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 lingxling/awesome-skills-cn --skill agentflowgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote 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/lingxling/awesome-skills-cn/agentflow)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/agentflow"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agentflow/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/lingxling/awesome-skills-cn/agentflow"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/agentflow.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 | $0.00052 | $0.01953 |
| Opus 5.5 | $0.00021 | $0.00781 |
| Sonnet 5.5 | $0.00010 | $0.00391 |
| Haiku 4.5 | $0.00005 | $0.00195 |
Grade A, and why
agentflow 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 10d 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
100% identical to agentflow — 37 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentFlow
Overview
AgentFlow turns your existing Kanban board into a fully autonomous AI development pipeline. Instead of building custom orchestration infrastructure, it treats your project management tool (Asana, GitHub Projects, Linear) as a distributed state machine — tasks move through stages, AI agents read and write state via comments, and humans intervene through the same UI they already use.
The result is complete pipeline observability from your phone, free crash recovery (state lives in your PM tool, not in memory), and human override at any point by dragging a card.
When to Use This Skill
- Use when you need to orchestrate multiple Claude Code workers across a full development lifecycle (build, review, test, integrate)
- Use when you want deterministic quality gates (tsc/eslint/tests) before AI review on AI-generated code
- Use when you want full pipeline visibility from your Kanban board or phone
- Use when running a solo or team project that needs autonomous task dispatch with cost tracking
- Use when you need crash-proof orchestration that survives session restarts
Core Concepts
7-Stage Kanban Pipeline
Tasks flow through: Backlog, Research, Build, Review, Test, Integrate, Done. Each stage has specific gates. The Kanban board IS the orchestration layer — no separate database, no message queue, no custom infrastructure.
Stateless Orchestrator
A crontab-driven one-shot sweep runs every 15 minutes. No daemon, no session dependency. If it crashes, the next sweep picks up where it left off because all state lives in your PM tool.
Deterministic Before Probabilistic
Hard gates (tsc + eslint + tests) run before any AI review, catching roughly 60% of issues at near-zero cost. AI review comes after, as a second layer.
Adversarial Review
A different AI agent reviews code and must list 3 things wrong before deciding to pass. This prevents rubber-stamp approvals.
Transitive Priority Dispatch
Tasks that unblock the most downstream work get built first, automatically computing the critical path.
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.
- 10d ago First seen · 205 lines · 52 tokens per session scan A c28cac9851b6
agentflow is a skill published in the GitHub repository lingxling/awesome-skills-cn (312 stars, last pushed 10d ago), licensed MIT. It adds 52 tokens to every session and 1,953 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agentflow, differing in 37 lines, and is treated as a copy.
Other skills, from other repositories
mm-init-project
A project setup workflow that creates a shared project record in Markdown files, including a passport, dashboard, handoff notes, and instructions for Claude.ai. Obsidian is a note-taking app that can store these files as a connected project vault.
mm-projects
A read-only overview of all registered projects stored in Obsidian notes. It shows each project's status, type, current GSD phase, recent activity, and number of open questions.
notion-spec-to-implementation
Turns product or tech specs into concrete Notion tasks that Claude code can implement. Breaks down spec pages into detailed implementation plans with clear tasks, acceptance criteria, and progress tracking to guide development from requirements to completion.
high-output-management
Think like Intel's legendary CEO. Apply Andy Grove's management operating system to maximize your team's output through leverage, OKRs, and systematic decision-making. Use when: Scaling a team when individual contribution isn't enough; Performance management to measure and improve output; Meeting optimization to make…
radical-candor
Stop being a "nice" manager. Learn Kim Scott's framework for caring personally while challenging directly—the combination that builds trust and drives performance. Use when: Giving feedback (positive or constructive); Building trust with direct reports; Performance conversations that need to happen; Team culture…
product-discovery
Build products customers actually want. Apply Marty Cagan's Silicon Valley-tested framework to discover solutions that are valuable, usable, feasible, and viable. Use when: New product development when validating what to build; Feature prioritization to ensure you're solving real problems; Pivot decisions when current…