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/ckokoski/authoragent/prompt-optimizernpx skills add Ckokoski/AuthorAgent --skill prompt-optimizergit clone --depth 1 https://github.com/Ckokoski/AuthorAgentWrote 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/ckokoski/authoragent/prompt-optimizer)<a href="https://agentmods.dev/skills/ckokoski/authoragent/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/ckokoski/authoragent/prompt-optimizer.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 | $0.00020 | $0.01412 |
| Opus 5 | $0.00010 | $0.00706 |
| Sonnet 5 | $0.00004 | $0.00282 |
| Haiku 4.5 | $0.00002 | $0.00141 |
Grade A, and why
prompt-optimizer scanned grade A with 1 finding 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.
Asks the agent to reveal its instructionslowSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- `show prompt winners` — See which optimizations have been promoted Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer — Core Skill
AuthorAgent's prompts are its most important tool. This skill continuously optimizes them based on what actually produces good results — not guesswork, but measured outcomes.
The Problem
Every skill, goal step, and system prompt contains prompts. Some work great. Some produce mediocre results. Without measurement, you're flying blind. The Prompt Optimizer tracks which prompt formulations produce the best outputs and evolves them over time.
How It Works
Prompt Tracking
Every prompt sent to an AI provider is logged with its outcome:
{
"promptId": "p-347",
"timestamp": "2026-02-24T15:00:00Z",
"template": "Write a compelling book blurb for: {{description}}...",
"skill": "blurb-writer",
"taskType": "marketing",
"provider": "gemini",
"inputTokens": 450,
"outputTokens": 890,
"outcome": "accepted",
"userEdited": false,
"qualitySignals": {
"wordCount": 147,
"completeness": true,
"followedInstructions": true,
"userAccepted": true
}
}
Quality Signals
The optimizer watches for these signals:
Positive signals (prompt is working):
- User accepted output without edits
- Output matched requested format/length
- No follow-up "try again" or "that's not what I meant"
- User explicitly praised the result
- Output was saved to a file (user valued it enough to keep)
Negative signals (prompt needs improvement):
- User heavily edited the output
- User said "try again" or "not quite"
- Output was too long/short for the task
- AI produced an error or refusal
- Output missed key requirements from the prompt
- User abandoned the result
Prompt Evolution
When a prompt consistently underperforms, the optimizer creates variations:
Prompt Lab: "blurb-writer" skill
════════════════════════════════
Original (Score: 6.2/10 across 14 uses):
"Write a compelling book blurb for: {{description}}.
Create 3 versions: (1) short tagline, (2) back-cover
blurb (150 words), (3) Amazon description with HTML."
Variation A (Score: 7.8/10 across 6 uses):
"You are a bestselling book marketer. Write a blurb
for: {{description}}.
Rules: Hook in first sentence. No spoilers past Act 1.
End with a question or cliffhanger.
Format: tagline (10 words max), back cover (150 words),
Amazon listing (with <b> tags for emphasis)."
Variation B (Score: 8.1/10 across 4 uses):
"Study these bestselling blurbs for pacing and hooks:
[example 1], [example 2].
Now write a blurb for: {{description}} using the same
techniques. Output: tagline, 150-word back cover,
Amazon description."
→ RECOMMENDATION: Promote Variation B to primary.
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 · 182 lines · 20 tokens per session scan A 124d75bbe4e7
prompt-optimizer is a skill published in the GitHub repository Ckokoski/AuthorAgent (103 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 1,412 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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