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 claude-hangar/claude-hangar --skill prompt-optimizergit clone --depth 1 https://github.com/claude-hangar/claude-hangarWrote 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/claude-hangar/claude-hangar/prompt-optimizer)<a href="https://agentmods.dev/skills/claude-hangar/claude-hangar/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/claude-hangar/claude-hangar/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/claude-hangar/claude-hangar/prompt-optimizer"><img src="https://agentmods.dev/badge/skills/claude-hangar/claude-hangar/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 | $0.00046 | $0.01381 |
| Opus 5 | $0.00023 | $0.00691 |
| Sonnet 5 | $0.00009 | $0.00276 |
| Haiku 4.5 | $0.00005 | $0.00138 |
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 10d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/prompt-optimizer — Prompt Analysis Pipeline
Six-phase advisory skill that takes a user's draft prompt or task description and produces an optimized version with the right skills, agents, and workflow.
Advisory only — this skill never executes the task. It produces an optimized prompt the user can then run.
Usage
/prompt-optimizer "Add authentication to my SvelteKit app"
/prompt-optimizer # Analyze the last user message
/prompt-optimizer --quick # Short version for experienced users
The Six Phases
Phase 1: Detect Project Context
Gather project metadata without reading the full codebase:
# Quick project detection
cat CLAUDE.md 2>/dev/null | head -30
cat package.json 2>/dev/null | node -e "const p=require('/dev/stdin'); console.log(p.name, Object.keys(p.dependencies||{}).slice(0,10))"
ls -d src/ app/ lib/ pages/ routes/ components/ 2>/dev/null
git remote -v 2>/dev/null | head -1
Output: Tech stack, framework, project type, repo context.
Phase 2: Detect User Intent
Parse the prompt to identify:
- Primary goal: What does the user want to achieve?
- Implicit requirements: What's assumed but not stated?
- Scope boundaries: What should NOT change?
- Success criteria: How will we know it's done?
Phase 3: Assess Scope
Classify the task:
| Size | Criteria | Recommended Approach |
|---|---|---|
| Small | Single file, < 30 min | Direct implementation |
| Medium | 2-5 files, clear scope | Plan then implement |
| Large | 6+ files, architectural | Full planning phase with planner agent |
| Ambiguous | Unclear scope or requirements | Brainstorming first |
Phase 4: Match Hangar Components
Scan available skills and agents for the best match:
# List available skills and their descriptions
for skill in core/skills/*/SKILL.md; do
head -5 "$skill" | grep "description:"
done
# List available agents
for agent in core/agents/*.md; do
head -5 "$agent" | grep "description:"
done
What ships with it
1 file 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.
- 10d ago First seen · 182 lines · 46 tokens per session scan A ec68a0f7a081
prompt-optimizer is a skill published in the GitHub repository claude-hangar/claude-hangar (1 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 1,381 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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