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investigate

Trace production failures through code paths, git history, and deployment state.

1|Updated May 15, 2026
Search Tags:#git#debugging#investigation#production#aws#datadog#incident-analysis
One-click install
npx skills add https://github.com/mslshao/claude-harness --skill investigate-mslshao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: investigate
Source: https://github.com/mslshao/claude-harness/tree/main/project-tier/skills/investigate
Command: npx skills add https://github.com/mslshao/claude-harness --skill investigate-mslshao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps teams investigate production errors without jumping prematurely to fixes. It provides a disciplined workflow for tracing failures back through code paths, git history, deployment state, and observability signals so you can explain what changed, why the error surfaced, and how broad the impact is.

Core Features & Use Cases

  • Structured error investigation: Parses stack traces and failure context into concrete investigation questions that guide evidence gathering.
  • Cross-system tracing: Connects file-level code analysis with git regression history, AWS deployment metadata, and Datadog activity to determine whether a suspected regression is actually live and firing.
  • Evidence-ready reporting: Produces a structured investigation document with file:line citations, blast radius assessment, in-flight fix status, and calibrated uncertainty suitable for Jira or Slack.
  • Use cases: Investigating Lambda failures, ECS service regressions, unexpected production behavior, silent regressions after deployment, or any issue where the contributing factors are still unknown.

Quick Start

Ask the investigate skill to analyze a production error or stack trace and produce a cited investigation document explaining the leading hypothesis, contributing factors, blast radius, and whether the regression is currently deployed and firing.

Frequently Asked Questions about investigate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I investigate production errors by tracing git history and deployment state?▼

Investigate production failures by parsing stack traces into investigation questions, tracing code paths backward, verifying git regression history, checking AWS deployment state, and validating Datadog observability signals.

What is the best way to find the cause of a silent regression after a deployment?▼

Trace silent regressions by cross-referencing file-level code analysis with git history and AWS deployment metadata to determine whether the suspected regression is actually live and firing.

How do I analyze Lambda failures and ECS issues without jumping to fixes prematurely?▼

Analyze Lambda and ECS issues using structured question-driven analysis that requires file and line citations, blast radius estimation, and explicit separation of investigation from fix planning.

Can I use Datadog and AWS deployment metadata to estimate the blast radius of an incident?▼

Yes, Datadog signal validation and AWS deployment verification are used to estimate blast radius, assess whether the regression is firing, and produce an evidence-ready investigation document.

Does production incident analysis work for tracing failures across schema or contract boundaries?▼

Yes, production incident analysis traces failures backward through code paths, schema or contract boundaries, git history, and observability evidence to explain what changed and why the error surfaced.

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