Nw Investigation Techniques

Organization

nWave-ai

nw-investigation-techniques

Evidence collection methods, problem categorization, analysis techniques, and solution design patterns

Overview

Publisher	nWave-ai
Repository	`nWave`
Skill name	`nw-investigation-techniques`
Stars	616
Forks	65
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by nWave-ai on GitHub. Read the source before you install it.

Installation

Install the Nw Investigation Techniques AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-investigation-techniques
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/nWave-ai/nWave.git /tmp/nWave
mkdir -p .claude/skills
cp -r /tmp/nWave/nWave/skills/nw-investigation-techniques .claude/skills/nw-investigation-techniques

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nw Investigation Techniques in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nw Investigation Techniques on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nw Investigation Techniques is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Investigation Techniques

Problem Categorization

Technical Problems

Category	Sub-Category	Common Symptoms
System Failures	App crashes, memory leaks, deadlocks, data corruption	Service unavailability, resource exhaustion, integrity errors
System Failures	Hardware, network, database, security	Connectivity loss, capacity limits, access failures
Performance	Response time: slow queries, latency, algorithmic inefficiency	High p95/p99, user-reported slowness
Performance	Throughput: thread pool exhaustion, connection limits, queue backlog	Reduced capacity, growing queues
Integration	Internal: component comms, data format, version conflicts	Interface errors, serialization failures
Integration	External: third-party availability, API changes, auth failures	Timeouts, contract violations

Operational Problems

Category	Common Symptoms
Deployment: script failures, config drift, migration errors	Failed releases, environment inconsistencies
Monitoring: alerting gaps, backup failures, incident response	Missed incidents, slow recovery
Human factors: communication gaps, knowledge silos, skill gaps	Repeated mistakes, slow onboarding

Evidence Collection

Technical Evidence Sources

Logs: application (timestamp correlation) | system/infrastructure | database | network traces

Metrics: performance/resource utilization | error rates/response time trends | user behavior/transaction patterns | infrastructure health/capacity

Configuration: system/deployment settings | code changes/VCS history (git log, blame) | env vars/dependencies | security/access controls

Evidence Validation

Cross-reference: verify from multiple independent sources
Timestamp validation: confirm event sequence accuracy
Completeness check: identify data gaps/corruption
Correlation vs causation: distinguish co-occurrence from causation

Analysis Techniques

Quantitative

Trend: time series of metrics, error pattern frequency
Distribution: response time percentiles, error rate across components
Pattern recognition: log anomalies, behavior patterns, error clustering

Qualitative

Timeline reconstruction: detailed incident timeline, correlate changes with symptoms
Process analysis: workflow disruptions, communication flow, decision chains
Environmental: recent changes, system load, external factors, related incidents

Solution Design Patterns

Immediate Mitigations (restore service)

Quick fixes | workarounds to minimize impact | emergency procedures | monitoring enhancements

Permanent Fixes (prevent recurrence)

Architecture modifications | code quality/defensive programming | config management/environment consistency | testing/validation improvements

Early Detection (catch faster)

Leading indicators | anomaly detection/predictive alerting | automated quality gates | threshold tuning from learnings

Solution Prioritization Matrix

Priority	Criteria	Action
P0	Active incident, users impacted	Immediate mitigation, hours
P1	Root cause fix for recurring issue	Permanent fix, current sprint
P2	Prevention for potential issues	Next sprint
P3	Systemic improvement	Backlog with evidence

Frequently asked questions

What does the Nw Investigation Techniques AI skill do?

Evidence collection methods, problem categorization, analysis techniques, and solution design patterns

Why use Nw Investigation Techniques on TypingMind?

Because you install it once and use it with any model. Nw Investigation Techniques is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nw Investigation Techniques in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nWave-ai/nWave/tree/main/nWave/skills/nw-investigation-techniques. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Nw Investigation Techniques?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nw Investigation Techniques?

As many as you like. As long as a model supports skills, you can use Nw Investigation Techniques with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nw Investigation Techniques AI skill free?

Yes. It is published on GitHub by nWave-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Investigation Techniques

Problem Categorization

Technical Problems

Operational Problems

Evidence Collection

Technical Evidence Sources

Evidence Validation

Analysis Techniques

Quantitative

Qualitative

Solution Design Patterns

Immediate Mitigations (restore service)

Permanent Fixes (prevent recurrence)

Early Detection (catch faster)

Solution Prioritization Matrix

Frequently asked questions

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