skill-optimize

A command that trims a skill or a catalogue of skills to the information needed beyond what a chosen model already knows. It compares the result with a weaker model and checks for regressions.

In plain words
What is it for?
Use it to optimize one skill or every skill in a catalogue, preview cuts, or produce a report without restoring changes.
Why use it?
Skills can contain unnecessary explanations that add length without improving results. This helps identify and remove material that does not affect performance.

Command

Install

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.

agentmods
npx agentmods add commands/xonovex/platform/skill-optimize
Clone the repo
git clone --depth 1 https://github.com/xonovex/platform
Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 331 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 $0.00025 $0.00331
Opus 5 $0.00013 $0.00166
Sonnet 5 $0.00005 $0.00066
Haiku 4.5 $0.00003 $0.00033

Measured yesterday against content hash 162de77e0f29, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

skill-optimize 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 yesterday.

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.

packages/command/command-utility/commands/skill-optimize.md · 34 lines

What it actually says

/xonovex-utility:skill-optimize - Trim a skill to its knowledge delta and verify

Arguments

  • [skill-file] (required unless --all) - Path to a SKILL.md or skill directory
  • --all (optional) - Optimize every skill in the catalog, one optimize per skill in parallel
  • [--model <m>] (optional) - Weakest model to measure and ablate against (default haiku)
  • [--tier <t>] (optional) - Trim depth; auto classifies per skill (default auto)
  • [--dry-run] (optional) - Preview cuts without writing
  • [--report-only] (optional) - Ablate and report regressions without restoring

Delegation

Load the skill-guide skill (plugin xonovex-skill-skill) and perform its optimize operation with these arguments. The skill is the source of truth for the procedure, tiers, and gotchas. Do not restate them.

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. yesterday First seen · 34 lines · 25 tokens per session scan A 162de77e0f29

Subscribe to this mod's changes

skill-optimize is a command published in the GitHub repository xonovex/platform (5 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 331 once invoked, about $0.0001 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.