Session Analyzer logo

Session Analyzer

Organization
team-attention
session-analyzer

This skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.

Overview

Publisherteam-attention
Repositoryplugins-for-claude-natives
Skill namesession-analyzer
Stars
827
Forks
106
Bundled files
5
LicenseMIT
Links
  • 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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by team-attention on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/team-attention/plugins-for-claude-natives/tree/main/plugins/session-wrap/skills/session-analyzer
  4. 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.
2

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/team-attention/plugins-for-claude-natives.git /tmp/plugins-for-claude-natives
mkdir -p .claude/skills
cp -r /tmp/plugins-for-claude-natives/plugins/session-wrap/skills/session-analyzer .claude/skills/session-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Session Analyzer 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 Session Analyzer 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 Session Analyzer 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.

Session Analyzer Skill

Post-hoc analysis tool for validating Claude Code session behavior against SKILL.md specifications.

Purpose

Analyze completed sessions to verify:

  1. Expected vs Actual Behavior - Did the skill follow SKILL.md workflow?
  2. Component Invocations - Were SubAgents, Hooks, and Tools called correctly?
  3. Artifacts - Were expected files created/deleted?
  4. Bug Detection - Any unexpected errors or deviations?

Input Requirements

ParameterRequiredDescription
sessionIdYESUUID of the session to analyze
targetSkillYESPath to SKILL.md to validate against
additionalRequirementsNOExtra validation criteria

Phase 1: Locate Session Files

Step 1.1: Find Session Files

Session files are located in ~/.claude/:

bash
# Main session log
~/.claude/projects/-{encoded-cwd}/{sessionId}.jsonl

# Debug log (detailed)
~/.claude/debug/{sessionId}.txt

# Agent transcripts (if subagents were used)
~/.claude/projects/-{encoded-cwd}/agent-{agentId}.jsonl

Use script to locate files:

bash
${baseDir}/scripts/find-session-files.sh {sessionId}

Step 1.2: Verify Files Exist

Check all required files exist before proceeding. If debug log is missing, analysis will be limited.


Phase 2: Parse Target SKILL.md

Step 2.1: Extract Expected Components

Read the target SKILL.md and identify:

From YAML Frontmatter:

  • hooks.PreToolUse - Expected PreToolUse hooks and matchers
  • hooks.PostToolUse - Expected PostToolUse hooks
  • hooks.Stop - Expected Stop hooks
  • hooks.SubagentStop - Expected SubagentStop hooks
  • allowed-tools - Tools the skill is allowed to use

From Markdown Body:

  • SubAgents mentioned (Task(subagent_type="..."))
  • Skills called (Skill("..."))
  • Artifacts created (.dev-flow/drafts/, .dev-flow/plans/, etc.)
  • Workflow steps and conditions

Step 2.2: Build Expected Behavior Checklist

Create checklist from SKILL.md analysis:

markdown
## Expected Behavior

### SubAgents
- [ ] Explore agent called (parallel, run_in_background)
- [ ] gap-analyzer called before plan generation
- [ ] reviewer called after plan creation

### Hooks
- [ ] PreToolUse[Edit|Write] triggers plan-guard.sh
- [ ] Stop hook validates reviewer approval

### Artifacts
- [ ] Draft file created at .dev-flow/drafts/{name}.md
- [ ] Plan file created at .dev-flow/plans/{name}.md
- [ ] Draft file deleted after OKAY

### Workflow
- [ ] Interview Mode before Plan Generation
- [ ] User explicit request triggers plan generation
- [ ] Reviewer REJECT causes revision loop

Phase 3: Analyze Debug Log

The debug log (~/.claude/debug/{sessionId}.txt) contains detailed execution traces.

Step 3.1: Extract SubAgent Calls

Search patterns:

SubagentStart with query: {agent-name}
SubagentStop with query: {agent-id}

Use script:

bash
${baseDir}/scripts/extract-subagent-calls.sh {debug-log-path}

Step 3.2: Extract Hook Events

Search patterns:

Getting matching hook commands for {HookEvent} with query: {tool-name}
Matched {N} unique hooks for query "{query}"
Hooks: Processing prompt hook with prompt: {prompt}
Hooks: Prompt hook condition was met/not met
permissionDecision: allow/deny

Use script:

bash
${baseDir}/scripts/extract-hook-events.sh {debug-log-path}

Step 3.3: Extract Tool Calls

Search patterns:

executePreToolHooks called for tool: {tool-name}
File {path} written atomically

Step 3.4: Extract Hook Results

For prompt-based hooks, find the model response:

Hooks: Model response: {
  "ok": true/false,
  "reason": "..."
}

Phase 4: Verify Artifacts

Step 4.1: Check File Creation

For each expected artifact:

  1. Search debug log for FileHistory: Tracked file modification for {path}
  2. Search for File {path} written atomically
  3. Verify current filesystem state

Step 4.2: Check File Deletion

For files that should be deleted:

  1. Search for rm commands in Bash calls
  2. Verify file no longer exists on filesystem

Phase 5: Compare Expected vs Actual

Step 5.1: Build Comparison Table

markdown
| Component | Expected | Actual | Status |
|-----------|----------|--------|--------|
| Explore agent | 2 parallel calls | 2 calls at 09:39:26 | ✅ |
| gap-analyzer | Called before plan | Called at 09:43:08 | ✅ |
| reviewer | Called after plan | 2 calls (REJECT→OKAY) | ✅ |
| PreToolUse hook | Edit\|Write matcher | Triggered for Write | ✅ |
| Stop hook | Validates approval | Returned ok:true | ✅ |
| Draft file | Created then deleted | Created→Deleted | ✅ |
| Plan file | Created | Exists (10KB) | ✅ |

Step 5.2: Identify Deviations

Flag any mismatches:

  • Missing component calls
  • Wrong order of operations
  • Hook failures
  • Missing artifacts
  • Unexpected errors

Phase 6: Generate Report

Report Template

markdown
# Session Analysis Report

## Session Info
- **Session ID**: {sessionId}
- **Target Skill**: {skillPath}
- **Analysis Date**: {date}

---

## 1. Expected Behavior (from SKILL.md)

[Summary of expected workflow]

---

## 2. Skill/SubAgent/Hook Verification

### SubAgents
| SubAgent | Expected | Actual | Time | Result |
|----------|----------|--------|------|--------|
| ... | ... | ... | ... | ✅/❌ |

### Hooks
| Hook | Matcher | Triggered | Result |
|------|---------|-----------|--------|
| ... | ... | ... | ✅/❌ |

---

## 3. Artifacts Verification

| Artifact | Path | Expected State | Actual State |
|----------|------|----------------|--------------|
| ... | ... | ... | ✅/❌ |

---

## 4. Issues/Bugs

| Severity | Description | Location |
|----------|-------------|----------|
| ... | ... | ... |

---

## 5. Overall Result

**Verdict**: ✅ PASS / ❌ FAIL

**Summary**: [1-2 sentence summary]

Scripts Reference

ScriptPurpose
find-session-files.shLocate all files for a session ID
extract-subagent-calls.shParse subagent invocations from debug log
extract-hook-events.shParse hook events from debug log

Usage Example

User: "Analyze session 3cc71c9f-d27a-4233-9dbc-c4f07ea6ec5b against .claude/skills/specify/SKILL.md"

1. Find session files
2. Parse SKILL.md → Expected: Explore, gap-analyzer, reviewer, hooks
3. Analyze debug log → Extract actual calls
4. Verify artifacts → Check .dev-flow/
5. Compare → Build verification table
6. Generate report → PASS/FAIL with details

Additional Resources

Reference Files

  • references/analysis-patterns.md - Detailed grep patterns for log analysis
  • references/common-issues.md - Known issues and troubleshooting

Scripts

  • scripts/find-session-files.sh - Session file locator
  • scripts/extract-subagent-calls.sh - SubAgent call extractor
  • scripts/extract-hook-events.sh - Hook event extractor

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Session Analyzer AI skill do?

This skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.

Why use Session Analyzer on TypingMind?

Because you install it once and use it with any model. Session Analyzer 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 Session Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/team-attention/plugins-for-claude-natives/tree/main/plugins/session-wrap/skills/session-analyzer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Session Analyzer?

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 Session Analyzer?

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

Is the Session Analyzer AI skill free?

Yes. It is published on GitHub by team-attention 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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