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Retrospective Analysis

该 Skill 是一个纯 Python 项目回顾(复盘)自动化库,支持创建回顾、记录做得好、偏慢、失败项及门禁摩擦,统计问题数并判定低/中/高/严重四级严重度,生成改进候选、建议与行动项报告,并通过 JSON 文件保存、加载和导出回顾数据,宣称支持并发操作。

terr123123
terr123123
热度 11.06
最近更新 2026-07-03 08:45:15
分类:协作依赖:代码执行 / 读写文件应用场景:报告撰写 / 会议纪要 / 工作流 / 任务管理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add retrospective-analysis
Metadata
name
retrospective-analysis
version
1.0.0
description
A project retrospective automation tool for friction identification, failure analysis, and improvement generation.
author
Terr123123
license
MIT
tags
["retrospective","analysis","improvement","process-optimization"]
repo
https://github.com/Terr123123/openclaw-skills/tree/main/business-stack/retrospective-analysis

Retrospective Analysis Skill

Overview

A project retrospective automation tool that helps teams identify process friction points, analyze failure causes, and generate actionable improvement candidates.

Key Features

  • Structured Retrospectives: Track what went well, what was slow, what failed, and gate friction.
  • Automatic Analysis: Compute issue counts, friction points, and severity (low/medium/high/critical).
  • Improvement Generation: Derive prioritized improvement candidates from frictions and failures.
  • Report Generation: Produce recommendations and action items from a retrospective.
  • JSON Persistence: Save/load retrospectives and export reports to disk.
  • Thread Safety: Concurrent adds and retrospective creation are safe.

Use Cases

  • Post-change retrospective record keeping
  • Process friction identification across gate checkpoints
  • Continuous improvement candidate backlog generation
  • Team retrospective reporting and archival

Installation

clawhub install retrospective-analysis

Usage

Basic Usage

from src import RetrospectiveAnalyzer
from src.models import ProjectInfo, GateFriction

analyzer = RetrospectiveAnalyzer()

project = ProjectInfo(name="Auth Refactor", team="Platform", duration="3 weeks", change_id="CH-42")
retro = analyzer.start_retrospective(project)

analyzer.add_what_went_well(retro.id, "fast design review")
analyzer.add_what_was_slow(retro.id, "manual test gate")
analyzer.add_what_failed(retro.id, "deploy rollback")
analyzer.add_gate_friction(
    retro.id,
    GateFriction(gate="testing-gate", issue="flaky tests", impact="blocked merge", suggested_change="stabilize suite"),
)

analysis = analyzer.analyze(retro.id)
print(analysis.severity, analysis.total_issues)

report = analyzer.generate_report(retro.id)
for item in report.action_items:
    print(item)

Persistence

analyzer.save_to_file(retro.id, "retro.json")
loaded = analyzer.load_from_file("retro.json")
analyzer.export_report(retro.id, "report.json")

API Reference

RetrospectiveAnalyzer

  • start_retrospective(project_info) -> Retrospective — start a new retrospective
  • add_what_went_well(retro_id, item) -> None — record something that went well
  • add_what_was_slow(retro_id, item) -> None — record something slow/redundant
  • add_what_failed(retro_id, item) -> None — record a failure/rework cause
  • add_gate_friction(retro_id, friction) -> None — record gate friction
  • analyze(retro_id) -> AnalysisResult — analyze the retrospective
  • generate_report(retro_id) -> RetrospectiveReport — generate a report
  • get_improvement_candidates(retro_id) -> List[ImprovementCandidate] — get candidates
  • archive(retro_id) -> None — archive the retrospective
  • save_to_file(retro_id, path) / load_from_file(path) / export_report(retro_id, path) — persistence

Models

  • RetroStatus: ACTIVE / ANALYZED / REPORTED / ARCHIVED
  • ProjectInfo: name, team, duration, change_id
  • GateFriction: gate, issue, impact, suggested_change
  • ImprovementCandidate: target, recommendation, reason, priority
  • AnalysisResult: total_issues, friction_points, improvement_candidates, summary, severity
  • Retrospective: id, project_info, status, what_went_well, what_was_slow, what_failed, gate_frictions, created_at
  • RetrospectiveReport: retro_id, project_info, analysis, recommendations, action_items

Severity Logic

Total Issues Severity
0 low
1–4 medium
5–9 high
10+ critical

Quality Metrics

  • 81 comprehensive tests (unit + extended unit + integration + e2e)
  • JSON serialization round-trips for all models
  • Concurrent operation safety
  • MIT License

Changelog

v1.0.0 (2026-07-03)

  • Initial release
  • Retrospective lifecycle management
  • Friction identification and failure analysis
  • Improvement candidate generation with priority
  • Report generation and JSON persistence