Engineering Retrospective

VerifiedCaution

Weekly engineering retrospective analyzing commit history, work patterns, and code quality metrics with trend tracking. Team-aware with per-person contributions.

Sby Skills Guide Bot
ProductivityIntermediate
7/25/2026
Claude Code
#engineering-retrospective#git-analysis#team-metrics#trend-tracking#code-quality

Recommended for

Automated summary

Generates a weekly engineering retrospective from commit history, work patterns, and code-quality metrics, with persistent trend tracking and per-contributor feedback.

Strengths

  • Analyzes commit history and code-quality metrics instead of producing only a qualitative summary.
  • Stores persistent history so week-over-week trends can be compared.
  • Accounts for the team by giving each contributor individual praise and growth areas.
  • Uses shell commands and interactive questions within Claude Code.

Limitations

  • Tightly coupled to the gstack ecosystem (update checks, session files, contributor reports), which can add dependencies and noise.
  • Per-person insights depend on accurate commit attribution and a sufficiently rich Git history.
  • The provided SKILL.md is a partial excerpt and does not fully spell out the analysis and report-generation workflow.
When to use it

Use at the end of a week to generate a team retrospective, compare commit trends over time, and prepare individual praise and growth feedback.

When not to use it

Avoid it for one-off code review, security auditing, or repositories with little usable commit history.

Security analysis

Caution
Quality score86/100

The skill is a git-history analytics workflow: it shells out to git, reads a few local files, and writes a JSON snapshot plus optional contributor logs. There is no exfiltration, no curl|sh, no destructive deletion, and no safety-disabling. The one real concern is the preamble's dependency on an external self-updating binary that can trigger an automated upgrade flow, which is a supply-chain/discretion issue rather than malice — hence 'caution' rather than 'risky'.

Findings
  • •Preamble executes an external, user-writable binary (`~/.claude/skills/gstack/bin/gstack-update-check`) and, if it prints `UPGRADE_AVAILABLE`, directs the agent to read and follow an 'inline upgrade flow' that can auto-upgrade the toolchain — self-updating code execution governed only by prior config rather than a per-run confirmation.
  • •Runs `git fetch origin main` and a batch of git history queries (network + repo metadata) — read-only and legitimate for the stated purpose, but touches remote state.
  • •Deletes files (`find ~/.gstack/sessions -mmin +120 -type f -delete`) and creates session markers/config reads outside the project directory; scope is limited to its own state dir so impact is minor.
  • •Writes files (`.context/retros/*.json`, `~/.gstack/contributor-logs/*.md`) and invokes `open` on the generated report, and incorporates repository/author metadata into output — no evidence of secrets or history being sent anywhere.
  • •Per-author performance analysis (praise/growth areas, late-night coding patterns) is aggregated from commit metadata; harmless technically but generates employee-evaluation-style content that teams may want to treat as sensitive.
  • •Preamble executes local helper binaries (`gstack-update-check`, `gstack-config`) from `~/.claude/skills/gstack/bin` or `.claude/skills/gstack/bin`, creating a local code-execution dependency if those paths are untrusted.
  • •Preamble deletes files under `~/.gstack/sessions` older than 120 minutes with `find ... -delete`; scope is limited, but destructive behavior should be disclosed.
  • •The update flow may auto-upgrade gstack when `UPGRADE_AVAILABLE` is detected, potentially modifying installed skill code if auto-upgrade is configured.
  • •Runs `git fetch origin main` and many git commands against `origin/main`; legitimate for the task, but requires network and repository access.
  • •Contributor mode writes local field reports and opens them; captured raw output or error messages could include local details if those reports are shared.
How this analysis is made

Examples

Weekly team retrospective
/retro — generate this week's engineering retrospective for the current repo, including commit trends and per-contributor praise and growth areas.
Compare weekly trends
Run /retro for the last week and compare the results with our previous retrospective history.
Code quality and team feedback
Use /retro to analyze recent commit history and code-quality metrics, then give each contributor one specific praise and one growth area.

name: retro version: 2.0.0 description: | Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware: breaks down per-person contributions with praise and growth areas. allowed-tools:

  • Bash
  • Read
  • Write
  • Glob
  • AskUserQuestion

<!-- AUTO-GENERATED from SKILL.md.tmpl — do not edit directly --> <!-- Regenerate: bun run gen:skill-docs -->

Preamble (run first)

_UPD=$(~/.claude/skills/gstack/bin/gstack-update-check 2>/dev/null || .claude/skills/gstack/bin/gstack-update-check 2>/dev/null || true)
[ -n "$_UPD" ] && echo "$_UPD" || true
mkdir -p ~/.gstack/sessions
touch ~/.gstack/sessions/"$PPID"
_SESSIONS=$(find ~/.gstack/sessions -mmin -120 -type f 2>/dev/null | wc -l | tr -d ' ')
find ~/.gstack/sessions -mmin +120 -type f -delete 2>/dev/null || true
_CONTRIB=$(~/.claude/skills/gstack/bin/gstack-config get gstack_contributor 2>/dev/null || true)

If output shows UPGRADE_AVAILABLE <old> <new>: read ~/.claude/skills/gstack/gstack-upgrade/SKILL.md and follow the "Inline upgrade flow" (auto-upgrade if configured, otherwise AskUserQuestion with 4 options, write snooze state if declined). If JUST_UPGRADED <from> <to>: tell user "Running gstack v{to} (just updated!)" and continue.

AskUserQuestion Format

ALWAYS follow this structure for every AskUserQuestion call:

  1. Context: project name, current branch, what we're working on (1-2 sentences)
  2. The specific question or decision point
  3. RECOMMENDATION: Choose [X] because [one-line reason]
  4. Lettered options: A) ... B) ... C) ...

If _SESSIONS is 3 or more: the user is juggling multiple gstack sessions and context-switching heavily. ELI16 mode — they may not remember what this conversation is about. Every AskUserQuestion MUST re-ground them: state the project, the branch, the current plan/task, then the specific problem, THEN the recommendation and options. Be extra clear and self-contained — assume they haven't looked at this window in 20 minutes.

Per-skill instructions may add additional formatting rules on top of this baseline.

Contributor Mode

If _CONTRIB is true: you are in contributor mode. When you hit friction with gstack itself (not the user's app), file a field report. Think: "hey, I was trying to do X with gstack and it didn't work / was confusing / was annoying. Here's what happened."

gstack issues: browse command fails/wrong output, snapshot missing elements, skill instructions unclear or misleading, binary crash/hang, unhelpful error message, any rough edge or annoyance — even minor stuff. NOT gstack issues: user's app bugs, network errors to user's URL, auth failures on user's site.

To file: write ~/.gstack/contributor-logs/{slug}.md with this structure:

# {Title}

Hey gstack team — ran into this while using /{skill-name}:

**What I was trying to do:** {what the user/agent was attempting}
**What happened instead:** {what actually happened}
**How annoying (1-5):** {1=meh, 3=friction, 5=blocker}

## Steps to reproduce
1. {step}

## Raw output
(wrap any error messages or unexpected output in a markdown code block)

**Date:** {YYYY-MM-DD} | **Version:** {gstack version} | **Skill:** /{skill}

Then run: mkdir -p ~/.gstack/contributor-logs && open ~/.gstack/contributor-logs/{slug}.md

Slug: lowercase, hyphens, max 60 chars (e.g. browse-snapshot-ref-gap). Skip if file already exists. Max 3 reports per session. File inline and continue — don't stop the workflow. Tell user: "Filed gstack field report: {title}"

/retro — Weekly Engineering Retrospective

Generates a comprehensive engineering retrospective analyzing commit history, work patterns, and code quality metrics. Team-aware: identifies the user running the command, then analyzes every contributor with per-person praise and growth opportunities. 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 and identify the current user:

git fetch origin main --quiet
# Identify who is running the retro
git config user.name
git config user.email

The name returned by git config user.name is "you" — the person reading this retro. All other authors are teammates. Use this to orient the narrative: "your" commits vs teammate contributions.

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

# 1. All commits in window with timestamps, subject, hash, AUTHOR, files changed, insertions, deletions
git log origin/main --since="<window>" --format="%H|%aN|%ae|%ai|%s" --shortstat

# 2. Per-commit test vs total LOC breakdown with author
#    Each commit block starts with COMMIT:<hash>|<author>, followed by numstat lines.
#    Separate test files (matching test/|spec/|__tests__/) from production files.
git log origin/main --since="<window>" --format="COMMIT:%H|%aN" --numstat

# 3. Commit timestamps for session detection and hourly distribution (with author)
#    Use TZ=America/Los_Angeles for Pacific time conversion
TZ=America/Los_Angeles git log origin/main --since="<window>" --format="%at|%aN|%ai|%s" | sort -n

# 4. Files most frequently changed (hotspot analysis)
git log origin/main --since="<window>" --format="" --name-only | grep -v '^$' | sort | uniq -c | sort -rn

# 5. PR numbers from commit messages (extract #NNN patterns)
git log origin/main --since="<window>" --format="%s" | grep -oE '#[0-9]+' | sed 's/^#//' | sort -n | uniq | sed 's/^/#/'

# 6. Per-author file hotspots (who touches what)
git log origin/main --since="<window>" --format="AUTHOR:%aN" --name-only

# 7. Per-author commit counts (quick summary)
git shortlog origin/main --since="<window>" -sn --no-merges

# 8. Greptile triage history (if available)
cat ~/.gstack/greptile-history.md 2>/dev/null || true

# 9. TODOS.md backlog (if available)
cat TODOS.md 2>/dev/null || true

Step 2: Compute Metrics

Calculate and present these metrics in a summary table:

| Metric | Value | |--------|-------| | Commits to main | N | | Contributors | N | | PRs merged | N | | Total insertions | N | | Total deletions | N | | Net LOC added | N | | Test LOC (insertions) | N | | Test LOC ratio | N% | | Version range | vX.Y.Z.W → vX.Y.Z.W | | Active days | N | | Detected sessions | N | | Avg LOC/session-hour | N | | Greptile signal | N% (Y catches, Z FPs) |

Then show a per-author leaderboard immediately below:

Contributor         Commits   +/-          Top area
You (garry)              32   +2400/-300   browse/
alice                    12   +800/-150    app/services/
bob                       3   +120/-40     tests/

Sort by commits descending. The current user (from git config user.name) always appears first, labeled "You (name)".

Greptile signal (if history exists): Read ~/.gstack/greptile-history.md (fetched in Step 1, command 8). Filter entries within the retro time window by date. Count entries by type: fix, fp, already-fixed. Compute signal ratio: (fix + already-fixed) / (fix + already-fixed + fp). If no entries exist in the window or the file doesn't exist, skip the Greptile metric row. Skip unparseable lines silently.

Backlog Health (if TODOS.md exists): Read TODOS.md (fetched in Step 1, command 9). Compute:

  • Total open TODOs (exclude items in ## Completed section)
  • P0/P1 count (critical/urgent items)
  • P2 count (important items)
  • Items completed this period (items in Completed section with dates within the retro window)
  • Items added this period (cross-reference git log for commits that modified TODOS.md within the window)

Include in the metrics table:

| Backlog Health | N open (X P0/P1, Y P2) · Z completed this period |

If TODOS.md doesn't exist, skip the Backlog Health row.

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)

Step 9: Team Member Analysis

For each contributor (including the current user), compute:

  1. Commits and LOC — total commits, insertions, deletions, net LOC
  2. Areas of focus — which directories/files they touched most (top 3)
  3. Commit type mix — their personal feat/fix/refactor/test breakdown
  4. Session patterns — when they code (their peak hours), session count
  5. Test discipline — their personal test LOC ratio
  6. Biggest ship — their single highest-impact commit or PR in the window

For the current user ("You"): This section gets the deepest treatment. Include all the detail from the solo retro — session analysis, time patterns, focus score. Frame it in first person: "Your peak hours...", "Your biggest ship..."

For each teammate: Write 2-3 sentences covering what they worked on and their pattern. Then:

  • Praise (1-2 specific things): Anchor in actual commits. Not "great work" — say exactly what was good. Examples: "Shipped the entire auth middleware rewrite in 3 focused sessions with 45% test coverage", "Every PR under 200 LOC — disciplined decomposition."
  • Opportunity for growth (1 specific thing): Frame as a leveling-up suggestion, not criticism. Anchor in actual data. Examples: "Test ratio was 12% this week — adding test coverage to the payment module before it gets more complex would pay off", "5 fix commits on the same file suggest the original PR could have used a review pass."

If only one contributor (solo repo): Skip the team breakdown and proceed as before — the retro is personal.

If there are Co-Authored-By trailers: Parse Co-Authored-By: lines in commit messages. Credit those authors for the commit alongside the primary author. Note AI co-authors (e.g., noreply@anthropic.com) but do not include them as team members — instead, track "AI-assisted commits" as a separate metric.

Step 10: Week-over-Week Trends (if window >= 14d)

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

  • Commits per week (total and per-author)
  • LOC per week
  • Test ratio per week
  • Fix ratio per week
  • Session count per week

Step 11: Streak Tracking

Count consecutive days with at least 1 commit to origin/main, going back from today. Track both team streak and personal streak:

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

# Personal streak: only the current user's commits
TZ=America/Los_Angeles git log origin/main --author="<user_name>" --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 both:

  • "Team shipping streak: 47 consecutive days"
  • "Your shipping streak: 32 consecutive days"

Step 12: Load History & Compare

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

ls -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 13: Save Retro History

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

mkdir -p .context/retros

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

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

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

{
  "date": "2026-03-08",
  "window": "7d",
  "metrics": {
    "commits": 47,
    "contributors": 3,
    "prs_merged": 12,
    "insertions": 3200,
    "deletions": 800,
    "net_loc": 2400,
    "test_loc": 1300,
    "test_ratio": 0.41,
    "active_days": 6,
    "sessions": 14,
    "deep_sessions": 5,
    "avg_session_minutes": 42,
    "loc_per_session_hour": 350,
    "feat_pct": 0.40,
    "fix_pct": 0.30,
    "peak_hour": 22,
    "ai_assisted_commits": 32
  },
  "authors": {
    "Garry Tan": { "commits": 32, "insertions": 2400, "deletions": 300, "test_ratio": 0.41, "top_area": "browse/" },
    "Alice": { "commits": 12, "insertions": 800, "deletions": 150, "test_ratio": 0.35, "top_area": "app/services/" }
  },
  "version_range": ["1.16.0.0", "1.16.1.0"],
  "streak_days": 47,
  "tweetable": "Week of Mar 1: 47 commits (3 contributors), 3.2k LOC, 38% tests, 12 PRs, peak: 10pm",
  "greptile": {
    "fixes": 3,
    "fps": 1,
    "already_fixed": 2,
    "signal_pct": 83
  }
}

Note: Only include the greptile field if ~/.gstack/greptile-history.md exists and has entries within the time window. Only include the backlog field if TODOS.md exists. If either has no data, omit the field entirely.

Include backlog data in the JSON when TODOS.md exists:

  "backlog": {
    "total_open": 28,
    "p0_p1": 2,
    "p2": 8,
    "completed_this_period": 3,
    "added_this_period": 1
  }

Step 14: Write the Narrative

Structure the output as:


Tweetable summary (first line, before everything else):

Week of Mar 1: 47 commits (3 contributors), 3.2k LOC, 38% tests, 12 PRs, peak: 10pm | Streak: 47d

Engineering Retro: [date range]

Summary Table

(from Step 2)

Trends vs Last Retro

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

Time & Session Patterns

(from Steps 3-4)

Narrative interpreting what the team-wide 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 (team aggregate)
  • Notable patterns: do team members code at the same time or in shifts?

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
  • Greptile signal ratio and trend (if history exists): "Greptile: X% signal (Y valid catches, Z false positives)"

Focus & Highlights

(from Step 8)

  • Focus score with interpretation
  • Ship of the week callout

Your Week (personal deep-dive)

(from Step 9, for the current user only)

This is the section the user cares most about. Include:

  • Their personal commit count, LOC, test ratio
  • Their session patterns and peak hours
  • Their focus areas
  • Their biggest ship
  • What you did well (2-3 specific things anchored in commits)
  • Where to level up (1-2 specific, actionable suggestions)

Team Breakdown

(from Step 9, for each teammate — skip if solo repo)

For each teammate (sorted by commits descending), write a section:

[Name]

  • What they shipped: 2-3 sentences on their contributions, areas of focus, and commit patterns
  • Praise: 1-2 specific things they did well, anchored in actual commits. Be genuine — what would you actually say in a 1:1? Examples:
    • "Cleaned up the entire auth module in 3 small, reviewable PRs — textbook decomposition"
    • "Added integration tests for every new endpoint, not just happy paths"
    • "Fixed the N+1 query that was causing 2s load times on the dashboard"
  • Opportunity for growth: 1 specific, constructive suggestion. Frame as investment, not criticism. Examples:
    • "Test coverage on the payment module is at 8% — worth investing in before the next feature lands on top of it"
    • "3 of the 5 PRs were 800+ LOC — breaking these up would catch issues earlier and make review easier"
    • "All commits land between 1-4am — sustainable pace matters for code quality long-term"

AI collaboration note: If many commits have Co-Authored-By AI trailers (e.g., Claude, Copilot), note the AI-assisted commit percentage as a team metric. Frame it neutrally — "N% of commits were AI-assisted" — without judgment.

Top 3 Team Wins

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

  • What it was
  • Who shipped it
  • Why it matters (product/architecture impact)

3 Things to Improve

Specific, actionable, anchored in actual commits. Mix personal and team-level suggestions. Phrase as "to get even better, the team could..."

3 Habits for Next Week

Small, practical, realistic. Each must be something that takes <5 minutes to adopt. At least one should be team-oriented (e.g., "review each other's PRs same-day").

Week-over-Week Trends

(if applicable, from Step 10)


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
  • Praise should feel like something you'd actually say in a 1:1 — specific, earned, genuine
  • Growth suggestions should feel like investment advice — "this is worth your time because..." not "you failed at..."
  • Never compare teammates against each other negatively. Each person's section stands on its own.
  • Keep total output around 3000-4500 words (slightly longer to accommodate team sections)
  • 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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After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code
test -f '.skill-downloads/engineering-retrospective-mrzxuqez/SKILL.md' && test -s '.skill-downloads/engineering-retrospective-mrzxuqez/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/retro' && cp -R '.skill-downloads/engineering-retrospective-mrzxuqez' '.claude/skills/retro'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation
openskills install './.skill-downloads/engineering-retrospective-mrzxuqez'

openskills read 'engineering-retrospective-mrzxuqez'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/engineering-retrospective-mrzxuqez/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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