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workflow-chain工作流程链

Agent Skill

workflow-chain 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:workflow-chain(工作流程链)
来源仓库:https://github.com/samledger67-dotcom/workflow-chain
安装命令:
openclaw skills install workflow-chain
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install workflow-chain

简介

workflow-chain 将多个管道脚本整合为顺序或并行执行的工作流组合。

  • 适用于 PrecisionLedger 生态下的任务调度与资源协调场景。
  • 充当“播放列表”角色,统一管理子流程启动时机与依赖关系。
  • 安装命令为 openclaw skills install workflow-chain,需预先准备好独立脚本文件。
  • 建议监控整体执行状态,防止某环节失败影响后续链条连续性。

SKILL.md

name
workflow-chain
description
>
license
MIT
metadata
openclaw
emoji
⛓️
negative_boundaries

Workflow Chain — Multi-Pipeline Orchestrator

Chain multiple PrecisionLedger pipeline scripts into a single coordinated workflow. Think of it as a "playlist" for pipeline scripts — run them in sequence or parallel, with data flowing between steps.


Trigger

Use this skill when:

  • User says "run full close and analysis", "complete financial package", "run all pipelines for [client]"
  • A task clearly requires 2+ pipeline scripts in sequence
  • User says "chain", "workflow", "run everything", "full suite"
  • Building a reusable workflow template for a client
  • Need to coordinate parallel pipeline execution with a merge step

Do NOT use for:

  • Single pipeline tasks → use the specific skill (pl-quick-compare, month-end-close, etc.)
  • Non-pipeline work → email, web search, content creation
  • Tasks without QBO data or pipeline scripts

Architecture

Pipeline Registry

All available pipelines live in scripts/pipelines/ with a manifest at scripts/pipelines/manifest.json.

Current production pipelines (19 scripts, all have argparse):

PipelineScriptTypical Order
pl-quick-comparepl-quick-compare.py1 (income statement first)
pl-deep-analysispl-deep-analysis.py2 (after quick compare flags)
bs-quick-comparebs-quick-compare.py3 (balance sheet)
bs-deep-analysisbs-deep-analysis.py4 (after BS flags)
scf-quick-comparescf-quick-compare.py5 (cash flow)
scf-deep-analysisscf-deep-analysis.py6 (after SCF flags)
financial-ratiosfinancial-ratios.py7 (cross-statement ratios)
bank-reconciliationbank-reconciliation.pyparallel with above
payroll-reconciliationpayroll-reconciliation.pyparallel with above
ar-collectionsar-collections.pyparallel (if client has AR)
budget-builderbudget-builder.pyad-hoc
cash-flow-forecastcash-flow-forecast.pyafter close
client-dashboardclient-dashboard.pyfinal (needs all data)
doc-ingestiondoc-ingestion.pypre-close
document-ingestiondocument-ingestion.pypre-close
financial-packagefinancial-package.pystandalone TTM package
month-end-closemonth-end-close.pyorchestrates close checklist
tax-package-preptax-package-prep.pyyear-end only
vendor-compliance-1099vendor-compliance-1099.pyyear-end only

Common Arguments (all pipelines share these)

--slug <client-slug>        QBO company identifier (required)
--start YYYY-MM-DD          Period start
--end YYYY-MM-DD            Period end
--out <directory>            Output directory (default: ~/Desktop)
--sandbox                   Use QBO sandbox

Workflow Execution

Step 1: Identify the Task

Parse the user's request to determine:

  1. Client (slug) — from name/alias matching against clients/*/sop.md
  2. Period — month, quarter, or year
  3. Scope — which pipelines are needed

Step 2: Build the Chain

Read the client's SOP to determine which pipelines apply:

  • Does the client have AR? → include ar-collections
  • Does the client have payroll? → include payroll-reconciliation
  • Is this year-end? → include tax-package-prep, vendor-compliance-1099
  • Is this a close? → include month-end-close as the anchor

Step 3: Determine Execution Order

Pipelines have natural dependencies:

Layer 0 (Pre-Close — parallel):
  doc-ingestion
  bank-reconciliation
  payroll-reconciliation

Layer 1 (Close):
  month-end-close (reads outputs from Layer 0)

Layer 2 (Analysis — parallel):
  pl-quick-compare
  bs-quick-compare
  scf-quick-compare

Layer 3 (Deep Analysis — parallel, depends on Layer 2 flags):
  pl-deep-analysis (only if PL flags > 0)
  bs-deep-analysis (only if BS flags > 0)
  scf-deep-analysis (only if SCF flags > 0)

Layer 4 (Cross-Statement):
  financial-ratios
  cash-flow-forecast

Layer 5 (Delivery):
  client-dashboard
  financial-package

Step 4: Execute

For each layer:

  1. Run all pipelines in that layer (parallel where possible via sub-agents)
  2. Check exit codes — if any pipeline fails, log the error and continue (don't block the chain)
  3. Pass shared arguments (slug, dates, output dir) to each pipeline
  4. Collect outputs (Excel files, JSON caches, manifests)

Step 5: Report

After all layers complete, produce a summary:

  • Which pipelines ran ✅
  • Which failed ❌ (with error)
  • Which were skipped ⏭️ (not applicable per SOP)
  • Output file locations
  • Total execution time

Pre-Built Templates

Template: Full Monthly Close

Layers: 0 → 1 → 2 → 3 → 4 → 5
Pipelines: bank-rec → close → PL/BS/SCF quick → deep (if flagged) → ratios → dashboard
Trigger: "full close for [client]", "complete close [month]"

Template: Quick Analysis Package

Layers: 2 → 4 → 5
Pipelines: PL/BS/SCF quick compare → ratios → dashboard
Trigger: "quick analysis for [client]", "variance package [month]"

Template: Year-End Tax Package

Layers: 0 → 1 → 2 → tax-specific
Pipelines: bank-rec → close → PL → tax-package-prep → vendor-1099
Trigger: "tax package for [client]", "year-end prep [year]"

Template: Deep Dive (Single Statement)

Layers: quick → deep
Pipelines: [statement]-quick-compare → [statement]-deep-analysis
Trigger: "deep dive P&L", "analyze balance sheet in detail"

Template: Financial Package (TTM)

Layers: single
Pipelines: financial-package.py (self-contained TTM generator)
Trigger: "financial package", "TTM statements"

Example Usage

  • "Run the full monthly close and analysis package for Acme for March 2026."
  • "Chain PL, BS, and SCF quick compare, then build the dashboard."
  • "Create a reusable year-end tax workflow for this client."

Execution Commands

# Common pattern for all pipelines
SCRIPTS=~/.openclaw/workspace/scripts/pipelines
SLUG="my-client"
MONTH_START="2026-03-01"
MONTH_END="2026-03-31"
OUT=~/Desktop/close-$SLUG-$(date +%Y%m)

# Layer 0 (parallel)
python3 $SCRIPTS/bank-reconciliation.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT &
python3 $SCRIPTS/payroll-reconciliation.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT &
wait

# Layer 1
python3 $SCRIPTS/month-end-close.py --slug $SLUG --month ${MONTH_START:0:7} --out $OUT

# Layer 2 (parallel)
python3 $SCRIPTS/pl-quick-compare.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT &
python3 $SCRIPTS/bs-quick-compare.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT &
python3 $SCRIPTS/scf-quick-compare.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT &
wait

# Layer 3 (conditional)
# Only run deep analysis if quick compare flagged material items
python3 $SCRIPTS/pl-deep-analysis.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT
python3 $SCRIPTS/bs-deep-analysis.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT
python3 $SCRIPTS/scf-deep-analysis.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT

# Layer 4
python3 $SCRIPTS/financial-ratios.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT
python3 $SCRIPTS/cash-flow-forecast.py --slug $SLUG --out $OUT

# Layer 5
python3 $SCRIPTS/client-dashboard.py --slug $SLUG --start $MONTH_START --end $MONTH_END --out $OUT

Client SOP Integration

Before running any chain, read clients/{slug}/sop.md to check:

  1. Which reports are relevant — not every client needs AR aging or payroll rec
  2. Special instructions — some clients have non-standard periods or reporting requirements
  3. Key financial characteristics — informs which deep dives to prioritize
  4. Schedule — when the client expects deliverables

The SOP is the authority. If the SOP says "no AR aging" (like SB Paulson — POS collection), skip ar-collections even in a full close chain.


Error Handling

  • Pipeline fails: Log error, mark as ❌, continue chain. Don't block subsequent layers unless the failed pipeline is a hard dependency.
  • QBO token expired: Run node integrations/qbo-client/bin/qbo connect <slug> to refresh. Auto-detected by 401 response.
  • Missing data: Some pipelines produce empty results for new clients. That's OK — the dashboard handles nulls gracefully.
  • Timeout: Each pipeline has a 5-minute max. If exceeded, kill and mark as timed out.

Output Convention

All chain outputs go to a single directory:

~/Desktop/close-{slug}-{YYYYMM}/
├── PLCompare_{slug}_*.xlsx
├── BSCompare_{slug}_*.xlsx
├── SCFCompare_{slug}_*.xlsx
├── PLDeep_{slug}_*.xlsx
├── BSDeep_{slug}_*.xlsx
├── SCFDeep_{slug}_*.xlsx
├── FinancialRatios_{slug}_*.xlsx
├── BankRec_{slug}_*.xlsx
├── PayrollRec_{slug}_*.xlsx
├── ClientDashboard_{slug}_*.xlsx
├── CashFlow_{slug}_*.xlsx
├── chain-summary.json          ← workflow metadata
└── chain-log.txt               ← execution log

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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