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 hqhq1025/skill-optimizer --skill skill-personalizergit clone --depth 1 https://github.com/hqhq1025/skill-optimizerWrote 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/hqhq1025/skill-optimizer/skill-personalizer)<a href="https://agentmods.dev/skills/hqhq1025/skill-optimizer/skill-personalizer"><img src="https://agentmods.dev/badge/skills/hqhq1025/skill-optimizer/skill-personalizer/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/hqhq1025/skill-optimizer/skill-personalizer"><img src="https://agentmods.dev/badge/skills/hqhq1025/skill-optimizer/skill-personalizer.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 | $0.00052 | $0.00511 |
| Opus 5 | $0.00026 | $0.00255 |
| Sonnet 5 | $0.00010 | $0.00102 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
skill-personalizer 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 9d 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Personalizer
Overview
Audit and tune a skill for one user's real environment. The goal is not public portability; it is better triggering, less friction, and stronger fit with the user's actual tools, paths, style, and recurring tasks.
When To Use
- A user installs a skill from GitHub or creates one from scratch and wants it to fit their setup.
- A skill undertriggers, overtriggers, asks unnecessary questions, or misses the user's preferred workflow.
- A user asks whether an existing skill is good, broken, noisy, too long, conflicting, or worth keeping.
- Local paths, aliases, memories, CLIs, MCP tools, or repo conventions should be reflected in the skill.
Do not use when preparing a skill for public release; use skill-generalizer for that.
Workflow
- Inspect the target skill, installed copies, local memories, and real session evidence when available.
- Run the audit checks in audit-rubric.md when quality, trigger fit, or retention is unclear.
- Identify the user's recurring phrasing, expected autonomy level, tools, directories, and verification habits.
- Compare the skill's trigger conditions against real user requests that should or should not load it.
- Edit only the target skill and bundled resources needed for personalization.
- Add concrete local defaults, preferred commands, safety boundaries, and verification steps.
- Preserve useful upstream behavior; document any intentional local divergence.
- Validate with realistic prompts and a frontmatter/layout check.
Personalization Rules
- Personal details are allowed only if they improve this user's future execution.
- Do not add brittle fallbacks that hide broken local setup.
- Prefer real local evidence over generic best practices.
- Keep trigger descriptions broad enough to catch the user's natural phrasing.
- If editing an installed third-party skill, avoid changing upstream attribution or license text.
References
Read audit-rubric.md for the diagnostic pass inherited from the original optimizer.
What ships with it
3 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.
- 9d ago First seen · 45 lines · 52 tokens per session scan A 0705899cb042
skill-personalizer is a skill published in the GitHub repository hqhq1025/skill-optimizer (167 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 511 once invoked, about $0.0003 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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