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/knoxops/agent-runbook/outputnpx skills add KnoxOps/agent-runbook --skill outputgit clone --depth 1 https://github.com/KnoxOps/agent-runbookWrote 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/knoxops/agent-runbook/output)<a href="https://agentmods.dev/skills/knoxops/agent-runbook/output"><img src="https://agentmods.dev/badge/skills/knoxops/agent-runbook/output.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00017 | $0.00632 |
| Opus 5 | $0.00009 | $0.00316 |
| Sonnet 5 | $0.00003 | $0.00126 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
fix-failing-tests 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 4d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Execution Flow
Task Context
Before starting execution, initialize task_context.json:
{
"task_id": "<task_id from input>",
"current_step": 0,
"current_step_id": null,
"status": "running",
"steps": {
"fix_loop": "pending",
"present": "pending"
},
"updated_at": "<ISO timestamp>"
}
Update this file after each step completes. On error, set step status to "failed" and overall status to "failed".
Step 1: fix_loop
Type: loop Description: Run tests, analyze failures, fix source code, repeat until green
Iteration Loop
Goal: pytest exits with 0 failures (all tests pass) Max Iterations: 10
This step executes as a loop. The body steps repeat until the goal is met or max iterations reached.
Loop Body (repeats each iteration)
Body Step 1: run_tests
Type: script
Execution: Execute the following command:
cd examples/fix-loop && python3 -m pytest tests/ --tb=short 2>&1 | tail -60
Body Step 2: fix
Type: agent
Execution: Launch an independent agent with the following prompt file:
Look at the pytest failures from run_tests. Pick ONE source file that has failing tests and fix the bugs in that file.
Rules:
- Only modify files in src/, NEVER modify test files
- Fix exactly ONE file, then stop immediately
- Do NOT read or modify any other source files
Goal Evaluation
After all body steps complete, evaluate:
Goal: pytest exits with 0 failures (all tests pass)
- If goal IS met → mark this step completed, proceed to next step.
- If goal NOT met and iterations remain → reset body steps, start next iteration.
- If max iterations reached → mark step completed with status "max_iterations_reached", report what remains.
Append a summary to iteration_history after each iteration.
Progress Tracking
After completing this step, update task_context.json:
- Set
current_step_idto"fix_loop" - Set
steps.fix_loopto"completed"
Step 2: present
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 105 lines · 17 tokens per session scan A f82b18609f23
fix-failing-tests is a skill published in the GitHub repository KnoxOps/agent-runbook (17 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 17 tokens to every session and 632 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 2026-08-30.
Other skills, from other repositories
design-taste-frontend
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
image-to-code
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards…
brandkit
Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional…
minimalist-ui
Clean editorial-style interfaces. Warm monochrome palette, typographic contrast, flat bento grids, muted pastels. No gradients, no heavy shadows.
edgeone skill scanner
Scan any agent skill for security risks before you install or use it. Powered by Tencent Zhuque Lab A.I.G (AI-Infra-Guard). 100% local static analysis — no file contents or credentials leave your device. Compatible with CodeBuddy, Cursor, Windsurf, Claude Code, OpenClaw and more. Triggers on: 这个 skill 安全吗, skill 安全扫描…
rubber-duck
Adversarial "rubber duck" review that turns explaining-out-loud into a hallucination check. The main session is the PRESENTER (it did the work — a design doc, investigation, or analysis — and holds the real reasoning) and reconstructs the topic to a LISTENER — a spawned subagent pinned to a DIFFERENT-vendor model that…