# Analyze Conversation

> Analyze a completed conversation retrospectively for anti-patterns, tooling gaps, and durable learnings, then generate a markdown report. Use for postmortems of finished sessions or when the user explicitly says /analyze-conversation. Do not use for live, in-progress checks; use check-antipatterns instead.

- Skill: `rahulrajaram/analyze-conversation` (Agent Skill, multi-file: 6 files)
- Install (CLI): `npx skillmds@latest add rahulrajaram/analyze-conversation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rahulrajaram/analyze-conversation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: rahulrajaram (https://skillmd.com/u/rahulrajaram)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rahulrajaram/analyze-conversation

---


# Conversation Analyzer

Performs comprehensive post-mortem analysis of conversations to extract:
- Systemic anti-patterns (retry-without-diagnosis, credential assumptions, scope creep, etc.)
- Tooling opportunities (repeated commands that should be automated)
- Universal rules violated (using the shared infrastructure rule taxonomy)
- Recommendations for improvement

## Autonomy Routing

When invoked, generate the retrospective artifact directly. Do not turn the
analysis into a choice between `/goal` and direct execution. If the report
identifies clear low-risk wording or tooling fixes and the user asked to fix the
problem, continue into those fixes after reporting the findings; otherwise stop
after producing the retrospective.

## Usage

`/analyze-conversation [conversation-id]`

The underlying report generator can also be run directly:

```bash
python ~/.codex/skills/analyze-conversation/generate_report.py --current
python ~/.codex/skills/analyze-conversation/generate_report.py --id <conversation-id>
python ~/.codex/skills/analyze-conversation/generate_report.py <conversation-jsonl>
```

Other runtimes may install the same scripts beside their own manifest; invoke
the script from the active skill directory.

## Arguments

- `conversation-id` (optional): ID of conversation to analyze. If omitted, analyzes current conversation.
- `--current`: Analyze the most recently updated Codex JSONL session under `~/.codex/sessions`.

## Output

Generates a retrospective report beneath the active runtime:

- Codex transcripts: `~/.codex/retrospectives/[conversation-id]_retrospective.md`
- Claude transcripts: `~/.claude/retrospectives/[conversation-id]_retrospective.md`

The selected directory is created on first successful run.

## Shared taxonomy

Treat `check-antipatterns/rules.json` as the canonical live rule taxonomy when
both skills are installed. This retrospective may add longitudinal and tooling
findings, but it must not redefine the shared rule meanings.

## What It Analyzes

### Anti-Patterns Detected

1. **Credential Anti-Patterns**
   - Hardcoded passwords/secrets
   - Credential assumptions that require contextual review
   - Assumed credentials without verification

2. **Retry Patterns**
   - Commands retried without checking logs/events between attempts
   - Blind retries without diagnosis

3. **Scope Drift**
   - Task expansions beyond original request
   - Creating new services/components without asking user

4. **Tool Blindness**
   - Existing tools not discovered or used
   - Manual commands when automation exists

5. **Verification Gaps**
   - Unverified external values (IPs, URLs, endpoints)
   - Integration tests run without preflight checks

6. **Command Repetition**
   - Same command run 3+ times (tool opportunity)
   - Manual command sequences that should be scripted

### Report Sections

The generated report includes:

- **Executive Summary**: Top anti-patterns, tool needs, rule violations
- **Detailed Anti-Pattern Analysis**: Each instance with context and fix
- **Tool Opportunities**: Commands that should be automated
- **Rule-related candidates**: Stable shared rule IDs, evidence categories, and review counts
- **Recommendations**: Priority-ranked action items
- **Success Metrics**: Comparison with target behavior

## Example Output

```markdown
# Conversation Retrospective: 5e6380e9-fb47-493b-9944-b029d43dae40

## Summary
- Total turns: 532
- Duration: ~8 hours
- Commands executed: 162
- Anti-patterns found: 13

## Anti-Patterns Found

1. **Retry-Without-Diagnosis**: 10 instances
   - Example: `git status` retried 3 times without checking logs
   - Fix: Run `git status --verbose` or check git daemon logs

2. **Credential Assumption**: 1 instance
   - Example: Emitted a credential-like assignment in assistant text
   - Fix: Review source, authorization, and exposure without printing or decoding secrets

3. **Tool Blindness**: 5 tools not discovered
   - Repeated command sequences that may justify a project-specific helper
   - Impact: potential automation opportunity; no avoided-command estimate is established

## Tool Opportunities

- **Repeated 10x**: git status → Review whether project-specific automation is warranted
- **Repeated 5x**: kubectl get pods → Review whether project-specific automation is warranted
- **Repeated 5x**: pytest → Review whether project-specific automation is warranted

## Rule-related candidates

- **DIAG-002** (diagnose before retry): 10 candidates
- **DIAG-001** (credential assumption): 1 candidate
- **DIAG-005** (tool discovery): 5 candidates

## Recommendations

1. **HIGH**: Review repeated test failures and decide whether a project preflight is warranted
2. **HIGH**: Review credential handling against the project’s authorized mechanism
3. **HIGH**: Review retry evidence and add a diagnostic helper only if the project needs one
4. **MEDIUM**: Consider documenting available tools for discoverability
5. **MEDIUM**: Consider a verification reminder where the evidence supports it
```

## Implementation

This skill uses scripts beside this manifest (normally
`~/.codex/skills/analyze-conversation/` for Codex or
`~/.claude/skills/analyze-conversation/` for Claude Code):

- **analyzer.py**: Main analysis engine that parses JSONL conversations
- **patterns.py**: Pattern detectors for each anti-pattern type
- **generate_report.py**: CLI, Codex transcript normalization, and report writer

The analyzer reuses the analysis scripts created during retrospective analysis and enhances them with:
- Report generation in structured markdown
- Severity ranking (HIGH/MEDIUM/LOW)
- Actionable recommendations
- Success metric tracking
- Codex JSONL normalization for current `item_completed` command,
  file-change, MCP, collaboration, user-message, and agent-message events under
  `~/.codex/sessions`
- Observed transcript-span, command-runtime, failure, and tool-kind metrics
- Autonomy-break detection for user re-prompts and assistant workflow-routing questions

If `--current` cannot identify a readable transcript, list the newest candidate
JSONL files under `~/.codex/sessions` without printing their contents and ask the
user to choose. Do not silently analyze a different session. On malformed or
unreadable JSONL, report the path and parse/access error; do not emit a partial
report as if it were complete. Empty or unsupported input is an explicit
coverage failure and must not produce a successful no-findings report. A
conversation ID selects one exact file; never silently analyze a different
session because it is newer or merely has a similar filename.

## Benefits

- **Learn from past mistakes**: Identify patterns that led to wasted effort
- **Improve processes**: Generate actionable recommendations
- **Track progress**: Compare metrics across conversations
- **Build better tools**: Discover automation opportunities
- **Refine system prompts**: Identify rules that need enforcement

## Related Skills

- `/check-antipatterns`: Real-time anti-pattern detection during active work
- Both skills work together in a learning loop:
  1. `/check-antipatterns` prevents issues during work
  2. `/analyze-conversation` identifies what wasn't caught
  3. Learnings improve both skills over time