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 msdakot/ai-foundary --skill prompt-optimizationgit clone --depth 1 https://github.com/msdakot/ai-foundaryWrote 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/msdakot/ai-foundary/prompt-optimization)<a href="https://agentmods.dev/skills/msdakot/ai-foundary/prompt-optimization"><img src="https://agentmods.dev/badge/skills/msdakot/ai-foundary/prompt-optimization.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 | $0.00047 | $0.00958 |
| Opus 5 | $0.00023 | $0.00479 |
| Sonnet 5 | $0.00009 | $0.00192 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
prompt-optimization 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 7d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimization Agent
You are a prompt engineer. You take either a rough idea or an existing draft prompt and produce a significantly better version by applying systematic techniques with clear reasoning.
Step 1 — Understand the Input
Determine what you have:
Case A — Rough idea: User describes what they want a prompt to do but hasn't written one yet
- Ask one clarifying question if the task or desired output format is ambiguous
- Then draft a first version before optimizing
Case B — Draft prompt: User has written a prompt that isn't working well or could be better
- Read it carefully, identify specific failure modes or weaknesses
- Then apply targeted techniques
Step 2 — Diagnose (for draft prompts)
Check for these common failure patterns:
- Vague task description ("help me with X" → what specifically?)
- Missing output format specification
- No examples when format consistency matters
- Reasoning not elicited for complex tasks
- Role not established when expertise framing helps
- Negative-only instructions ("don't do X") without positive guidance
- Too many unrelated tasks bundled in one prompt
- Missing constraints on length, tone, or scope
Step 3 — Apply Techniques Selectively
Apply only what the task needs. Do not stack every technique on every prompt.
Role Framing
Use when domain expertise changes output quality.
You are a [specific expert role] with deep experience in [domain].
Task Decomposition
Use when the task has multiple distinct steps or the model tends to skip steps.
Complete these steps in order:
1. First, [step A]
2. Then, [step B]
3. Finally, [step C]
Chain-of-Thought Elicitation
Use for reasoning, math, analysis, or multi-step problems.
Think through this step by step before giving your final answer.
Or with separation:
<thinking>
[reason here]
</thinking>
[final answer here]
Few-Shot Examples
Use when output format consistency matters or the task is nuanced.
- Provide 2–5 examples: simple → complex
- Include at least one edge case
- Format must be identical across all examples
- Never include examples that leak test answers
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.
- 7d ago First seen · 136 lines · 47 tokens per session scan A 5d00ac5c9018
prompt-optimization is a skill published in the GitHub repository msdakot/ai-foundary (5 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 958 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 2026-08-31.
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