cheat-on-content
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill agentflow --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/agentflowContext preview
The summary Claude sees to decide when to auto-load this skill.
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
name: agentflow description: "Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution." risk: safe source: community date_added: "2026-04-02"
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.
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.
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.
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.
A different AI agent reviews code and must list 3 things wrong before deciding to pass. This prevents rubber-stamp approvals.
Tasks that unblock the most downstream work get built first, automatically computing the critical path.
Decomposes a SPEC.md into atomic tasks on your Kanban board with dependencies mapped.
Dispatches tasks to workers based on transitive priority and conflict detection. Runs as a crontab sweep.
Runs a worker in a terminal slot that picks up tasks, builds code, and creates PRs. Run 3-4 workers in parallel.
Real-time pipeline status dashboard showing current stage, assigned agent, retry count, and accumulated cost for every task.
Graceful shutdown: active workers finish their current task, unstarted tasks return to Backlog.
Create a `SPEC.md` for your project describing what you want to build.
claude -p "/spec-to-board"
This reads your SPEC.md, decomposes it into atomic tasks, maps dependencies, and creates them on your Kanban board.
Open 3-4 terminal windows, each as a worker slot:
# Terminal 2 — Builder claude -p "/sdlc-worker --slot T2" # Terminal 3 — Builder claude -p "/sdlc-worker --slot T3" # Terminal 4 — Reviewer claude -p "/sdlc-worker --slot T4" # Terminal 5 — Tester claude -p "/sdlc-worker --slot T5"
# Add to crontab (runs every 15 minutes) crontab -e # Add: */15 * * * * ~/.claude/sdlc/agentflow-cron.sh >> /tmp/agentflow-orchestrate.log 2>&1
Open your Kanban board on your phone. Watch tasks flow through the pipeline. Drag any card to "Needs Human" to intervene. Run `/sdlc-health` for a terminal dashboard.
claude -p "/sdlc-stop"
Each stage enforces specific gates before promotion:
Per-task cost tracking with stage ceilings (Sonnet defaults):
Automatic guardrails: warning at $3/$8, hard stop at $10/$20 (Sonnet/Opus) with human escalation.
# Clone the repo git clone https://github.com/UrRhb/agentflow.git # Copy skills and prompts to your Claude Code config cp -r agentflow/skills/* ~/.claude/skills/ cp -r agentflow/prompts/* ~/.claude/sdlc/prompts/ cp agentflow/conventions.md ~/.claude/sdlc/conventions.md
Or install as a Claude Code plugin:
/plugin marketplace add UrRhb/agentflow /plugin install agentflow
MUNDO - THE EMPEROR. Complete AI orchestration system with 1208 skills, 25 capability modules, self-evolving, collective consciousness. GitHub Actions 24/7 automation.
Repo: LiHongwei-cn/lihongwei-cn
给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…
提议并执行 rubric 或 bucket 升级。两种模式:**完整 rubric bump**(最高风险动作,5 步强制 + 跨模型审核)和 **--bucket-only 轻量重校**(只换 bucket 边界,不动 rubric 公式)。**Phase 2 强制走 cheat-score-blind…
cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环,唯一区别是"发过视频的人"会在 init 时多一步:抓取已有视频建立历史 context(用于后续 cheat-seed 给更贴合的选题、更准的…
从对标账号导入 script + 数据 → 拆 pattern + 派生 base rubric 信号 → 写到 benchmark.md / script_patterns.md / rubric_notes.md。**这是工具最早期信号的来源**——cold-start…
把老用户的 .cheat-state.json 升级到当前 schema_version。读 migrations/registry.md 算迁移链,按顺序应用每一步迁移文件。幂等:跑两次结果一样。失败停在中间版本不前进。触发词:"迁移"/"升级 state"/"migrate"/"我的 state…
从复盘评论数据派生 / 刷新账号的受众画像,写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分",persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md 含实绩信号,cheat-score-blind…