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Engineering Retro

Community
Mathews-Tom
engineering-retro

Git-based engineering retrospective analyzing commits, PRs, and velocity over configurable windows with monorepo path scoping. Triggers on: "retrospective", "sprint retro", "weekly review", "what did we ship", "engineering retro", "dev summary", "commit analysis".

Overview

PublisherMathews-Tom
Repositoryarmory
Skill nameengineering-retro
Stars
318
Forks
47
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Mathews-Tom on GitHub. Read the source before you install it.

Installation

Install the Engineering Retro AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mathews-Tom/armory.git /tmp/armory
mkdir -p .claude/skills
cp -r /tmp/armory/skills/engineering-retro .claude/skills/engineering-retro
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Engineering Retro in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Engineering Retro on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Engineering Retro is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Engineering Retrospective

Generate a structured, git-based engineering retrospective for a configurable time window. This is a read-only analysis — no files are modified except the optional JSON snapshot.

Arguments

/engineering-retro [TIME_WINDOW] [PATH_SCOPE]
  • TIME_WINDOW (optional): 24h, 7d (default), 14d, 30d
  • PATH_SCOPE (optional): restrict analysis to a subdirectory (monorepo support), e.g. services/api

Examples:

  • /engineering-retro — last 7 days, full repo
  • /engineering-retro 30d — last 30 days, full repo
  • /engineering-retro 14d services/api — last 14 days, scoped to services/api/

Execution Steps

Step 1: Environment Detection

Detect runtime context before any analysis:

bash
# Default branch
DEFAULT_BRANCH=$(git symbolic-ref refs/remotes/origin/HEAD 2>/dev/null | sed 's@^refs/remotes/origin/@@')
if [ -z "$DEFAULT_BRANCH" ]; then
  DEFAULT_BRANCH=$(git remote show origin 2>/dev/null | grep 'HEAD branch' | awk '{print $NF}')
fi

# System timezone
TZ_NAME=$(date +%Z)

# Time window — convert argument to --since format
# 24h → "24 hours ago", 7d → "7 days ago", 14d → "14 days ago", 30d → "30 days ago"

If DEFAULT_BRANCH detection fails, abort with an error — do not guess.

Step 2: Gather Raw Git Data

Collect commits within the time window on the detected default branch:

bash
# All commits in window (with optional path scope)
git log origin/$DEFAULT_BRANCH --since="$SINCE" --format="%H|%aI|%aN|%s" -- $PATH_SCOPE

# Diff stats for the window
git log origin/$DEFAULT_BRANCH --since="$SINCE" --numstat --format="%H" -- $PATH_SCOPE

Capture: commit hash, author date (ISO), author name, subject line, files changed, insertions, deletions.

Step 3: Compute Aggregate Metrics

From the raw data, compute:

  • Total commits in window
  • Unique contributors (distinct author names)
  • Files changed (unique file paths across all commits)
  • Lines added (sum of insertions)
  • Lines removed (sum of deletions)
  • Net delta (added - removed)
  • Avg commit size (total lines changed / total commits)

Step 4: Time Distribution

Analyze commit timestamps (converted to system timezone $TZ_NAME):

  • Commits by day of week: Mon-Sun histogram
  • Commits by hour: 0-23 histogram
  • Peak day: day with most commits
  • Peak hours: hours with most activity

Present as a compact text histogram.

Step 5: Session Analysis

Group commits into work sessions using a >2 hour gap as a session boundary:

  1. Sort commits by author and timestamp
  2. For each author, iterate chronologically — if gap between consecutive commits exceeds 2 hours, start a new session
  3. Compute per-session: duration (first commit to last commit), commit count
  4. Aggregate: total sessions, average session length, longest session, average commits per session

Sessions with a single commit get a default duration of 0 (point-in-time).

Step 6: Commit Type Classification

Classify each commit using conventional commit prefixes from the subject line:

Prefix patternCategory
feat:, feat(feature
fix:, fix(, bugfixfix
refactor:, refactor(refactor
chore:, chore(, build:, ci:chore
docs:, doc:docs
test:, tests:test
perf:perf
style:style

For commits without conventional prefixes, apply diff heuristics:

  • Primarily new files added → feature
  • Primarily deletions → refactor
  • Test files only → test
  • Config/CI files only → chore
  • Documentation files only → docs
  • Otherwise → uncategorized

Report counts and percentages per category.

Step 7: Hotspot Analysis

Identify the top 10 most-modified files by number of commits touching them:

bash
git log origin/$DEFAULT_BRANCH --since="$SINCE" --name-only --format="" -- $PATH_SCOPE | sort | uniq -c | sort -rn | head -20

Flag any file modified in >50% of total commits as a hotspot. Hotspots indicate:

  • Active area of development (expected during feature work)
  • Potential coupling issues (if unrelated commits keep touching the same file)
  • Possible need for decomposition (if the file is large)

Step 8: PR Analysis

If the remote is GitHub (check git remote get-url origin for github.com):

bash
# Merged PRs in window
gh pr list --state merged --base $DEFAULT_BRANCH --search "merged:>=$SINCE_DATE" --json number,title,author,mergedAt,additions,deletions,changedFiles,reviews

Compute:

  • Total merged PRs
  • Size distribution: S (<50 lines), M (50-200), L (200-500), XL (>500)
  • Review turnaround: time from PR creation to first review (median, p90)
  • Merge turnaround: time from PR creation to merge (median, p90)

If not a GitHub remote or gh is unavailable, skip this step and note it in the output.

Step 9: Focus Score

Compute the ratio of focused commits (touching 3 or fewer files) to total commits:

focus_score = commits_touching_le_3_files / total_commits

Interpretation:

  • >0.8: highly focused, small incremental changes
  • 0.5-0.8: moderate focus, mix of targeted and broad changes
  • <0.5: broad changes dominating, may indicate large refactors or low commit discipline

Step 10: Per-Author Breakdown

For each contributor, report:

  • Commit count
  • Lines added / removed
  • Top 3 most-touched files
  • Primary commit types (from Step 6)
  • Number of sessions and average session length (from Step 5)

Frame this as contributor highlights — recognition of work done, not a ranking or performance metric. Order alphabetically by author name.

Step 11: Week-over-Week Comparison

Check for a prior snapshot in .engineering-retros/:

  • Find the most recent *.json file
  • If it exists and covers the adjacent prior window, compute deltas:
    • Commit count delta (%)
    • Lines changed delta (%)
    • Contributor count delta
    • Focus score delta
    • Category distribution shift

If no prior snapshot exists, note this is the first retrospective and skip comparison.

Step 12: Save Snapshot

Save a JSON snapshot for future comparisons:

.engineering-retros/<YYYY-MM-DD>.json

Schema:

json
{
  "date": "YYYY-MM-DD",
  "window": "7d",
  "path_scope": null,
  "branch": "main",
  "timezone": "PST",
  "metrics": {
    "commits": 0,
    "contributors": 0,
    "files_changed": 0,
    "lines_added": 0,
    "lines_removed": 0,
    "net_delta": 0,
    "focus_score": 0.0
  },
  "categories": {},
  "hotspots": [],
  "sessions": {
    "total": 0,
    "avg_length_minutes": 0
  },
  "authors": {},
  "pr_stats": null
}

Create the .engineering-retros/ directory if it does not exist. Ensure .engineering-retros/ is in .gitignore (add it if missing — this is the one permitted file modification).

Step 13: Generate Narrative Summary

Produce the final output in this structure:


Engineering Retrospective — [DATE_RANGE] ([TIMEZONE]) Branch: [DEFAULT_BRANCH] | Scope: [PATH_SCOPE or "full repo"]

Metrics
  • Commits: N | Contributors: N | Files changed: N
  • Lines: +N / -N (net: +/-N)
  • Avg commit size: N lines | Focus score: N.NN
Time Patterns
  • Peak day: [DAY] | Peak hours: [RANGE]
  • [compact histogram]
  • Sessions: N total | Avg length: Nm | Longest: Nm
Work Breakdown
  • [category]: N commits (NN%)
  • ...
Hotspots
  • path/to/file — N commits [HOTSPOT if >50%]
  • ...
Contributor Highlights
  • [Author]: N commits, +N/-N lines, focused on [top files], primarily [categories]
  • ...
PR Summary (if available)
  • Merged: N | Size dist: S/M/L/XL | Median review turnaround: Xh
Week-over-Week (if available)
  • Commits: +/-N% | Lines: +/-N% | Focus: +/-N.NN
Observations
  • [2-4 bullet points identifying patterns, achievements, and areas worth attention]
  • Based on data only — no speculation about intent or quality judgments about individuals

Constraints

  • Read-only: no code modifications, no branch changes, no git operations that alter state
  • No hardcoded timezone: always detect from date +%Z
  • No hardcoded branch: always detect dynamically via git symbolic-ref or git remote show
  • No individual performance judgments: author breakdown is for recognition, not evaluation
  • Path scope respected: all git commands must include -- $PATH_SCOPE when a scope is provided
  • Snapshot storage: .engineering-retros/ only, never .context/retros/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Engineering Retro AI skill do?

Git-based engineering retrospective analyzing commits, PRs, and velocity over configurable windows with monorepo path scoping. Triggers on: "retrospective", "sprint retro", "weekly review", "what did we ship", "engineering retro", "dev summary", "commit analysis".

Why use Engineering Retro on TypingMind?

Because you install it once and use it with any model. Engineering Retro is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Engineering Retro in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mathews-Tom/armory/tree/main/skills/engineering-retro. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Engineering Retro?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Engineering Retro?

As many as you like. As long as a model supports skills, you can use Engineering Retro with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Engineering Retro AI skill free?

Yes. It is published on GitHub by Mathews-Tom under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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