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 skills/matt-bat/agent-command-center/file-structure-optimizationnpx skills add matt-bat/agent-command-center --skill file-structure-optimizationgit clone --depth 1 https://github.com/matt-bat/agent-command-centerWhat 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 | $0.00034 | $0.01116 |
| Opus 5 | $0.00017 | $0.00558 |
| Sonnet 5 | $0.00007 | $0.00223 |
| Haiku 4.5 | $0.00003 | $0.00112 |
Grade A, and why
file-structure-optimization 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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 · 133 lines · 34 tokens per session scan A 9c11fe86aa88
file-structure-optimization is a skill published in the GitHub repository matt-bat/agent-command-center (4 stars, last pushed 9d ago), with no licence file. It adds 34 tokens to every session and 1,116 once invoked, about $0.0002 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.
Other skills, from other repositories
temporal-developer
Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java, .NET, Ruby, and Rust. Use when the user is building workflows, activities, or workers with a Temporal SDK, debugging issues like non-determinism errors, stuck workflows, or activity retries, using Temporal CLI, Temporal Server, or…
skill-author
Teach an author how to integrate the CAFleet-orchestrated team pattern into a new skill they are writing. Auto-load when the author intends to create a new CAFleet-orchestrated skill, write a skill that spawns cafleet members, add a Director/Member team skill, design a multi-agent broker-coordinated skill, build a…
package-release
Prepare, publish, repair, or verify package releases and changelogs. Always use for package/version release, publish, ship, tag, changelog, release notes, registry publication, documentation publication, or GitHub Release work. Distinguish package releases from application deployments before acting.
clean-docs
Clean up the repository's docs, comments, rules, and tests as a CAFleet team of disjoint-slice scanner members gated by a reviewer — exactly one workflow per run. Use the residue workflow when the user asks to clean up historical narration or historical residue, remove deprecation notes / "this replaces X" / "renamed…
cafleet
Interact with the CAFleet message broker and supervise CAFleet member teams. Use when an agent needs to register as a member, send/receive messages, poll inbox, acknowledge messages, or discover other members; or when a Director is about to spawn, monitor, health-check, or recover a stalled team of CAFleet members…
session-reflect
Analyze a user's pi coding-agent session history for recurring behavior, prompting habits, workflow loops, friction, and preferences. Use when the user asks to inspect or reflect on pi sessions, common behavior patterns, agent/user interaction style, prompting habits, or personal pi workflow quality.