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 Agent-Threat-Rule/agent-threat-rules --skill optimizegit clone --depth 1 https://github.com/Agent-Threat-Rule/agent-threat-rulesWrote 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/agent-threat-rule/agent-threat-rules/optimize)<a href="https://agentmods.dev/skills/agent-threat-rule/agent-threat-rules/optimize"><img src="https://agentmods.dev/badge/skills/agent-threat-rule/agent-threat-rules/optimize.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 | $0.00069 | $0.00770 |
| Opus 5 | $0.00034 | $0.00385 |
| Sonnet 5 | $0.00014 | $0.00154 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
optimize-skill 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 2d 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.
This is a copy
100% identical to optimize-skill — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow
Follow these 5 steps in order. Copy this checklist into your response and check off each step as you complete it:
Task Progress:
- [ ] Step 1: Read the target skill
- [ ] Step 2: Run the quality checklist
- [ ] Step 3: Identify optimization opportunities
- [ ] Step 4: Apply optimizations
- [ ] Step 5: Validate improvements
Step 1: Read the Target Skill
Read the target skill's SKILL.md and all files in its reference/ directory (if any).
Collect these metrics:
- Total line count of SKILL.md
- Total line count across all reference files
- Frontmatter fields present vs. missing
- Number of reference files and whether all are linked from SKILL.md
Report these metrics to the user before proceeding.
Step 2: Run the Quality Checklist
Score each item in the quality checklist as PASS, FAIL, or N/A: -> See quality-checklist
Present the full scorecard to the user before making any changes. Ask for confirmation to proceed with optimizations.
Step 3: Identify Optimization Opportunities
Review all FAIL items from the checklist. Prioritize by impact (highest first):
- Description quality — Most common cause of skill not being invoked. Fix first.
- Content compression — Remove knowledge the agent already has. Reduces token cost and noise.
- Progressive disclosure — Split oversized SKILL.md into reference files, or merge tiny reference files back.
- Structure clarity — Improve headers, cross-references, and flow. Numbered steps for workflows.
- Consistency — Fix terminology, formatting, and style inconsistencies.
- Triggering precision — Under-triggering: add keywords, trigger phrases, concrete use cases. Over-triggering: add negative triggers ("Do NOT use for X"), narrow the scope.
List each optimization opportunity with:
- What is wrong
- Why it matters
- What the fix will be
Step 4: Apply Optimizations
For each issue identified in Step 3, apply the fix. Use compression techniques where applicable: -> See compression-techniques
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.
- 2d ago First seen · 84 lines · 69 tokens per session scan A 06000b2950f7
optimize-skill is a skill published in the GitHub repository Agent-Threat-Rule/agent-threat-rules (385 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 770 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to optimize-skill, differing in 0 lines, and is treated as a copy.
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