# Mining Session Patterns

> Session Pattern Mining

- Skill: `qte77/mining-session-patterns` (Agent Skill)
- Install (CLI): `npx skillmds@latest add qte77/mining-session-patterns`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qte77/mining-session-patterns/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: qte77 (https://skillmd.com/u/qte77)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/qte77/mining-session-patterns

---


# Session Pattern Mining

**Target**: $ARGUMENTS

Mines Claude Code session transcripts for recurring patterns that feed
compound learning. Converts raw session data into actionable improvements.

## Arguments

| Position | Name | Required | Default | Description |
|----------|------|----------|---------|-------------|
| 1 | `time-range` | no | `7d` | Period to scan. E.g. `7d`, `30d`, `this-week`. |
| 2 | `output-path` | no | `docs/patterns/session-patterns.md` | Where to write findings. |

**Examples:**

```
/mining-session-patterns                    # Last 7 days, default output
/mining-session-patterns 30d                # Last 30 days
/mining-session-patterns 7d ./patterns.md   # Custom output path
```

## Data Source

```
~/.claude/projects/*/*.jsonl    # Session transcripts
```

**Critical**: Never bulk-read full `.jsonl` files. Use sampling strategy below
to respect context budget.

## Workflow

1. **Discover session files** — Glob `~/.claude/projects/*/*.jsonl`. Filter by
   mtime within `time-range`. Select up to **10 files**, preferring recent.

2. **Sample each file** — Read **first 20 lines** + **last 20 lines** per file.
   This captures session setup (tools, config) and final outcomes (errors,
   completions). Skip files smaller than 5 lines.

3. **Extract patterns** from sampled lines:

   - **Error-fix sequences**: Tool call with error response followed by a
     successful retry or different approach. Look for `type: "tool_error"` or
     error messages in tool results, then the next tool call on the same target.
   - **Tool failure rates**: Count tool calls and failures per tool type
     (Bash, Edit, Read, Grep, Glob, Write). A failure is any tool result
     containing error indicators.
   - **Cost signals**: Estimate token usage per session from message counts
     and approximate message sizes. Map to task complexity (small/medium/large)
     based on message count thresholds: <20 small, 20-80 medium, >80 large.

4. **Format findings** — Structure as tables per Output Format below.
   Every row must suggest a concrete improvement or be omitted.

5. **Write output** — Write to `output-path`. Create parent directories
   if needed.

## Output Format

```markdown
# Session Patterns — <start-date> to <end-date>

## Error-Fix Sequences
| Error Pattern | Recovery Strategy | Frequency | Candidate Learning |
|---------------|-------------------|-----------|-------------------|
| <tool>: <error summary> | <what worked> | N occurrences | <rule or skill suggestion> |

## Tool Failure Rates
| Tool | Calls | Failures | Rate | Common Cause |
|------|-------|----------|------|--------------|
| Bash | N | N | N% | <top failure reason> |

## Cost Signals
| Session | Messages | Est. Tokens | Task Complexity |
|---------|----------|-------------|-----------------|
| <uuid-short> | N | ~Nk | small/medium/large |

## Recommended Actions
- <Concrete improvement derived from patterns above>
```

## Quality Check

- Every table row is **actionable** — suggests a specific improvement
- Findings trace to specific session files (cite UUID prefix)
- Output stays under ~100 lines — summarize, don't dump
- Correct > Complete > Minimal (ACE-FCA)
- If no meaningful patterns found, say so explicitly rather than padding

## Common Pitfalls

- **Reading full transcripts**: Sample only. First 20 + last 20 lines.
- **Max 10 files**: Don't exceed. Prefer recent files over completeness.
- **False patterns**: 1-2 occurrences are anecdotes, not patterns. Minimum 3
  occurrences before reporting as a pattern.
- **Data dumps**: Interpret the data. Raw counts without analysis are noise.