real problem.
Do not explain existing capabilities, list obvious practices, add preventive instructions, or repeat information.
Structure
Use markdown sections and paragraphs. Each section describes one behavior or constraint.
State what to do or avoid. Explain why if non-obvious. Show correct behavior with examples.
Formatting
Headings up to level 3. Plain paragraphs. No bold, italics, or emojis. Code blocks for commands. Lists only for distinct enumerable items.
Examples
Wrap examples in <example> tags with user/assistant prefixes. One pair per tag.
<example>
user: What's the capital of France?
assistant: Paris
</example>
Use brackets for tool actions instead of showing invocations:
<example>
user: Find all TODO comments
assistant: [searches codebase]
Found 3 TODOs: ...
</example>
Include
Behaviors the model gets wrong by default. Domain constraints. Output format requirements. Safety boundaries. Tool integrations.
Omit
Reasoning instructions. Problem-solving approaches. Common sense behaviors. Ethical guidelines. Capability descriptions.
Iteration
Start minimal. Test with real inputs. Identify failures. Add targeted fixes. Remove unnecessary instructions.
Track which instructions prevent which failures. If you cannot identify the specific problem an instruction solves, remove it.
Model-Specific Guidance
Consult references/ for model-specific patterns:
- references/claude.md - XML structure, countering sycophancy, trigger words, parallel execution
- references/gpt.md - Contradiction sensitivity, role hierarchy, verbosity control, metaprompting
- references/gemini.md - Conciseness, tool explanations, library checks, context placement
- references/codex.md - OpenAI Codex models, tool implementations, autonomy patterns, compaction
What ships with it
4 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.
- 10d ago First seen · 70 lines · 31 tokens per session scan A 9c7ca553edd2
prompting is a skill published in the GitHub repository saffron-health/libretto (889 stars, last pushed 20d ago), licensed MIT. It adds 31 tokens to every session and 455 once invoked, about $0.0002 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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