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Agent Skill · 开发

speculative-pipeline(投机管道)

speculative-pipeline 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

speculative-pipeline 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 18,616。

总安装 326周安装 14GitHub Stars 798下载 114核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:speculative-pipeline(投机管道)
来源仓库:https://github.com/juliusbrussee/cavekit
仓库路径:skills/speculative-pipeline
安装命令:
npx skills add https://github.com/juliusbrussee/cavekit --skill speculative-pipeline
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/juliusbrussee/cavekit --skill speculative-pipeline

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

speculative-pipeline 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中整理项目状态。适用于围绕仓库变更、协作事项或代码状态进行信息梳理的场景。可通过安装命令或访问原始仓库获取具体功能说明。

使用前需确认权限范围、维护状态及是否涉及敏感数据访问。建议结合真实环境测试其准确性和稳定性。

来源仓库内路径:skills/speculative-pipeline

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/juliusbrussee/cavekit --skill speculative-pipe…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

speculative-pipeline 怎么安装?

在终端执行 npx skills add https://github.com/juliusbrussee/cavekit --skill speculative-pipeline。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

speculative-pipeline 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

speculative-pipeline 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/juliusbrussee/cavekit --skill speculative-pipeline 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

speculative-pipeline 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

speculative-pipeline 用的人多吗?

公开数据是累计安装 326 次,近一周安装 14 次,下载 114 次,GitHub 798 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

speculative-pipeline 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
speculative-pipeline

投机管道 · 本页

326798执行命令未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Speculative-pipeline Strategy

Run pipeline stages with staggered timing instead of sequentially. The leader starts first; followers start after a configurable delay and build from whatever upstream output exists at that point. Combined with convergence loops, followers self-correct as upstream artifacts arrive and stabilize.

Core Principle

Start downstream work early with partial upstream output. Convergence loops correct the errors introduced by working from incomplete input.

The insight is that waiting for perfect upstream output is wasteful. A follower working from 80% of the upstream artifacts will produce output that is ~60-70% correct on the first pass. But with convergence loops running, the follower re-reads the upstream artifacts on each iteration and corrects course. By the time the leader finishes, the follower is already most of the way done.


The Pattern

Sequential (Traditional)

Stage 1: Specs     ████████████████████                          (5 hours)
Stage 2: Plans                         ████████████████          (4 hours)
Stage 3: Implement                                     ████████  (3 hours)
                   ─────────────────────────────────────────────
                   Total: 12 hours

Speculative-pipeline (Staggered)

Stage 1: Specs     ████████████████████                          (5 hours)
Stage 2: Plans            ████████████████                       (4 hours, started 1.5h after Stage 1)
Stage 3: Implement              ████████████                     (3 hours, started 3h after Stage 1)
                   ─────────────────────────────────────────────
                   Total: ~7 hours

Why It Works

  1. Stage 2 starts after a 1.5-hour offset. By then, Stage 1 has produced a meaningful set of partial specs.
  2. Stage 2 generates plans from whatever specs are available. Some plans will be built on incomplete information.
  3. Stage 1 keeps refining specs. When Stage 2 loops back for its next pass, it picks up the newly completed specs and adjusts its plans accordingly.
  4. Stage 3 starts after a 3-hour offset. By then, both specs and plans exist in draft form.
  5. All stages self-correct through iteration. Each pass re-reads the latest upstream artifacts. Mistakes caused by working from partial input are washed out on subsequent passes.

The key mechanism is convergence -- the iterative loop that re-reads inputs each pass. Without convergence loops, speculative-pipeline would produce garbage. With them, early errors wash out over iterations.


Example: 3-Stage Pipeline

Directory Structure

context/
├── specs/              # Stage 1 output: implementation-agnostic specs
├── plans/              # Stage 2 output: framework-specific plans
├── impl/               # Stage 3 output: implementation tracking
└── prompts/
    ├── 001-generate-specs.md       # Stage 1 prompt
    ├── 002-generate-plans.md       # Stage 2 prompt
    └── 003-implement.md            # Stage 3 prompt

Terminal Commands

Open three terminal windows (or use tmux panes):

# Terminal 1: Specs from reference materials (leader -- starts immediately)
{LOOP_TOOL} context/prompts/001-generate-specs.md -n 5 -t 2h

# Terminal 2: Plans from specs (follower -- starts after 1-hour delay)
{LOOP_TOOL} context/prompts/002-generate-plans.md -n 5 -t 2h -d 1h

# Terminal 3: Implementation from plans (follower -- starts after 2-hour delay)
{LOOP_TOOL} context/prompts/003-implement.md -n 10 -t 1h -d 2h

Parameter reference:

  • -n 5 -- Run up to 5 convergence iterations
  • -t 2h -- Time budget per iteration (max total time = iterations x budget)
  • -d 1h -- Delay before starting (speculative-pipeline offset)

Replace {LOOP_TOOL} with your convergence loop runner (any script or tool that repeatedly executes a prompt against the codebase, committing between iterations).

What Happens Chronologically

TimeStage 1 (Specs)Stage 2 (Plans)Stage 3 (Implement)
0:00Starts. Reads refs, begins generating specs.Waiting (1.5h delay).Waiting (3h delay).
1:30Iteration 1 complete. ~50% of specs written. Committed.Starts. Reads partial specs, begins generating plans.Waiting.
3:00Iteration 2. Specs ~80% complete.Iteration 1 complete. Plans based on partial specs. Some plans will need correction.Starts. Reads partial specs + plans, begins implementing.
4:00Iteration 3. Specs ~92% complete, converging.Iteration 2. Re-reads updated specs. Corrects plans. Plans ~65% correct.Iteration 1 complete. Some implementation based on incomplete plans.
5:00Converged. Specs complete. Done.Iteration 3. Re-reads final specs. Plans ~88% correct.Iteration 2. Re-reads corrected plans. Fixes implementation.
5:30--Iteration 4. Plans converged. Done.Iteration 3. Implementation ~75% correct.
7:00----Iteration 4-5. Implementation converges. Done.

Result: ~7 hours total versus ~12 hours sequential.


Choosing Delay Values

The delay determines how much upstream work exists when the follower starts. Too short and the follower wastes iterations on garbage input. Too long and you lose the time savings.

Guidelines

Upstream Stage DurationRecommended DelayRationale
1-2 hours15-30 minutesShort stages produce useful partial output quickly
2-4 hours1 hourEnough time for the first iteration to complete and commit
4+ hours1-2 hoursFirst iteration should have substantial output

Rules of Thumb

  1. Delay >= 1 upstream iteration. The follower should not start until the leader has completed at least one full iteration and committed results.
  2. Delay < 50% of upstream duration. If the delay is longer than half the upstream time, the time savings are marginal.
  3. More follower iterations compensate for shorter delays. If you start the follower early (aggressive delay), give it more iterations to converge.

Multi-Stage Pipelines (4+ Stages)

For pipelines with more than 3 stages, stagger each stage relative to Stage 1:

# 5-stage pipeline example
{LOOP_TOOL} {PROMPT_001} -n 5 -t 2h           # Stage 1: starts immediately
{LOOP_TOOL} {PROMPT_002} -n 5 -t 2h -d 1h     # Stage 2: 1h delay
{LOOP_TOOL} {PROMPT_003} -n 8 -t 1h -d 2h     # Stage 3: 2h delay
{LOOP_TOOL} {PROMPT_004} -n 8 -t 1h -d 3h     # Stage 4: 3h delay
{LOOP_TOOL} {PROMPT_005} -n 10 -t 45m -d 4h   # Stage 5: 4h delay

Notice the pattern:

  • Later stages get more iterations (they need more correction cycles)
  • Later stages get shorter time budgets per iteration (less work per stage)
  • Delays increase linearly (each stage offset by roughly 1 hour)

When Speculative-pipeline Works Best

Good Fit

  • Long pipelines (3+ stages): The time savings scale with pipeline depth
  • Stages that share a git repo: Followers read upstream commits automatically
  • Stages with convergence loops: The self-correction mechanism is essential
  • Specs that are mostly stable after 1-2 iterations: Partial specs are useful early

Poor Fit

  • Stages with hard dependencies: If Stage 2 literally cannot start without Stage 1's complete output (e.g., code generation that requires a fully resolved type system), the follower will produce only errors
  • Single-iteration stages: Without convergence loops, there is no self-correction
  • Very short pipelines (2 stages, <1 hour each): The overhead of staggering is not worth the small time savings

Monitoring Speculative-pipeline Execution

What to Watch

  1. Follower diff sizes per iteration. If the follower's diffs are large on every iteration (not decreasing), it is thrashing -- the delay was too short or the upstream output is too unstable.
  2. Follower convergence rate. The follower should converge within 1-2 iterations of the leader finishing. If it takes many more, the stages may have a hard dependency.
  3. Git commit frequency. Both leader and follower should be committing regularly. If commits stall, the agent may be stuck.

Convergence Signals

A speculative-pipeline pipeline has converged when:

  • All stages have completed their iteration loops
  • The final iteration of each stage produces minimal diffs
  • Build and test gates pass on the merged output

Thrashing Detection

Thrashing = the follower keeps making large changes because upstream output keeps changing.

Signs of thrashing:

  • Follower diff sizes do not decrease across iterations
  • Follower reverts changes it made in previous iterations
  • Build failures increase instead of decreasing

Fix thrashing by:

  1. Increasing the delay (give the leader more time to stabilize)
  2. Reducing follower iterations (let upstream settle first)
  3. Adding a "wait for upstream convergence" gate between stages

Combining with Agent Teams

In multi-agent setups, speculative-pipeline applies at the pipeline level, not the agent team level:

Pipeline Level (speculative-pipeline timing):
  Stage 1 (Specs)     → Single agent or agent team
  Stage 2 (Plans)     → Single agent or agent team (starts after delay)
  Stage 3 (Implement) → Agent team dispatched via Agent tool (starts after delay)

Each stage can internally use agent teams (multiple teammates working in parallel on different domains), but the *stages themselves* are staggered using speculative-pipeline timing.

Do not confuse:

  • Leader-follower: Pipeline stages overlapping in time
  • Agent teams: Multiple agents working in parallel within a single stage

They are orthogonal and composable.


Implementation Checklist

When setting up a speculative-pipeline pipeline:

  • Define the pipeline stages (typically: specs, plans, implement)
  • Create a prompt file for each stage with explicit input/output directories
  • Ensure each stage reads from upstream directories and writes to its own directory
  • Configure convergence loop for each stage with appropriate iteration counts
  • Choose delays: first follower at ~1 upstream iteration, subsequent at ~1h increments
  • Set up terminal sessions (one per stage) or use tmux
  • Monitor: watch for convergence (decreasing diffs) vs thrashing (constant large diffs)
  • After all stages complete, run full build + test validation on the merged output

Cross-References

  • prompt-pipeline -- How to design the prompt files that each stage executes
  • convergence-monitoring -- How to detect convergence vs ceiling in each stage
  • methodology -- Where speculative-pipeline fits in the Hunt lifecycle
  • validation-first -- Validation gates that run after each stage completes
  • context-architecture -- Directory structure that stages read from and write to

平台分布

Codex

35.43%
按下载量换算40

Claude

30.22%
按下载量换算34

Cursor

16.26%
按下载量换算19

Gemini CLI

8.31%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/juliusbrussee/cavekit --skill speculative-pipeline 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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