retro — ai-assistant iris-system, community, ai-assistant, ide skills, ai-automation, ai-development, automation-framework, claude-code, magi-system, personal-assistant, Claude Code

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关于此技能

🧠 AI Personal Assistant System built with Claude Code - Complete documentation of the MAGI System architecture and Iris development methodology

# 核心主题

lmanchu lmanchu
[6]
[2]
更新于: 3/21/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 3/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Quality floor passed for review
Review Score
3/11
Quality Score
65
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🧠 AI Personal Assistant System built with Claude Code - Complete documentation of the MAGI System architecture and Iris development methodology

核心价值

🧠 AI Personal Assistant System built with Claude Code - Complete documentation of the MAGI System architecture and Iris development methodology

适用 Agent 类型

Suitable for operator workflows that need explicit guardrails before installation and execution.

赋予的主要能力 · retro

! 使用限制与门槛

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The page lacks a strong recommendation layer.
  • - The page lacks concrete use-case guidance.
  • - The page lacks explicit limitations or caution signals.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

retro 是什么?

🧠 AI Personal Assistant System built with Claude Code - Complete documentation of the MAGI System architecture and Iris development methodology

如何安装 retro?

运行命令:npx killer-skills add lmanchu/iris-system/retro。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

retro 支持哪些 IDE 或 Agent?

该技能兼容 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer。可使用 Killer-Skills CLI 一条命令通用安装。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add lmanchu/iris-system/retro。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    retro 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

retro

安装 retro,这是一款面向AI agent workflows and automation的 AI Agent Skill。支持 Claude Code、Cursor、Windsurf,一键安装。

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

/retro — Weekly Engineering Retrospective

Generates a comprehensive engineering retrospective analyzing commit history, work patterns, and code quality metrics. Designed for a senior IC/CTO-level builder using Claude Code as a force multiplier.

User-invocable

When the user types /retro, run this skill.

Arguments

  • /retro — default: last 7 days
  • /retro 24h — last 24 hours
  • /retro 14d — last 14 days
  • /retro 30d — last 30 days
  • /retro compare — compare current window vs prior same-length window
  • /retro compare 14d — compare with explicit window

Instructions

Parse the argument to determine the time window. Default to 7 days if no argument given. Use --since="N days ago", --since="N hours ago", or --since="N weeks ago" (for w units) for git log queries. All times should be reported in Pacific time (use TZ=America/Los_Angeles when converting timestamps).

Argument validation: If the argument doesn't match a number followed by d, h, or w, the word compare, or compare followed by a number and d/h/w, show this usage and stop:

Usage: /retro [window]
  /retro              — last 7 days (default)
  /retro 24h          — last 24 hours
  /retro 14d          — last 14 days
  /retro 30d          — last 30 days
  /retro compare      — compare this period vs prior period
  /retro compare 14d  — compare with explicit window

Step 1: Gather Raw Data

First, fetch origin to ensure we have the latest:

bash
1git fetch origin main --quiet

Run ALL of these git commands in parallel (they are independent):

bash
1# 1. All commits in window with timestamps, subject, hash, files changed, insertions, deletions 2git log origin/main --since="<window>" --format="%H|%ai|%s" --shortstat 3 4# 2. Per-commit test vs total LOC breakdown (single command, parse output) 5# Each commit block starts with COMMIT:<hash>, followed by numstat lines. 6# Separate test files (matching test/|spec/|__tests__/) from production files. 7git log origin/main --since="<window>" --format="COMMIT:%H" --numstat 8 9# 3. Commit timestamps for session detection and hourly distribution 10# Use TZ=America/Los_Angeles for Pacific time conversion 11TZ=America/Los_Angeles git log origin/main --since="<window>" --format="%at|%ai|%s" | sort -n 12 13# 4. Files most frequently changed (hotspot analysis) 14git log origin/main --since="<window>" --format="" --name-only | grep -v '^$' | sort | uniq -c | sort -rn 15 16# 5. PR numbers from commit messages (extract #NNN patterns) 17git log origin/main --since="<window>" --format="%s" | grep -oE '#[0-9]+' | sed 's/^#//' | sort -n | uniq | sed 's/^/#/'

Step 2: Compute Metrics

Calculate and present these metrics in a summary table:

MetricValue
Commits to mainN
PRs mergedN
Total insertionsN
Total deletionsN
Net LOC addedN
Test LOC (insertions)N
Test LOC ratioN%
Version rangevX.Y.Z.W → vX.Y.Z.W
Active daysN
Detected sessionsN
Avg LOC/session-hourN

Step 3: Commit Time Distribution

Show hourly histogram in Pacific time using bar chart:

Hour  Commits  ████████████████
 00:    4      ████
 07:    5      █████
 ...

Identify and call out:

  • Peak hours
  • Dead zones
  • Whether pattern is bimodal (morning/evening) or continuous
  • Late-night coding clusters (after 10pm)

Step 4: Work Session Detection

Detect sessions using 45-minute gap threshold between consecutive commits. For each session report:

  • Start/end time (Pacific)
  • Number of commits
  • Duration in minutes

Classify sessions:

  • Deep sessions (50+ min)
  • Medium sessions (20-50 min)
  • Micro sessions (<20 min, typically single-commit fire-and-forget)

Calculate:

  • Total active coding time (sum of session durations)
  • Average session length
  • LOC per hour of active time

Step 5: Commit Type Breakdown

Categorize by conventional commit prefix (feat/fix/refactor/test/chore/docs). Show as percentage bar:

feat:     20  (40%)  ████████████████████
fix:      27  (54%)  ███████████████████████████
refactor:  2  ( 4%)  ██

Flag if fix ratio exceeds 50% — this signals a "ship fast, fix fast" pattern that may indicate review gaps.

Step 6: Hotspot Analysis

Show top 10 most-changed files. Flag:

  • Files changed 5+ times (churn hotspots)
  • Test files vs production files in the hotspot list
  • VERSION/CHANGELOG frequency (version discipline indicator)

Step 7: PR Size Distribution

From commit diffs, estimate PR sizes and bucket them:

  • Small (<100 LOC)
  • Medium (100-500 LOC)
  • Large (500-1500 LOC)
  • XL (1500+ LOC) — flag these with file counts

Step 8: Focus Score + Ship of the Week

Focus score: Calculate the percentage of commits touching the single most-changed top-level directory (e.g., app/services/, app/views/). Higher score = deeper focused work. Lower score = scattered context-switching. Report as: "Focus score: 62% (app/services/)"

Ship of the week: Auto-identify the single highest-LOC PR in the window. Highlight it:

  • PR number and title
  • LOC changed
  • Why it matters (infer from commit messages and files touched)

If the time window is 14 days or more, split into weekly buckets and show trends:

  • Commits per week
  • LOC per week
  • Test ratio per week
  • Fix ratio per week
  • Session count per week

Step 10: Streak Tracking

Count consecutive days with at least 1 commit to origin/main, going back from today:

bash
1# Get all unique commit dates (Pacific time) — no hard cutoff 2TZ=America/Los_Angeles git log origin/main --format="%ad" --date=format:"%Y-%m-%d" | sort -u

Count backward from today — how many consecutive days have at least one commit? This queries the full history so streaks of any length are reported accurately. Display: "Shipping streak: 47 consecutive days"

Step 11: Load History & Compare

Before saving the new snapshot, check for prior retro history:

bash
1ls -t .context/retros/*.json 2>/dev/null

If prior retros exist: Load the most recent one using the Read tool. Calculate deltas for key metrics and include a Trends vs Last Retro section:

                    Last        Now         Delta
Test ratio:         22%    →    41%         ↑19pp
Sessions:           10     →    14          ↑4
LOC/hour:           200    →    350         ↑75%
Fix ratio:          54%    →    30%         ↓24pp (improving)
Commits:            32     →    47          ↑47%
Deep sessions:      3      →    5           ↑2

If no prior retros exist: Skip the comparison section and append: "First retro recorded — run again next week to see trends."

Step 12: Save Retro History

After computing all metrics (including streak) and loading any prior history for comparison, save a JSON snapshot:

bash
1mkdir -p .context/retros

Determine the next sequence number for today (substitute the actual date for $(date +%Y-%m-%d)):

bash
1# Count existing retros for today to get next sequence number 2today=$(TZ=America/Los_Angeles date +%Y-%m-%d) 3existing=$(ls .context/retros/${today}-*.json 2>/dev/null | wc -l | tr -d ' ') 4next=$((existing + 1)) 5# Save as .context/retros/${today}-${next}.json

Use the Write tool to save the JSON file with this schema:

json
1{ 2 "date": "2026-03-08", 3 "window": "7d", 4 "metrics": { 5 "commits": 47, 6 "prs_merged": 12, 7 "insertions": 3200, 8 "deletions": 800, 9 "net_loc": 2400, 10 "test_loc": 1300, 11 "test_ratio": 0.41, 12 "active_days": 6, 13 "sessions": 14, 14 "deep_sessions": 5, 15 "avg_session_minutes": 42, 16 "loc_per_session_hour": 350, 17 "feat_pct": 0.40, 18 "fix_pct": 0.30, 19 "peak_hour": 22 20 }, 21 "version_range": ["1.16.0.0", "1.16.1.0"], 22 "streak_days": 47, 23 "tweetable": "Week of Mar 1: 47 commits, 3.2k LOC, 38% tests, 12 PRs, peak: 10pm" 24}

Step 13: Write the Narrative

Structure the output as:


Tweetable summary (first line, before everything else):

Week of Mar 1: 47 commits, 3.2k LOC, 38% tests, 12 PRs, peak: 10pm | Streak: 47d

Engineering Retro: [date range]

Summary Table

(from Step 2)

(from Step 11, loaded before save — skip if first retro)

Time & Session Patterns

(from Steps 3-4)

Narrative interpreting what the patterns mean:

  • When the most productive hours are and what drives them
  • Whether sessions are getting longer or shorter over time
  • Estimated hours per day of active coding
  • How this maps to "CEO who also codes" lifestyle

Shipping Velocity

(from Steps 5-7)

Narrative covering:

  • Commit type mix and what it reveals
  • PR size discipline (are PRs staying small?)
  • Fix-chain detection (sequences of fix commits on the same subsystem)
  • Version bump discipline

Code Quality Signals

  • Test LOC ratio trend
  • Hotspot analysis (are the same files churning?)
  • Any XL PRs that should have been split

Focus & Highlights

(from Step 8)

  • Focus score with interpretation
  • Ship of the week callout

Top 3 Wins

Identify the 3 highest-impact things shipped in the window. For each:

  • What it was
  • Why it matters (product/architecture impact)
  • What's impressive about the execution

3 Things to Improve

Specific, actionable, anchored in actual commits. Phrase as "to get even better, you could..."

3 Habits for Next Week

Small, practical, realistic for a very busy person. Each must be something that takes <5 minutes to adopt.

(if applicable, from Step 9)


Compare Mode

When the user runs /retro compare (or /retro compare 14d):

  1. Compute metrics for the current window (default 7d) using --since="7 days ago"
  2. Compute metrics for the immediately prior same-length window using both --since and --until to avoid overlap (e.g., --since="14 days ago" --until="7 days ago" for a 7d window)
  3. Show a side-by-side comparison table with deltas and arrows
  4. Write a brief narrative highlighting the biggest improvements and regressions
  5. Save only the current-window snapshot to .context/retros/ (same as a normal retro run); do not persist the prior-window metrics.

Tone

  • Encouraging but candid, no coddling
  • Specific and concrete — always anchor in actual commits/code
  • Skip generic praise ("great job!") — say exactly what was good and why
  • Frame improvements as leveling up, not criticism
  • Keep total output around 2500-3500 words
  • Use markdown tables and code blocks for data, prose for narrative
  • Output directly to the conversation — do NOT write to filesystem (except the .context/retros/ JSON snapshot)

Important Rules

  • ALL narrative output goes directly to the user in the conversation. The ONLY file written is the .context/retros/ JSON snapshot.
  • Use origin/main for all git queries (not local main which may be stale)
  • Convert all timestamps to Pacific time for display (use TZ=America/Los_Angeles)
  • If the window has zero commits, say so and suggest a different window
  • Round LOC/hour to nearest 50
  • Treat merge commits as PR boundaries
  • Do not read CLAUDE.md or other docs — this skill is self-contained
  • On first run (no prior retros), skip comparison sections gracefully

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