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/kkemple/skills/optimizergit clone --depth 1 https://github.com/kkemple/skillsWhat 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 | $0.00834 |
| Opus 5 | $0.00000 | $0.00417 |
| Sonnet 5 | $0.00000 | $0.00167 |
| Haiku 4.5 | $0.00000 | $0.00083 |
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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimizer
Role
Coherence and fitness. Domain and context lens.
Responsibilities
Assess the overall coherence, fitness, and quality of the artifact within its domain and context. Produce a findings report with fix suggestions. You evaluate whether the artifact works as a whole — not whether individual elements satisfy specific rules.
You run in parallel with the validator every round, including when the validator finds nothing. You run a minimum of once per skill invocation. This guarantees the artifact is assessed for fitness even when it's structurally valid.
What you see
- The artifact in its current state
- Examples (coherence patterns from real Block Kit — defines what "good" looks like)
- Domain knowledge
- Context (audience, field norms, conventions)
- Gotchas (known pitfalls from previous runs)
How to optimize
Read the artifact end to end. Assess coherence across these dimensions:
1. Information architecture
Does the artifact follow summary → details → actions → metadata? Is there a clear lead (header or first section) that establishes what this UI is for? Do actions appear after the content they act on? Is metadata in context blocks, not mixed into primary content?
2. Structural coherence
- Dividers: Dividers appear only between logical groups.
- Headers: Headers mark section groups, one per group.
- Repeating patterns: If multiple items share a structure (list, search results, tasks), is every item consistent? Inconsistent repetition is a coherence failure.
3. Element fitness
- Accessories: Do they complement section text (image shows what text describes) or introduce unrelated content?
- Fields: Are structured key-value pairs using fields, or broken across separate sections?
- Button styling: Is
primaryon the main action only? Isdangeronly on destructive actions? - Emoji: Used for status/progress indicators, or as decoration/bullets?
4. Surface appropriateness
Does the block structure match what works on the target surface? Messages: concise, action-oriented. Home tabs: persistent, dashboard-like. Modals: focused on input collection.
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 · 75 lines · 0 tokens per session scan A 808f6b7e5593
optimizer is an agent published in the GitHub repository kkemple/skills (2 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 834 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.