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 BostonOrange/claude-code-framework --skill factorygit clone --depth 1 https://github.com/BostonOrange/claude-code-frameworkWrote 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/bostonorange/claude-code-framework/factory)<a href="https://agentmods.dev/skills/bostonorange/claude-code-framework/factory"><img src="https://agentmods.dev/badge/skills/bostonorange/claude-code-framework/factory/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/bostonorange/claude-code-framework/factory"><img src="https://agentmods.dev/badge/skills/bostonorange/claude-code-framework/factory.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00046 | $0.02015 |
| Opus 5.5 | $0.00018 | $0.00806 |
| Sonnet 5.5 | $0.00009 | $0.00403 |
| Haiku 4.5 | $0.00005 | $0.00201 |
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 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Factory
End-to-end AI Software Factory: readiness → develop → PR → CI deploy to test environment.
Usage
/factory TICKET-1234
/factory 1234
Overview
The factory pipeline automates the full path from an approved ticket to code deployed in a test environment for review. It chains existing skills with factory-mode defaults that eliminate manual decision points.
Human Touchpoints (reduced from 5+ to 2)
- Architect/PM drafts ticket —
/draft-storyresolves all open questions → ticket is "Ready for Sprint" - Code review — reviewer approves PR. Code is already deployed in test environment (by CI).
- Smoke test — users validate in staging after auto-deploy
Everything else is automated.
Architecture
Developer Terminal (Claude Code) │ CI/CD (automatic)
─────────────────────────────────────┼──────────────────────────────────
│
/factory TICKET-xxx │
├─ /check-readiness │
├─ Create worktree + branch │
├─ Scaffold + implement │
├─ /validate (code standards only) │
├─ Fix code standards issues │
├─ Commit + push │
├─ Create PR (factory label) ─────┼──► CI validate workflow
└─ /improve (background) │
│ ├─ Deploy to test environment
│ ├─ Run tests
│ └─ Post PR comment with env link
│
Review ─┼──► Reviewer tests in environment
│
PR Approved ──┼──► CI auto-merge workflow
│
PR Merged ──┼──► CI deploy workflow
│ ├─ Deploy to staging
│ ├─ Notify team
│ └─ Update ticket state
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 · 259 lines · 46 tokens per session scan A 8b292a6f4a39
factory is a skill published in the GitHub repository BostonOrange/claude-code-framework (3 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 2,015 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
deployment-patterns
Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications. Use when setting up deployment infrastructure or planning releases.
agent-github-modes
Agent skill for github-modes - invoke with $agent-github-modes.
baby-sit
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
chinese-git-workflow
A reference for configuring Git with Chinese code-hosting services such as Gitee, Coding.net, GitLab China, and CNB, including SSH, HTTPS, credentials, CI, and repository mirroring.
atmos-hooks
Atmos hooks: lifecycle events, hook kinds, command/store/git/security hooks, step/steps hooks, when: conditions, scoping and overrides, toolchain integration, --skip-hooks, and Atmos Pro/local output.
atmos-pro
Atmos Pro setup and workflows: settings.pro, GitHub OIDC, affected and inventory uploads, stack locks, pro commit, workflow dispatch, merge queues, and drift detection.