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/0xrafasec/ai-workflow/factorynpx skills add 0xrafasec/ai-workflow --skill factorygit clone --depth 1 https://github.com/0xrafasec/ai-workflowWrote 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/0xrafasec/ai-workflow/factory)<a href="https://agentmods.dev/skills/0xrafasec/ai-workflow/factory"><img src="https://agentmods.dev/badge/skills/0xrafasec/ai-workflow/factory.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 | $0.00107 | $0.08419 |
| Opus 5 | $0.00053 | $0.04209 |
| Sonnet 5 | $0.00021 | $0.01684 |
| Haiku 4.5 | $0.00011 | $0.00842 |
Grade A, and why
factory 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 — 770 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the factory pipeline for: $ARGUMENTS
What this does
Factory ships one milestone (== one roadmap phase) at a time. It scans the milestone's open issues, generates missing specs via speckit, then launches up to 5 parallel worktree writer agents — each implementing one issue on its own branch with lint+typecheck+tests as a hard gate before opening a PR. Every PR is then reviewed by a separate, fresh-context reviewer agent (writer/reviewer separation per CLAUDE.md). Reviewer findings are fixed via the writer agent resumed in-place (warm context, cheap), bounded to 2 fix cycles. The final review verdict is posted to the PR. Factory stops with PRs open for human merge — no auto-merge.
For multi-phase roadmap execution use /autopilot instead. Factory is scoped to exactly one phase/milestone per invocation.
You are the orchestrator (opus). You do not implement, review, or fix code yourself. You dispatch agents (sonnet) and aggregate their structured returns. You never read PR diffs or full review prose into your own context — only {verdict, cycles, pr_url} summaries.
Convention: phase ↔ milestone
A roadmap phase file docs/roadmap/<NNN>_<name>.md corresponds to a GitHub milestone whose title exactly equals <NNN>_<name> (e.g., phase file 003_auth.md ↔ milestone 003_auth). This is how /issues files them and how factory resolves them. No frontmatter, no labels — title match only.
Parse Arguments
Factory requires exactly one phase/milestone per invocation. Reject calls without a target.
/factory <phase-name>— e.g.,/factory 003_auth. Resolves to roadmap filedocs/roadmap/003_auth.mdAND milestone titled003_auth. Both must exist (phase file is the source of truth for spec paths and file lists; milestone is the source of truth for which issues are still open)./factory --milestone <N>— fetch milestone<N>viagh api, read its title, then resolve the matchingdocs/roadmap/<title>.md. Equivalent to/factory <title>.--no-issues: skip GitHub issue creation if a spec exists but no issue is filed yet (still requires a milestone for scoping).--dry-run: scan and report the planned batches + review-loop budget, spawn nothing.--limit N: override the default 5-agent parallel cap (use carefully).
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 · 770 lines · 107 tokens per session scan A 1667ce6ce472
factory is a skill published in the GitHub repository 0xrafasec/ai-workflow (9 stars, last pushed 2d ago), licensed MIT. It adds 107 tokens to every session and 8,419 once invoked, about $0.0005 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
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…