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 agentmods add skills/shinpr/ai-coding-project-boilerplate/skill-optimizationnpx skills add shinpr/ai-coding-project-boilerplate --skill skill-optimizationgit clone --depth 1 https://github.com/shinpr/ai-coding-project-boilerplateWrote 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/shinpr/ai-coding-project-boilerplate/skill-optimization)<a href="https://agentmods.dev/skills/shinpr/ai-coding-project-boilerplate/skill-optimization"><img src="https://agentmods.dev/badge/skills/shinpr/ai-coding-project-boilerplate/skill-optimization.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 | $0.00037 | $0.02753 |
| Opus 5 | $0.00018 | $0.01376 |
| Sonnet 5 | $0.00007 | $0.00551 |
| Haiku 4.5 | $0.00004 | $0.00275 |
Grade A, and why
skill-optimization 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 4d 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Content Optimization
Core Philosophy
- Finding-Based: Every change resolves a recorded issue or follows a named project-specific source
- Concrete: Each pattern provides detection criteria and transform methods
- Structure-Focused: Optimizes expression and organization; domain knowledge remains unchanged
- Intent-Preserving: Records the original requirements before changing structure, wording, constraints, context, or examples
- Traceable: Connects every applied change to a finding or named project source
- Self-Contained: Keeps every pure skill executable when loaded alone; duplication across independently loaded pure skills is valid when each copy is required for standalone execution
Content Optimization Patterns
P1: Critical (Must Fix)
Issues that directly reduce LLM execution accuracy when consuming the skill.
BP-001: Negative Instructions → Positive Form
| Detection | Transform |
|---|---|
| "don't", "do not", "never", "avoid" in skill instructions | State the desired action or allowed state first. Preserve an explicit prohibition only when the violation is an irreversible operational action, the caller cannot normally recover it, and a positive-only rewrite would blur the boundary. Pair the prohibition with the safe alternative and the condition that authorizes crossing the boundary. Rewrite reviewable quality policies in positive form. |
Exception boundary examples:
- Permitted: "Move obsolete records to the recoverable archive. Do not permanently delete them unless the user explicitly authorizes permanent deletion."
- Rewrite in positive form: "Do not invent issues" → "Base every issue on BP patterns or 10 principles", "Do not skip P1 issues" → "Evaluate all P1 issues in every review mode", "Do not give grade A when P1 exists" → "Assign grade A only when P1 count is zero"
Quality policies, role boundaries, scoring criteria, and general work rules always use positive form. Outputs that the caller validates, overwrites, or discards are never irreversible.
What ships with it
2 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.
- 4d ago First seen · 184 lines · 37 tokens per session scan A 3834bd0679d4
skill-optimization is a skill published in the GitHub repository shinpr/ai-coding-project-boilerplate (227 stars, last pushed 7d ago), licensed MIT. It adds 37 tokens to every session and 2,753 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 2026-08-30.
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