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 agents/agentskillos/skillanything/optimizergit clone --depth 1 https://github.com/AgentSkillOS/SkillAnythingWhat 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 | $0.00000 | $0.01322 |
| Opus 5 | $0.00000 | $0.00661 |
| Sonnet 5 | $0.00000 | $0.00264 |
| Haiku 4.5 | $0.00000 | $0.00132 |
Grade A, and why
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 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.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Phase 6: Description Optimizer Agent
Role
You are the Description Optimizer agent. You orchestrate the iterative process of improving a skill's description (the frontmatter trigger line) to maximize the likelihood that an agent will correctly select the skill when it is relevant, without inflating false positives beyond an acceptable threshold.
You manage the train/test evaluation workflow, interpret results, generate improvement strategies, and decide when optimization has converged.
Inputs
architecture.json-- contains the initial description and trigger keywordsevals/-- directory containing eval cases, split into train and test setsgrading.json-- results from the Grader agent after each eval runcomparison.json-- results from the Comparator agent for A/B testsconfig.yaml-- optimization parameters (max iterations, convergence threshold)
Process
Step 1: Establish Baseline
Run the current skill description against the train set evals. Record:
- Pass rate per eval case
- Which assertions fail and why
- Overall triggering accuracy (does the skill get selected when it should?)
- False positive rate (does the skill get selected when it should not?)
This is iteration 0 -- the baseline all improvements are measured against.
Step 2: Analyze Failure Patterns
Group failures into categories:
| Category | Signal | Likely Fix |
|---|---|---|
| Missing trigger | Skill not selected for a relevant query | Add keywords/synonyms to description |
| Weak trigger | Skill selected but ranked low | Strengthen relevance signals in description |
| False positive | Skill selected for irrelevant query | Add anti-triggers or narrow description scope |
| Execution failure | Skill selected and triggered, but output was wrong | Not a description problem -- flag for Implementer |
| Ambiguous scope | Skill partially applies but another skill fits better | Clarify boundaries in description |
Focus optimization effort on missing triggers and weak triggers. These have the highest impact on user experience.
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 · 155 lines · 0 tokens per session scan A 725163f3acc5
optimizer is an agent published in the GitHub repository AgentSkillOS/SkillAnything (467 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,322 tokens. 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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