os-optimizer

os-optimizer is a skill for Claude Code from naveedharri/benai-skills. It costs 199 tokens per session (18,741 once invoked), scanned A, a copy of optimizer, MIT.

A framework-based auditor and optimiser for Markdown knowledge vaults, which are collections of linked notes and documents.

In plain words
What is it for?
Use it to review a vault file by file, guide fixes, and save a comprehensive HTML report or a reorganisation plan.
Why use it?
It finds problems in note structure, context, memory, discoverability, and organisation, then records or applies fixes.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions CLAUDE.md; names the AskUserQuestion tool.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is | ./About/me.md | identity | curated | Operator bio + voice | high |.

Part of the all-skills plugin — 99 skills shipped together

Good fit Use it to review a vault file by file, guide fixes, and save a comprehensive HTML report or a reorganisation plan.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/naveedharri/benai-skills
agentmods
npx agentmods add skills/naveedharri/benai-skills/os-optimizer

Made for: Claude Code.

Or install all-skills, the plugin that ships this one along with the rest of its 99 skills.

Wrote 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.

agentmods badge for os-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/naveedharri/benai-skills/os-optimizer/github.svg)](https://agentmods.dev/skills/naveedharri/benai-skills/os-optimizer)
Your own site
<a href="https://agentmods.dev/skills/naveedharri/benai-skills/os-optimizer"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/os-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.

agentmods 80×15 button for os-optimizer

Your own site · 80×15
<a href="https://agentmods.dev/skills/naveedharri/benai-skills/os-optimizer"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/os-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 199 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 18,741 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 88% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00199 $0.18741
Opus 5 $0.00100 $0.09371
Sonnet 5 $0.00040 $0.03748
Haiku 4.5 $0.00020 $0.01874

Measured 5d ago against content hash 265e74d81838, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

os-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 5d 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.

Origin

This is a copy

88% identical to optimizer — 94 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.

plugins/all-skills/skills/os-optimizer/SKILL.md · 925 lines

How it starts

The opening of the file, as written. The whole thing — 925 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Vault Optimizer

Apply 10 frameworks to every markdown file in the vault. For each framework, read its pass-implementation file, run every check, log findings, walk the user through fixes per item, apply (or save to a plan). Save one comprehensive HTML report grouped by framework. Do not inline the HTML in chat — only the saved path and a one-paragraph summary.

Operating philosophy — read carefully, this is what makes this skill different

  1. Every finding ships a concrete fix. No flag-only. No "warn and forget." No manual-review pile. When the user runs apply-mode, every finding becomes either an applied edit, a saved migration step in a dated reorg plan, or (only if the user explicitly declines this finding in walk) a recorded decline. Nothing lingers as an open warning across runs.
  2. Severity is informational, not gating. fail / warn / info describes how load-bearing the issue is; it does not gate whether a fix is offered. Every check produces fixes.
  3. Walk per item, user picks the target. Bulk-apply is reserved for purely mechanical fixes (em dashes, duplicate H1). Anything semantic — wikilink repointing, merges, routing rewrites, Plot.md generation, reorganizations — is walk-only with the user confirming the destination/winner/wording per item.
  4. Two modes for every fix: apply now (executes in this run) or save to plan (writes the change as a checklist step into the plan file, whose path is resolved from the role registry in Step 4.3). User picks per finding for high-blast-radius items. Smaller fixes default to apply-now.
  5. Visible progress, never silent. Step 0.5 creates one TaskCreate entry per stage and per framework; tasks update in_progresscompleted as the run unfolds. The user watches the audit walk through the vault rather than waiting for one big report at the end.
  6. Read and reason, don't just match. Every framework's triggers surface candidates; every finding requires the agent to read context, judge alignment with the user's stated world, and produce reasoning specific to the case. No paraphrased rule restatements as "reasoning."
  7. Discover structure, never assume folder names. Vaults vary. One user's curated layer is Context/; another's is About/, Me/, frontmatter on root, or scattered across topic folders. The optimizer runs Step 1.5 (role discovery) before any framework, and every framework references roles (context-equivalent, decisions-equivalent, daily-equivalent, folder-index convention…) discovered from content — never hardcoded names. If a role is missing, it surfaces as a finding with a proposed fix ("you have no decisions-equivalent folder; here's a recommendation"), not as a silent assumption. Static path references in pass files are always abstractions; the agent resolves them through the role registry at run time.

Read the full file on GitHub · 925 lines

Changes

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.

  1. 5d ago First seen · 925 lines · 199 tokens per session scan A 265e74d81838

Subscribe to this mod's changes

os-optimizer is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 6d ago), licensed MIT. It adds 199 tokens to every session and 18,741 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to optimizer, differing in 94 lines, and is treated as a copy.

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