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/warpdotdev-demos/cloud-factory-demo/implementationnpx skills add warpdotdev-demos/cloud-factory-demo --skill implementationgit clone --depth 1 https://github.com/warpdotdev-demos/cloud-factory-demoWrote 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/warpdotdev-demos/cloud-factory-demo/implementation)<a href="https://agentmods.dev/skills/warpdotdev-demos/cloud-factory-demo/implementation"><img src="https://agentmods.dev/badge/skills/warpdotdev-demos/cloud-factory-demo/implementation.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 | $0.00072 | $0.03131 |
| Opus 5 | $0.00036 | $0.01566 |
| Sonnet 5 | $0.00014 | $0.00626 |
| Haiku 4.5 | $0.00007 | $0.00313 |
Grade A, and why
implementation 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 5d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation
Implement the issue passed in the user's prompt and open a GitHub pull request with the fix or feature.
Expect the prompt to contain a link, key, or number for exactly one issue in an issue tracker. Use tracker context and the current checkout to understand the requested behavior before changing code.
Workflow
1. Identify the issue and repository
Extract the issue URL, key, or number from the prompt. Determine whether it belongs to GitHub Issues, Jira, Linear, or another tracker.
Confirm the current checkout is the repository where the implementation should happen. If the prompt does not identify one issue unambiguously, ask for clarification before making changes.
2. Verify optional helper skills
If the checkout contains .agents/skills/validate-changes-match-specs/SKILL.md, use it after implementation whenever PRODUCT.md and TECH.md specs exist for the issue.
If specs exist but the validation skill is missing, continue only if you can still manually compare the implementation against the specs. Report that the common validation skill was unavailable in the PR description and issue comment.
If the checkout contains .agents/skills/verify-behavior/SKILL.md and the issue has visible UI, browser, desktop, mobile, or other interactive behavior, you must run that skill before claiming the implementation is complete. It delegates to Oz's dedicated computer_use capability on this computer-use-enabled run — do not drive the GUI yourself and do not substitute generic remote children. Do not skip verification because PR creation failed or because automated unit tests passed.
3. Post an implementation-started status comment
For GitHub Issues, post a short status comment before doing implementation work so issue subscribers know an agent has started.
Use the authenticated gh CLI when available. Include:
- That automated Oz implementation has started.
- The issue identifier being implemented.
- A follow-along link to the Oz run (see Oz run URLs below).
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.
- 5d ago First seen · 225 lines · 72 tokens per session scan A a3f10beef048
implementation is a skill published in the GitHub repository warpdotdev-demos/cloud-factory-demo (122 stars, last pushed 22d ago), licensed MIT. It adds 72 tokens to every session and 3,131 once invoked, about $0.0004 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…