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 agentic-dev3o/devx-plugins --skill prompt-optimizergit clone --depth 1 https://github.com/agentic-dev3o/devx-pluginsWrote 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/agentic-dev3o/devx-plugins/prompt-optimizer)<a href="https://agentmods.dev/skills/agentic-dev3o/devx-plugins/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/agentic-dev3o/devx-plugins/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/agentic-dev3o/devx-plugins/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/agentic-dev3o/devx-plugins/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 | $0.00099 | $0.01346 |
| Opus 5.5 | $0.00040 | $0.00538 |
| Sonnet 5.5 | $0.00020 | $0.00269 |
| Haiku 4.5 | $0.00010 | $0.00135 |
Grade A, and why
optimizing-prompts 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 1mo 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer
Target: $ARGUMENTS (path to a prompt file, agent definition, or pasted prompt text)
Workflow
Progress checklist:
Prompt Optimization:
- [ ] Step 1: Capture the target prompt
- [ ] Step 2: Identify intent, model, and runtime
- [ ] Step 3: Diagnose against eight techniques
- [ ] Step 4: Rewrite the prompt
- [ ] Step 5: Produce before/after report with rationale
Step 1: Capture the Target Prompt
Determine whether $ARGUMENTS is:
- A file path → read the file
- An agent definition (markdown with frontmatter, JSON/YAML) → extract the system prompt and any few-shot examples
- Inline prompt text → use as-is
- A directory → list it and ask the user to pick one prompt to optimize
If the prompt is part of a larger agent (tools, examples, memory layout), capture the surrounding shape but keep the rewrite scoped to the prompt itself. Do not rewrite tool definitions in this skill — the audit skill flags those, and tool rewrites belong in their own pass.
Step 2: Identify Intent, Model, and Runtime
Before optimizing, answer:
- What does the prompt do? Classification, extraction, generation, agent loop, chat?
- Who is the end user? Internal engineer, customer, batch pipeline?
- What model runs it? Claude Opus 4.7 / Sonnet 4.6 / Haiku 4.5 / GPT-x / etc. — affects defaults (verbosity, thinking, effort) and which features apply (caching, adaptive thinking, structured outputs).
- Is this interactive or autonomous? Single-turn API, multi-turn chat, long-horizon agent? Verbosity and update-frequency guidance differs.
If any of these are ambiguous and would materially change the rewrite, ask one focused question. Do not ask if the answer is inferable from the prompt itself.
Step 3: Diagnose Against Eight Techniques
For each technique below, read the matching reference and decide whether the current prompt applies it well, partially, or not at all. Note the gap concretely (quote the offending text or note its absence).
What ships with it
8 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.
- 1mo ago First seen · 139 lines · 99 tokens per session scan A db62bf3bdebb
optimizing-prompts is a skill published in the GitHub repository agentic-dev3o/devx-plugins (12 stars, last pushed 10d ago), licensed MIT. It adds 99 tokens to every session and 1,346 once invoked, about $0.0004 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 2026-08-30.
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