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/takaoumehara/superforge-skill/superforge-devnpx skills add takaoumehara/superforge-skill --skill superforge-devgit clone --depth 1 https://github.com/takaoumehara/superforge-skillWrote 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/takaoumehara/superforge-skill/superforge-dev)<a href="https://agentmods.dev/skills/takaoumehara/superforge-skill/superforge-dev"><img src="https://agentmods.dev/badge/skills/takaoumehara/superforge-skill/superforge-dev.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 | $0.00244 | $0.02440 |
| Opus 5 | $0.00122 | $0.01220 |
| Sonnet 5 | $0.00049 | $0.00488 |
| Haiku 4.5 | $0.00024 | $0.00244 |
Grade A, and why
superforge-dev 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Superforge Dev — Multi-Agent Building & Model Tiering Engine
Use this skill when implementing multi-component features, executing complex build plans, or dispatching subagents. It ensures optimal model tiering and agent topology selection.
0. Split the work before choosing anything else
Topology and model tier are decisions about how to run tasks. They cannot rescue a bad split, and a bad split is where unattended runs actually fail — agents that conflict, duplicate, or wait.
A task is well-formed only when it has one outcome, a proof line, a listed set of files it will touch, and no question left to ask. The file list is not bookkeeping; it is what makes the next rule decidable:
Two tasks may run in parallel only if the set of files they write does not intersect. Not "probably do not conflict" — listed, and disjoint.
Shared foundations — schema, shared types, design tokens, the route table, a dependency upgrade, any rename — run alone and first, then fan out. Most failed parallel runs are one of those done concurrently with its dependants.
The never-parallel table, how to find the dependencies a file list cannot show,
how much context to hand each agent (both too little and too much fail, in
different ways), what to do when a subtask fails — revert before retry — and
when not to split at all → references/decomposition.md.
The schema is the shared foundation that runs first and alone, and it is
also the one part of a product that gets harder to change as the product
succeeds — code with no users can be rewritten in an afternoon, a table with
real rows cannot. The decisions that are cheap now and expensive later (IDs,
timestamps, money, deletion, the ownership chain every authorization check
reads), the three causes of every data performance problem, and how to run a
migration against data you cannot restore → references/data-design.md.
1. Agent Topology Proposal (Subagents · Agent Teams · Workflow)
What ships with it
11 files 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.
- evals/evals.json 5.0 KB
- README.es.md 5.2 KB
- README.ja.md 5.7 KB
- README.ko.md 5.6 KB
- README.md 4.8 KB
- README.zh-CN.md 4.6 KB
- references/autonomous-run.md 5.9 KB
- references/data-design.md 7.9 KB
- references/decomposition.md 7.3 KB
- references/dispatch-ledger.md 5.7 KB
- references/workflow-graphs.md 13 KB
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 · 172 lines · 244 tokens per session scan A 9400ebd09b23
superforge-dev is a skill published in the GitHub repository takaoumehara/superforge-skill (1 stars, last pushed 19d ago), licensed MIT. It adds 244 tokens to every session and 2,440 once invoked, about $0.0012 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
spec-kitty-charter-doctrine
Run charter interview, generation, context, and sync workflows for project governance in Spec Kitty 3.x. Access doctrine artifacts programmatically via DoctrineService. Resolve agent profiles. Load action-scoped governance context iteratively, not all at once. Triggers: "interview for charter", "generate charter"…
neo4j-modeling-skill
Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with…
alphafold-database
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
lov-env-management
统一管理平台、账号与多组 API Key,维护有效期和启用状态,安全同步到 zsh 或用户会话环境,并提供脱敏 Dashboard;用户说“管理环境变量”“rotate API keys”时使用。.
lov-deploy-to-vercel
Deploy frontend projects to Vercel with automatic custom domain setup. Handles Vite, Next.js, CRA, and static sites. Auto-configures Cloudflare DNS CNAME records and Vercel domain aliases. Supports SPA routing via vercel.json. Trigger when user says "deploy to vercel", "部署到 vercel", "vercel deploy", or mentions a…
lov-bp-outline
Turn existing project materials into a source-backed investor BP brief, evidence ledger, and 12–15 slide outline. Use before making slides, when the product positioning is unclear, or when an existing outline is too technical, generic, or unsupported. Trigger on "写 BP 大纲", "融资叙事", "梳理商业计划书", "先不要做 PPT", "BP outline"…