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 hanamizuki/solopreneur --skill senior-prompt-engineergit clone --depth 1 https://github.com/hanamizuki/solopreneurWrote 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/hanamizuki/solopreneur/senior-prompt-engineer)<a href="https://agentmods.dev/skills/hanamizuki/solopreneur/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/hanamizuki/solopreneur/senior-prompt-engineer/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/hanamizuki/solopreneur/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/hanamizuki/solopreneur/senior-prompt-engineer.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 | $0.00091 | $0.02242 |
| Opus 5 | $0.00046 | $0.01121 |
| Sonnet 5 | $0.00018 | $0.00448 |
| Haiku 4.5 | $0.00009 | $0.00224 |
Grade A, and why
senior-prompt-engineer 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Prompt Engineer
Eval-driven prompt engineering, RAG quality measurement, and agent workflow validation. Everything here is model-agnostic by design: techniques are framed by what they do, not by which model generation they were observed on, and the tools never hardcode model IDs or pricing — you supply your provider's current rates when you want dollar figures.
Operating Rules
- Never change a prompt without a baseline. Capture metrics first (
--analyze --output baseline.json), then compare every iteration against it. - Eval set before optimization. 10–20 representative cases with expected outputs minimum. If the user has no eval set, build one with them before touching the prompt — optimizing against vibes is the #1 failure mode.
- Prefer platform features over prompt hacks. If the provider offers native structured outputs / JSON schema enforcement, tool-use APIs, or prompt caching, use those instead of "respond ONLY with JSON" incantations. Prompt-level format enforcement is the fallback, not the default.
- Current-generation models need less scaffolding. Don't add chain-of-thought boilerplate, role framing, or few-shot examples reflexively — frontier models often do worse with redundant scaffolding. Add each element only when the eval set shows it helps.
- Cost numbers are always user-supplied. Look up the provider's current per-Mtok pricing and pass it via
--price-per-mtok(never trust a cached price table — including any you remember).
Tools (exact CLIs, all stdlib)
1. Prompt Optimizer — scripts/prompt_optimizer.py
Static analysis: token estimate, clarity/structure scores (0–100), ambiguity + redundancy detection, few-shot example extraction.
# Full analysis (human-readable report)
python3 scripts/prompt_optimizer.py prompt.txt --analyze
# Save machine-readable baseline for later comparison
python3 scripts/prompt_optimizer.py prompt.txt --analyze --json --output baseline.json
# Token estimate; cost only if you supply your provider's current rate
python3 scripts/prompt_optimizer.py prompt.txt --tokens --model claude --price-per-mtok 3.00
# Whitespace/redundancy-trimmed version
python3 scripts/prompt_optimizer.py prompt.txt --optimize --output optimized.txt
# Extract Input/Output few-shot pairs to JSON
python3 scripts/prompt_optimizer.py prompt.txt --extract-examples --output examples.json
# Compare a revision against the saved baseline
python3 scripts/prompt_optimizer.py optimized.txt --analyze --compare baseline.json
What ships with it
7 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 · 140 lines · 91 tokens per session scan A aac883ec95c4
senior-prompt-engineer is a skill published in the GitHub repository hanamizuki/solopreneur (148 stars, last pushed 26d ago), licensed MIT. It adds 91 tokens to every session and 2,242 once invoked, about $0.0005 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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