retro
Use when the user asks for a retrospective on an agent session ("/retro", "how did this session go", "what could this session do better"), for the current…
Skill by lwlee2608
Chalk Labs
Need help deploying retro?
Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/lwlee2608-retro
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
Get the weekly diff
The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.
About
Use cases: testing, agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/retro
What's inside
- Skill name
retro- Description
- Use when the user asks for a retrospective on an agent session ("/retro", "how did this session go", "what could this session do better"), for the current session or a given session ID or transcript path. Reports time per phase, tool-call errors, repeated slow test runs, and skill inefficiencies. Works for Claude Code and pi.
- License
- MIT
- Instructions
- 85 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Retro
Review a session transcript. Report how well it ran, where the time went, and what to change next time.
## Rules
1. **Default to the current session.** Find it by this session's ID, never by the newest file; parallel sessions share a project folder. Accept a session ID or a transcript path instead. Include subagent transcripts, since their work counts too. OpenCode keeps sessions in a database, so say it is not supported.
2. **Condense the transcript before you read it.** Transcripts run to megabytes, mostly tool output. Extract a short timeline: each user prompt, and each tool call with its time, duration, input, and error. Read a full result only when one error needs it.
3. **Time each phase.** Phases are understanding, implementing, testing, reviewing, and other (git, CI, deploy). A phase lasts until the next one starts. Time waiting for the user is not agent time; report it apart. Questions to the user and permission prompts count as user wait. Background tasks finish later than their call shows, so use their completion notice. Skip this retro's own calls.
4. **Flag slow checks run again for no reason.** Slow means Playwright, e2e, integration, full suites, or CI watches. A rerun is waste when no covered code changed, or a narrower check would have proved the fix. A rerun after a relevant change is fine.
5. **Group tool-call errors by cause.** Examples: bad path, bad args, permission denied, missing command. A failing call retried unchanged matters most.
6. **Find where effort went to waste.** Look for:
- skill steps ignored, or skill steps that cause waste
- the same lookup repeated
- whole files read when a search would do
- independent calls run one at a time
- subagents for tiny tasks
- user corrections ("no", "I said", "again")
Tie each to the skill, instruction file, or habit that should change.
Report only improvements worth the user's effort to make; drop nitpicks and one-off slips. None is fine.
7. **Report only; never edit.** The user decides what to change.
8. **Grade by share of agent time wasted.** A: none. B: under 10%. C: 10–25%. D: 25–50%. F: over 50%, or the task failed.
9. **Chart the subagents when the session spawned any.** One row per agent, nested under its spawner, with bars on a shared time axis: `█` working, `░` alive but waiting on another agent. Overlapping bars ran in parallel; staggered bars ran in sequence. End each row with agent time, which counts only `█`, so parallel agents can sum past wall time. Then add the task or outcome and any waste tag. Render the chart with a script from the condensed timeline; never place bars by hand. Scale it to about 60 columns. Group `implement-plan-feature` runs by phase. Example, an `implement-plan-feature` orchestrator run at 2 minutes per column:
```
10:00 10:30 11:00 11:30
you orchestrator █░░░░░░░░░░░░░░█░░░█░░█░░█░█░██░░░░░░░░░░░░░░█░███ 16m
├─ phase 1
│ ├─ W1 worker ██████████████░░░░███░░░██░░ 39m
│ ├─ R1 review ███ 6m 3 findings
│ ├─ R2 review ██ 5m 1 finding
│ └─ R3 review ██ 4m clean
├─ phase 2a
│ ├─ W2 worker ██████░░█░░███░░ 19m waste: e2e 3×, +10m
│ ├─ R1 review ██ 5m clean
│ └─ R2 review ██ 4m clean
└─ phase 2b
├─ W3 worker ███████░░░██░░█░░░ 21m
├─ R1 review ███ 6m 2 findings
└─ R2 review ██ 4m clean
```
Subagents the harness spawned on its own have no phases; list them in spawn order, with the agent type as role. Example at 1 minute per column:
```
14:00 14:15 14:30 14:45
you main session ███░░░█████████████████████████░░███████░░░░░░░░░███ 38m
├─ E1 Explore ███ 3m find auth handlers
├─ E2 Explore ██ 2m find session store
├─ G1 general ██ 2m check diagram alignment; waste: 6 lines, inline is faster
└─ G2 general ██░░█████ 7m review-code, local diff
└─ E3 Explore ██ 2m find callers
```
10. **Reply in this format.** Cite a timestamp for every finding. Omit empty sections. If the session ran clean, say so in one line; never invent findings.
```
Session: <id> (<harness>), <start>–<end>, <agent time> agent / <user wait> user wait
Grade: <A–F> — <one-line reason>
Time by phase
| Phase | Time | % | Notes |
Subagents
<chart from rule 9>
Wasteful verification
- <suite> run <N>× (<total>) at <times> — <why wasteful>; saves ~<time>
Tool-call errors
- <N>× <cause> at <times> — <fix>
Improvements
1. <file or skill>: <one-line change> — <evidence>
```From lwlee2608/agent-skills, MIT.
More from lwlee2608/agent-skills
- 0.587score
build-feature
lwlee2608
Use when implementing a phased feature plan file.
SkillAFreshness: A★ 1Claude Code +9 - 0.587score
gh-create-pr
lwlee2608
Use when creating/raising/opening/submitting a GitHub PR.
SkillAFreshness: A★ 1Claude Code +9 - 0.587score
plan-feature
lwlee2608
Use when planning a feature too big to hold in one session.
SkillAFreshness: A★ 1Claude Code +9 - 0.586score
create-skill
lwlee2608
Use when creating a new agent skill.
SkillAFreshness: A★ 1Claude Code +9 - 0.586score
review-code
lwlee2608
Use when asked to review a local diff, a GitHub PR, or a whole codebase.
SkillAFreshness: A★ 1Claude Code +9 - 0.576score
merge-pr
lwlee2608
Merge a GitHub PR, close linked issues, and delete the branch.
SkillAFreshness: A★ 1Claude Code +9 - 0.562score
second-opinion
lwlee2608
Use when the user asks for a second-opinion review from another agent (e.g., "get a second opinion", "review with pi", "review using gpt-6-astra"…
SkillAFreshness: A★ 1Claude Code +9 - 0.562score
whoami
lwlee2608
Use when the user asks for the current session's ID (e.g., "/whoami", "what's your session id", "who are you") so another agent session can find or message…
SkillAFreshness: A★ 1Claude Code +9 - 0.562score
review-loop
lwlee2608
Use only when the user explicitly asks to review and fix in a loop until clean.
SkillAFreshness: A★ 1Claude Code +9 - 0.547score
explain-outcome
lwlee2608
Use when reporting a failed command, test, build, or deploy, an investigation into an error or incident, or the result of a multi-step task.
SkillBFreshness: B★ 1Claude Code +9 - 0.547score
generate-media
lwlee2608
Use when generating or editing an image or video with an AI model — ads, mockups, style variations, image-to-video.
SkillBFreshness: B★ 1Claude Code +9 - 0.547score
linear-issues
lwlee2608
Use when reading, searching, creating, updating, or commenting on Linear issues.
SkillBFreshness: B★ 1Claude Code +9
Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 24, 2026 (6 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/lwlee2608/agent-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
More from lwlee2608
- 0.587score
build-feature
lwlee2608
Use when implementing a phased feature plan file.
SkillAFreshness: A★ 1Claude Code +9 - 0.587score
plan-feature
lwlee2608
Use when planning a feature too big to hold in one session.
SkillAFreshness: A★ 1Claude Code +9 - 0.587score
gh-create-pr
lwlee2608
Use when creating/raising/opening/submitting a GitHub PR.
SkillAFreshness: A★ 1Claude Code +9 - 0.586score
review-code
lwlee2608
Use when asked to review a local diff, a GitHub PR, or a whole codebase.
SkillAFreshness: A★ 1Claude Code +9 - 0.586score
create-skill
lwlee2608
Use when creating a new agent skill.
SkillAFreshness: A★ 1Claude Code +9 - 0.576score
merge-pr
lwlee2608
Merge a GitHub PR, close linked issues, and delete the branch.
SkillAFreshness: A★ 1Claude Code +9
Done for you
Rather not set it up yourself?
Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.
See how we build AI agents →Read the case studies
Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
- launches shipped
- 50+
- launches shipped
- media placements
- 500+
- media placements
- founders on every account
- 2
- founders on every account
Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
Get retro running in your business
Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.
Cite this page
retro skill. Agent Mart. https://agentmart.dev/skills/lwlee2608-retro (last updated 2026-10-24)