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/smallnest/goal-workflow/graphnpx skills add smallnest/goal-workflow --skill graphgit clone --depth 1 https://github.com/smallnest/goal-workflowWhat 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 | $0.00129 | $0.04085 |
| Opus 5 | $0.00064 | $0.02042 |
| Sonnet 5 | $0.00026 | $0.00817 |
| Haiku 4.5 | $0.00013 | $0.00409 |
Grade A, and why
graph 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 2d 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.
This is a copy
98% identical to graph — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
graph — Task/PRD to Parallel Execution Graph
Turn a task (or PRD / SPEC / issue set) into a directed acyclic graph of work units, layer it into supersteps (waves), and implement each wave's independent nodes concurrently using subagents. Each node runs the full /goal → /review-it → /ship-it pipeline inside its own git worktree, so parallel nodes never clobber each other's working tree. Between waves, a fan-in barrier merges results and re-plans the next wave.
This is the parallel sibling of /loop-it. /loop-it is strictly sequential (one worktree, one issue at a time). /graph fans out every independent node in a wave at once.
Mental Model (borrowed from LangGraph / graph engineering)
| Concept | Here |
|---|---|
| Node | One implementable unit of work (an issue / subtask) |
| Edge | A dependency: B depends on A → edge A → B |
| Superstep / wave | A set of nodes whose deps are all satisfied — run concurrently |
| Fan-out | Dispatch one subagent per node in the current wave |
| Fan-in (barrier) | Wait for all nodes in the wave before starting the next |
| State channel | .graph_state — shared checkpoint, rewritten between waves (resume source) |
| Live tracker | graph.html — Claude-style light-theme dashboard, re-rendered from .graph_state at every checkpoint |
| Dynamic re-plan | After a wave, revise the graph if new work/deps emerged |
Core principle: Independent nodes in the same wave have no shared state and no ordering dependency, so they can run in true parallel. Dependencies define the only ordering. Everything else runs at once.
Overview
Input (task / PRD / SPEC / issues)
│
▼
1. Decompose into nodes ─────────► nodes = {id, title, deps, criteria, scope}
│
▼
2. Build DAG + validate ─────────► detect cycles, orphan deps
│
▼
3. Topological layering ─────────► waves = [[n1,n2,n3], [n4,n5], [n6]]
│
▼
4. Render graph + confirm with user
│
▼ (write .graph_state + graph.html — open graph.html to watch live)
┌──────────── per wave (superstep) ────────────┐
│ │
│ FAN-OUT: 1 subagent per node (parallel) │
│ each subagent, in its own git worktree: │
│ /goal (inline implement) → /review-it │
│ → /ship-it │
│ │
│ FAN-IN barrier: wait for ALL nodes │
│ integrate, update .graph_state │
│ re-render graph.html │
│ re-plan next wave if graph changed │
│ │
└───────────────────────────────────────────────┘
│
▼
All waves done → final summary
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.
- 2d ago First seen · 330 lines · 129 tokens per session scan A 787a3694c019
graph is a skill published in the GitHub repository smallnest/goal-workflow (268 stars, last pushed 5d ago), licensed MIT. It adds 129 tokens to every session and 4,085 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to graph, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
criticism-self-criticism
触发：当一项工作已经完成、进入阶段验收、收到批评反馈，或反复出现同类错误需要系统纠偏时调用；常见信号包括 review、audit、retrospective、quality check、纠错与复盘。 English: Trigger after delivery or at a review checkpoint when quality must be examined honestly and errors must be corrected without defensiveness. Use this skill for structured self-review, feedback processing, and…
mass-line
触发：当你需要收集多方意见、把零散反馈整合成可执行方案，或把方案带回真实使用者/执行者验证时调用；常见信号包括 stakeholder input、user feedback、意见汇总、对齐与验证。 English: Trigger when input must be gathered from many people, synthesized into a clearer plan, and returned to the affected users or executors for validation. Use this skill for a collect-synthesize-validate loop.
workflows
触发：当你面临的任务明显需要多个思想武器协作时调用；常见信号包括：从零启动新项目、攻坚复杂疑难问题、对已有方案进行迭代优化。此 skill 提供标准化的跨 skill 工作流组合，解决"应该先用哪个 skill、怎么衔接"的问题。 English: Trigger when a task clearly requires multiple skills in sequence. Use this skill to select a standard workflow that chains skills together, defines data handoff between steps, and specifies…
concentrate-forces
触发：当多个任务同时争夺时间、注意力、算力或预算，必须确定主攻方向并停止分散用力时调用；常见信号包括优先级过多、资源紧张、推进分散、需要决定先做什么。 English: Trigger when limited resources are being split across too many tasks and one main target must be chosen. Use this skill to concentrate effort, sequence work decisively, and finish a meaningful breakthrough before expanding.
investigation-first
触发：当你准备下判断、做决策或提出建议，但事实、上下文或一手信息还不充分时优先调用；常见信号包括 unknowns、信息缺口、证据不足、领域陌生、需要先摸清现状。 English: Trigger before making claims or decisions when context is incomplete, evidence is weak, or the domain is unfamiliar. Use this skill to investigate first, gather firsthand facts, and let reality shape the conclusion.
overall-planning
触发：当你需要在多个目标、利益方或相互制约的指标之间做动态平衡时调用；常见信号包括 trade-offs、目标冲突、系统性约束、优化一项会伤害另一项。 English: Trigger when several important goals must be advanced together and optimizing one dimension can damage another. Use this skill to map the key relationships, avoid one-sided decisions, and balance the system as a whole.