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Post Mortem

Listed
boshu2·vlatest·other·Apache-2.0

Analyzes past project outcomes to identify lessons learned & improve future strategies – crucial for informed decision-making and efficiency.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it postmortem, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add boshu2/agentops --skill postmortem -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

boshu2

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

Nobody has reviewed this code. TrustedSkills indexes and links skills; it does not audit, run or scan them. A skill runs with whatever access you give your agent — read the source before you install it.

Automated safety checks

scanned

We could not read this skill's source, so none of the checks below were run.

Not scanned. none of the 62 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as boshu2/agentops.
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The post-mortem skill enables AI agents to analyze past interactions and outcomes, identifying what went well and what could be improved. It provides structured insights into performance, helping users refine strategies or workflows based on historical data.

When to use it

  • After a project or task has been completed to evaluate its success and identify areas for improvement.
  • To review failed attempts or errors in an AI agent's execution and understand the root causes.
  • When optimizing workflows by learning from previous iterations of similar tasks.

Key capabilities

  • Analyzes historical data from past interactions
  • Identifies patterns, successes, and failures
  • Provides actionable insights for improvement

Example prompts

  • "Analyze my last project to find what worked and what didn’t."
  • "Review the failed task and explain why it didn't succeed."
  • "What can I learn from this interaction to improve future outcomes?"

Tips & gotchas

  • Ensure historical data is available for accurate analysis.
  • Use this skill after completing a task, not during active execution.