agent-ops-retrospective

Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.

16 stars

Best use case

agent-ops-retrospective is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.

Teams using agent-ops-retrospective should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/agent-ops-retrospective/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/ai-agents/agent-ops-retrospective/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/agent-ops-retrospective/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How agent-ops-retrospective Compares

Feature / Agentagent-ops-retrospectiveStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Scan the current chat session for durable learnings (clarifications, corrections, decisions, pitfalls) and update .agent/memory.md. Use after critical review and before concluding work.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Retrospective / Learning workflow (mandatory)

## Goal
Ensure durable, reusable insights from this chat session are captured in `.agent/memory.md` so future sessions can resume without re-discovering them.

## Inputs
- The current chat session transcript (user + assistant messages in this session)
- `.agent/constitution.md`, `.agent/memory.md`, `.agent/focus.md`, `.agent/issues/`

## Extraction rules (strict)
Only capture *durable* items:
- confirmed workflow rules
- stable project conventions (not one-off)
- confirmed commands (but commands/boundaries belong in constitution, not memory)
- user preferences that affect future work on this repo
- corrections to previous misunderstandings
- pitfalls/gotchas discovered during implementation

Do NOT capture:
- transient task state (belongs in focus)
- speculative ideas not adopted
- secrets/tokens
- personal/sensitive data

## Placement rules (strict)
- Project-specific commands/boundaries/constraints → `.agent/constitution.md`
- Durable workflow learnings and recurring conventions → `.agent/memory.md`
- Current session status → `.agent/focus.md`
- Follow-ups and approvals needed → `.agent/issues/`

## Procedure
1) Read: constitution/memory/focus/tasks.
2) Scan the chat session for:
   - explicit corrections ("No, do X instead of Y")
   - newly confirmed commands or tools
   - newly confirmed constraints ("never refactor", "only write docs in …")
   - repeated misunderstandings (add a "pitfall to avoid")
   - preferences expressed by the user
3) Update `.agent/memory.md`:
   - append a dated subsection: `## Retrospective YYYY-MM-DD`
   - add short, atomic bullets phrased as "Do/Don't/Prefer"
   - avoid duplication with constitution (link to constitution section if needed)
4) Update focus if the retrospective reveals unresolved items.
5) Invoke `agent-ops-tasks` discovery if actionable items found.
6) Do not declare completion unless retrospective has been run.

## Issue Discovery During Retrospective

**After scanning session, invoke `agent-ops-tasks` discovery for actionable items:**

1) **Collect actionable learnings:**
   - "We should add tests for X" → `TEST` issue
   - "This pattern is confusing, needs docs" → `DOCS` issue
   - "Found a bug but didn't fix it" → `BUG` issue
   - "This could be optimized later" → `PERF` issue
   - "Technical debt noticed" → `CHORE` or `REFAC` issue
   - "Security concern noted" → `SEC` issue

2) **Present to user:**
   ```
   📋 Retrospective identified {N} actionable items:
   
   Medium:
   - [DOCS] Document the retry mechanism in PaymentService
   - [TEST] Add integration tests for new OAuth flow
   
   Low:
   - [CHORE] Clean up commented-out code in UserController
   - [PERF] Consider caching user preferences (noted during implementation)
   
   Create issues for these? [A]ll / [S]elect / [N]one
   ```

3) **After creating issues:**
   - Mark in memory.md: "Created {ISSUE-IDs} for follow-up"
   - These become part of the project backlog

Related Skills

Incident Retrospective

16
from diegosouzapw/awesome-omni-skill

A postmortem (also called incident review or retrospective) is a structured process for analyzing incidents to understand what happened, why it happened, and how to prevent similar incidents in future

bgo

10
from diegosouzapw/awesome-omni-skill

Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.

Coding & Development

moai-lang-r

16
from diegosouzapw/awesome-omni-skill

R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis patterns.

moai-lang-python

16
from diegosouzapw/awesome-omni-skill

Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or writing tests.

moai-icons-vector

16
from diegosouzapw/awesome-omni-skill

Vector icon libraries ecosystem guide covering 10+ major libraries with 200K+ icons, including React Icons (35K+), Lucide (1000+), Tabler Icons (5900+), Iconify (200K+), Heroicons, Phosphor, and Radix Icons with implementation patterns, decision trees, and best practices.

moai-foundation-trust

16
from diegosouzapw/awesome-omni-skill

Complete TRUST 4 principles guide covering Test First, Readable, Unified, Secured. Validation methods, enterprise quality gates, metrics, and November 2025 standards. Enterprise v4.0 with 50+ software quality standards references.

moai-foundation-memory

16
from diegosouzapw/awesome-omni-skill

Persistent memory across sessions using MCP Memory Server for user preferences, project context, and learned patterns

moai-foundation-core

16
from diegosouzapw/awesome-omni-skill

MoAI-ADK's foundational principles - TRUST 5, SPEC-First TDD, delegation patterns, token optimization, progressive disclosure, modular architecture, agent catalog, command reference, and execution rules for building AI-powered development workflows

moai-cc-claude-md

16
from diegosouzapw/awesome-omni-skill

Authoring CLAUDE.md Project Instructions. Design project-specific AI guidance, document workflows, define architecture patterns. Use when creating CLAUDE.md files for projects, documenting team standards, or establishing AI collaboration guidelines.

moai-alfred-language-detection

16
from diegosouzapw/awesome-omni-skill

Auto-detects project language and framework from package.json, pyproject.toml, etc.

mnemonic

16
from diegosouzapw/awesome-omni-skill

Unified memory system - aggregates communications and AI sessions across all channels into searchable, analyzable memory

mlops

16
from diegosouzapw/awesome-omni-skill

MLflow, model versioning, experiment tracking, model registry, and production ML systems