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# Session History Mining for Claude/Codex
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# Session History Mining for Claude/Codex/Cursor
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Use this when you want an AI agent to study recent Claude/Codex work before proposing a plan.
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Use this when you want an AI agent to study recent Claude/Codex/Cursor work before proposing a plan.
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This is optimized for:
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- cross-project history review
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f sessions
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```
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`f sessions` scans Claude + Codex sessions across projects, lets you pick one, and copies context to clipboard.
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`f sessions` scans Claude + Codex + Cursor sessions across projects, lets you pick one, and copies context to clipboard.
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## Core Commands
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# Only Codex sessions
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f sessions --provider codex --list
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# Only Cursor sessions
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f sessions --provider cursor --list
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# Copy selected session context (interactive picker via fzf)
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f sessions --provider all
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```bash
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f ai claude list
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f ai codex list
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f ai cursor list
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# Copy the last 6 exchanges from a selected Claude session for this repo
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f ai claude context - /absolute/path/to/repo 6
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f ai cursor context - /absolute/path/to/repo 6
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```
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Use this after `f sessions` when you want to zoom in on one thread.
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2. Pull 2 to 4 high-signal contexts:
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`f sessions --provider claude --count 6`
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`f sessions --provider codex --count 6`
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`f sessions --provider cursor --count 6`
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3. For stale/long sessions, prefer condensed transfer:
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`f sessions --provider all --handoff`
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4. Paste each copied output into labeled blocks in your prompt.
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Goal:
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- study goose and propose a concrete execution plan for token usage
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- use ideas from recent Claude/Codex histories
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- use ideas from recent Claude/Codex/Cursor histories
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- rank ideas by expected impact and execution cost
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Constraints:
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Session context 3 (optional handoff):
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<paste from f sessions --provider all --handoff>
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Session context 4 (optional Cursor):
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<paste from f sessions --provider cursor --count 6>
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Deliver:
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1. top opportunities (ranked)
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2. 48-hour execution plan
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- Prefer `--count` over `--full` unless you are reconstructing full intent.
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- Prefer `--handoff` for large stale sessions before pasting into expensive models.
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- Cursor transcripts use file-modified time rather than per-message timestamps, so repeated copies may include the whole latest transcript after a new edit.
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- Merge duplicate context manually before sending to avoid repeated tokens.
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- Request ranked outputs with hard deliverables (plan, commands, owners, fallback).
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