Agent skill

Retrospective

by glebis in glebis/claude-skills

Interactive post-session retrospective that captures learnings, updates skills, and saves memories.

MITAuto-check passedProduct & Project Management

Install Retrospective

skills CLI
$ npx skills add glebis/claude-skills --skill retrospective -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install glebis/claude-skills retrospective --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/glebis/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/retrospective .claude/skills/retrospective && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
retrospective
GitHub stars
390
Token cost
~2.3k tokens
SKILL.md length
1,058 words
Files
13
Skills in repo
92
Repo updated
First seen
Licence
MIT

At a glance

Interactive post-session retrospective that captures learnings, updates skills, and saves memories.

  • Works in 4 steps: Discover sessions → Gate Check (silent) → Execute (silent, no re-confirmation) → …
  • The user says /retrospective
  • SKILL.md covers Modes, Multi-Session Discovery, Single-Session Process and JSONL Transcript Format, plus 6 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Retrospective is an agent skill from glebis/claude-skills. Interactive post-session retrospective that captures learnings, updates skills, and saves memories. Use when the user says "/retrospective", "let's do a retro", "what did we learn", "session review", "retro", or "wrap up". Also use at the end of long productive sessions when significant patterns or corrections emerged. Supports multi-session mode — by default processes all of today's sessions across projects.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files (for example `.claude-plugin/plugin.json`, `retro_engine.py` and `scenarios/01_productive_long.yaml`).

It sits in Product & Project Management, covering Retrospectives. The repository describes itself as: Collection of Claude Code skills for enhanced AI workflows. The licence is MIT.

When your agent uses it

  • The user says /retrospective
  • Lets do a retro
  • What did we learn

Example prompts

  • “/retrospective”
  • “s do a retro”
  • “what did we learn”
  • “/retrospective”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Discover sessions
  2. Gate Check (silent)
  3. Execute (silent, no re-confirmation)
  4. Summary (brief)

What it can do on your machine

Read from SKILL.md and the folder at commit 3b88261. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Retrospective loads about 2.3k tokens when it runs. Until then it costs about 107 tokens; SKILL.md has 1,058 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~107
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from glebis/claude-skills at commit 3b88261, republished under its MIT licence (© glebis). 1,058 words, ~2,252 tokens.

Download SKILL.mdSave it as .claude/skills/retrospective/SKILL.md (or your agent's skills folder). This skill also uses 12 other files; get the full folder from GitHub.
name
retrospective
description
Interactive post-session retrospective that captures learnings, updates skills, and saves memories. Use when the user says "/retrospective", "let's do a retro", "what did we learn", "session review", "retro", or "wrap up". Also use at the end of long productive sessions when significant patterns or corrections emerged. Supports multi-session mode — by default processes all of today's sessions across projects.

Retrospective

Interactive post-session retro. Scans sessions, asks focused questions, proposes concrete actions the user approves in one step.

Modes

Single-session mode (default when inside a substantial conversation)

Scans the current conversation only. This is the original behavior.

Multi-session mode (default when invoked with no args, or with "today", or with a date)

Scans all sessions from a given day (default: today) across all projects. Extracts user corrections, skill failures, and patterns from JSONL transcripts.

Trigger: /retrospective at the start of a fresh session, or /retrospective today, or /retrospective 2026-05-24.

Multi-Session Discovery

Step 0 — Discover sessions
bash
# Find today's sessions (default)
find ~/.claude/projects -maxdepth 2 -name "*.jsonl" -not -path "*/subagents/*" -mtime 0

# Or for a specific date, filter by file modification date
find ~/.claude/projects -maxdepth 2 -name "*.jsonl" -not -path "*/subagents/*" -newermt "YYYY-MM-DD" ! -newermt "YYYY-MM-DD + 1 day"

For each JSONL file found, extract a summary:

  1. Parse the project name from the path (the directory name after projects/, decoded from the path-encoding)
  2. Extract user and assistant messages (type user and assistant)
  3. For user messages: message.content (may be string or array of {type: "text", text: "..."})
  4. For assistant messages: collect text blocks from message.content array where type == "text"
  5. Build a condensed transcript: first 10 user messages + last 5 user messages (to capture corrections at the end)
  6. Skip sessions with fewer than 3 user messages (too short to have learnings)
Step 0b — Present session list

Show the user what was found:

Found N sessions today:
- project-name-1 (session-id[:8]) — "first user message preview..."
- project-name-2 (session-id[:8]) — "first user message preview..."

Then continue to Step 1a with the combined findings from all sessions. Each candidate action should note which session it came from (project name + short ID).

Single-Session Process

Step 0 — Gate Check (silent)

Scan the conversation and estimate session depth. Look for tool calls (Read, Edit, Write, Bash, Skill invocations), errors encountered, and back-and-forth exchanges. Don't try to count exactly — judge by feel:

  • Short session (a quick question and answer, ~1-2 tasks) → Fast mode (Step 1b)
  • Substantial session (multiple tasks, skill usage, errors, corrections) → Full mode (Step 1a)
Step 1a — Full Mode

Silently scan the conversation and collect:

  1. Skills invoked — which succeeded, which failed, workarounds applied
  2. User corrections — explicit "no, do it this way" moments (highest signal)
  3. Repeated patterns — same error hit multiple times, same workaround applied
  4. Cross-skill workflows — 3+ skills chained in sequence

Then read existing state:

  • Find the current project's memory directory: glob ~/.claude/projects/*/memory/MEMORY.md and read it plus relevant memory files
  • Read skill files for any skills that were invoked (~/.claude/skills/{name}/skill.md)
  • Check if Linear CLI exists: test -f ~/.claude/skills/linear/scripts/linear && echo "configured" || echo "not configured"

Generate up to 5 candidate actions, ranked by signal strength:

  1. User corrections (highest priority)
  2. Failed/workarounded skills
  3. Repeated patterns
  4. Error patterns
  5. Workflow patterns (lowest)

Dedup rules:

  • If a candidate's content overlaps with an existing memory file → drop it
  • If a skill update candidate overlaps with existing skill file content → drop it
  • If Linear is not configured → omit any Linear task candidates

Present everything in a single AskUserQuestion call (up to 4 questions):

#QuestionType
1"Quick session check?"Single select: Productive / Mixed / Rough / Skip retro
2"What felt slow or broken?"Free text via Other (optional)
3"Anything to carry forward as a rule?"Free text via Other (optional)
4"Which of these should I save?"Multi-select: generated candidates with descriptions. Always include a "Nothing / skip all" option.

If Q1 = "Skip retro" → exit immediately.

If Q1 = "Rough" and Q2/Q3 are empty → exit with "Nothing to save — session closed." Don't add another question after the user already signaled they're done.

Step 1b — Fast Mode

Single AskUserQuestion call with one question:

  • "Anything worth remembering from this session?" with options:
    • "Nothing, we're done" (default)
    • Other (free text)

If "Nothing" → exit. If free text → save as memory, exit.

Step 2 — Execute (silent, no re-confirmation)

For each approved item from Q4 (plus any insights from Q2/Q3 free text):

  1. Read the target file before writing
  2. Check for conflicts/duplicates against current content
  3. Write the change if clean
  4. Skip with warning if conflict detected

Action types and their targets:

TypeTargetTool
Skill update~/.claude/skills/{name}/skill.mdEdit
Memory (feedback)Current project's memory/feedback_*.md + MEMORY.mdWrite
Memory (project)Current project's memory/project_*.md + MEMORY.mdWrite
CLAUDE.md rule~/.claude/CLAUDE.md or project CLAUDE.mdEdit
Linear task~/.claude/skills/linear/scripts/linear issue create --title "..." --description "..."Bash
Show full SKILL.md (392 more words)Show less
Step 3 — Summary (brief)

One-line per action taken:

Updated telegram skill — added chat type mismatch note
Saved memory — Qwen /api/chat not /api/generate
Skipped: pdf-generation update (already documented)

Done. No trailing commentary.

JSONL Transcript Format

Session transcripts are stored as JSONL files at ~/.claude/projects/{project-path}/{session-id}.jsonl.

Each line is a JSON object with a type field. Relevant types:

  • user — user message. Content at message.content (string or array of {type: "text", text: "..."})
  • assistant — Claude's response. Content at message.content (array of content blocks; extract where type == "text")
  • ai-title — auto-generated session title

To extract a readable transcript from a JSONL file, use a Python one-liner or read the file and filter for user/assistant types.

Project name decoding: The directory name uses the absolute path with slashes replaced by dashes, e.g., -Users-glebkalinin-ai-projects-foo → ~/ai_projects/foo.

Mode Selection Logic

When /retrospective is invoked:

  1. If the current conversation has 10+ user messages → single-session mode (retro this conversation)
  2. If the current conversation is short (just the /retrospective invocation) → multi-session mode (scan today's sessions)
  3. If args contain a date (e.g., "today", "yesterday", "2026-05-24") → multi-session mode for that date
  4. If args contain "all" → multi-session mode for today

Candidate Description Format

Each candidate in Q4 must have a description showing the exact proposed content, not just a title. The user judges candidates by reading descriptions, not by opening files.

Good: "Add to telegram skill: get_chat_type() misclassifies private chats as channels — use Telethon client.send_message() directly for DMs"

Bad: "Update telegram skill with DM fix"

What This Skill Does NOT Do

This skill only captures session learnings. It does not review code quality, analyze PRs, create documentation, or run tests. For those, use the appropriate dedicated skills.

Rules

  • Never write learnings into this skill file itself — distribute to relevant skills or memory
  • Cap candidates at 5 even if more findings exist
  • User corrections always rank above tool failures
  • The multi-select in Step 1a IS the approval — do not ask again per action
  • If the session used no skills, only offer memory and CLAUDE.md candidates
  • Keep the entire interaction to 2 moments: one question call, then silent execution

Tools

  • AskUserQuestion: Interactive questions (1-4 per call, single/multi select)
  • Read: Check existing memory and skill files before proposing changes
  • Edit: Update existing skill files and CLAUDE.md
  • Write: Create new memory files
  • Bash: Linear task creation, skill directory listing
  • Glob: Find skill and memory files

Testing

Engine logic is tested in retro_engine.py with 9 scenario fixtures and 30 pytest tests. Run: cd ~/.claude/skills/retrospective && python3 -m pytest test_retro_engine.py -v

© glebis, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 12 other files in retrospective of glebis/claude-skills.

  • SKILL.md
  • .claude-plugin/plugin.json
  • retro_engine.py
  • scenarios/01_productive_long.yaml
  • scenarios/02_short_session.yaml
  • scenarios/03_rough_session.yaml
  • scenarios/04_no_skills.yaml
  • scenarios/05_many_learnings.yaml
  • scenarios/06_duplicate_memory.yaml
  • scenarios/07_skill_conflict.yaml
  • scenarios/08_linear_not_configured.yaml
  • scenarios/09_cross_skill_workflow.yaml
  • test_retro_engine.py

Open the folder on GitHubat commit 3b88261

Compare with similar skills

Retrospective next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Retrospective compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Retrospective this skillglebis/claude-skills390—~2.3kAutomated safety check: PassMIT
Weekly Engineering Retrogarrytan/gstack136k—~2.4kAutomated safety check: PassMIT
Dough Execute Planterryyin/lizard2.6k—~4.3kAutomated safety check: PassCustom licence
Oral Paper SkillAdkid-Zephyr/oral-paper-skill350—~1.9kAutomated safety check: PassNone
Deck Retroasheshgoplani/agent-deck1k—~1.8kAutomated safety check: PassMIT
Dough Execution Retrospectiveterryyin/lizard2.6k—~4kAutomated safety check: PassCustom licence

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Questions about Retrospective

What does Retrospective do?

Interactive post-session retrospective that captures learnings, updates skills, and saves memories. Retrospective is an agent skill from glebis/claude-skills. Interactive post-session retrospective that captures learnings, updates skills, and saves memories.

When should I use Retrospective?

Retrospective fits situations like: the user says /retrospective; lets do a retro; what did we learn.

How do I install Retrospective in Claude Code?

Run `npx skills add glebis/claude-skills --skill retrospective -a claude-code`. Or copy the skill folder (retrospective in glebis/claude-skills) into .claude/skills/retrospective in your project. Claude Code loads it when a task matches its description.

How do I install Retrospective in Codex?

Run `npx skills add glebis/claude-skills --skill retrospective -a codex`. Or copy the skill folder (retrospective in glebis/claude-skills) into .agents/skills/retrospective in your project. Codex loads it when a task matches its description.

Can I use Retrospective in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add glebis/claude-skills --skill retrospective -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/retrospective, .gemini/skills/retrospective, .github/skills/retrospective and .opencode/skills/retrospective in your project.

What does Retrospective need to run?

Going by SKILL.md and its folder, Retrospective needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Retrospective access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Retrospective safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Retrospective use?

Retrospective is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Retrospective use?

About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Retrospective?

Skills that share tags, products or a category with Retrospective: Weekly Engineering Retro (garrytan/gstack, 136k stars), Dough Execute Plan (terryyin/lizard, 2.6k stars), Oral Paper Skill (Adkid-Zephyr/oral-paper-skill, 350 stars) and Deck Retro (asheshgoplani/agent-deck, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Retrospective?

glebis (a GitHub user) maintains it in glebis/claude-skills, which has 390 GitHub stars. The repository holds 92 skills in this directory. The repository was last updated on October 8, 2026.

Source: glebis/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.