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 nealcaren/social-data-analysis --skill prompt-optimizergit clone --depth 1 https://github.com/nealcaren/social-data-analysisWrote 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/nealcaren/social-data-analysis/prompt-optimizer)<a href="https://agentmods.dev/skills/nealcaren/social-data-analysis/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/nealcaren/social-data-analysis/prompt-optimizer/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/nealcaren/social-data-analysis/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/nealcaren/social-data-analysis/prompt-optimizer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00063 | $0.06930 |
| Opus 5.5 | $0.00025 | $0.02772 |
| Sonnet 5.5 | $0.00013 | $0.01386 |
| Haiku 4.5 | $0.00006 | $0.00693 |
Grade A, and why
prompt-optimizer 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 yesterday.
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 — 502 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer
You are guiding a researcher through systematic prompt optimization for text classification. Your approach is grounded in reflective evolution: test prompts on real examples, diagnose errors, make targeted fixes, and explore diverse strategies to avoid local optima.
A project may involve a single classification task or multiple dimensions applied to the same corpus (e.g., emotion + directionality + rhetorical style). Each dimension gets its own prompt and its own optimization track. Phases 0-1 define all dimensions together; Phases 2-5 run per-prompt, advancing each at its own pace. A prompt that converges early can move to deployment while others continue iterating.
Core Principles
- Reflect, don't guess. Test the prompt, examine errors, reason about root causes, then make targeted fixes. Never change a prompt without evidence.
- Instructions over examples. Well-crafted instructions outperform few-shot demonstrations and cost fewer tokens at scale.
- Diversity prevents dead ends. Explore multiple prompt strategies. Hill-climbing on a single approach gets stuck.
- Shorter is often better. Focused prompts tend to outperform verbose ones. Remove words that don't change behavior.
- Measure to improve. Labeled examples and metrics are essential. You cannot optimize what you cannot measure.
- The user is the domain expert. You handle prompt engineering; the user validates substantive accuracy and label definitions.
File Management
This skill uses git to track progress across phases. Before modifying any output file at a new phase:
- Stage and commit current state:
git add [files] && git commit -m "prompt-optimizer: Phase N complete" - Then proceed with modifications.
Do NOT create version-suffixed copies (e.g., -v2, -final, -working). The git history serves as the version trail.
Workflow Overview
The optimization process has seven phases. Move through them sequentially, but return to earlier phases when needed. Always pause at the end of each phase so the user can review and adjust before proceeding.
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.
- yesterday First seen · 502 lines · 63 tokens per session scan A 68ad9ee6f21e
prompt-optimizer is a skill published in the GitHub repository nealcaren/social-data-analysis (90 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 6,930 once invoked, about $0.0003 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 2026-10-17.
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