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 skills add Intelligent-Internet/zenith --skill optimization-mission-playbookgit clone --depth 1 https://github.com/Intelligent-Internet/zenithWrote 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.
[](https://agentmods.dev/skills/intelligent-internet/zenith/optimization-mission-playbook)<a href="https://agentmods.dev/skills/intelligent-internet/zenith/optimization-mission-playbook"><img src="https://agentmods.dev/badge/skills/intelligent-internet/zenith/optimization-mission-playbook/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.
<a href="https://agentmods.dev/skills/intelligent-internet/zenith/optimization-mission-playbook"><img src="https://agentmods.dev/badge/skills/intelligent-internet/zenith/optimization-mission-playbook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00118 | $0.09454 |
| Opus 5.5 | $0.00047 | $0.03782 |
| Sonnet 5.5 | $0.00024 | $0.01891 |
| Haiku 4.5 | $0.00012 | $0.00945 |
Grade A, and why
optimization-mission-playbook 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 1mo 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 — 684 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Optimization Mission Playbook
What This Playbook Is For
Load this playbook when the mission's goal is to move a metric: make something faster, smaller, cheaper, higher-scoring, more accurate, higher-throughput, better-compressed, better-ranked, or closer to a solver/objective bound. The signal is a number with a direction and a workload that produces it.
Do not use it for missions whose goal is durable behavior (a feature, a port, a migration, an API) — those are engineering missions; use engineering-mission-playbook. When a mission has both — build a benchmark harness then optimize a hot path, port a library then tune it — load both playbooks and record the boundary. Durable behavior and scaffolding may use engineering assertions; metric search should normally run as targetless optimization work, with formal assertions reserved for genuine durable commitments or externally required sign-off.
This playbook has two halves. The strategy (Posture through Anti-patterns) is how to think about an optimization mission — read it first; it is the reasoning every decision below depends on. The method (Operating Order onward) is how to express that thinking as orchestrator investigation, targetless experiment work in the current checkout, ledger review, and patches in this runtime. The method references the runtime lifecycle and task schema but does not redefine them.
Posture
An optimization mission moves a metric. You are not building toward a known end state — you are running an experiment whose answer you do not yet know, against a measurement that can mislead you. Think like a skeptical experimentalist, not a builder: most of the work is deciding what to measure, where, and whether the number is real — not deciding how to change the code.
Hold two ideas at once:
- The metric is the goal.
- The metric is a proxy that can lie — through noise, through non-comparable measurement, through gaming, through overfitting the cases you happened to look at.
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.
- 1mo ago First seen · 684 lines · 118 tokens per session scan A a95912ba03c5
optimization-mission-playbook is a skill published in the GitHub repository Intelligent-Internet/zenith (374 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 118 tokens to every session and 9,454 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
configure-coddy
Change Coddy's own configuration when the user asks for it: edit settings, providers, models, logging, permissions, or find, install, update, and remove MCP servers and skills. Stages UCI-style commands for config.yaml and commits only after the user confirms saving; MCP servers are entries of the mcp.json files. Load…
crossreview
Run when the user invokes /crossreview (or /crossreview:setup to choose the reviewers) or asks for a cross-review, a quorum review or a second opinion from other agents or models: fan a code review (uncommitted work, a branch, a commit range, files, a plan or a document) out to several console code agents (Claude…
rpa-init
Run when the user invokes /rpa-init or asks to onboard or warm up context on a repository. The agent studies code, reads documentation and test code, sets up the dev environment as the project expects, runs tests, and writes a short project report. No extra user brief is required.
release
Create a new versioned release with changelog. Bumps version in code, updates CHANGELOG.md, commits, tags, and pushes using the repository release flow. GitHub Actions creates the release and uses only the current changelog section as release notes. Use when the user says "release", "cut a release", "bump version"…
qa
Regression test a San feature by name. Looks for a feature doc in docs/reference/ or docs/packages/2-feature/, runs automated Go tests and interactive tmux tests, then produces a pass/fail report. Use this skill when the user says "qa", "regression test", "test feature X", "verify feature", or references a feature…
rpa-gen-rules
Use when the user invokes /rpa-gen-rules or asks to create, refresh, audit, or synchronize project instructions such as root AGENTS.md, Cursor .cursor/rules, or Claude Code .claude/rules, especially when existing rule trees have drifted.