Memory Refiner

Memory Refiner

Overview

Use this skill to audit how Codex is configured and instructed, then suggest targeted improvements. This skill is Codex-specific: analyze Codex files and usage patterns, not Claude files.

When To Use

Use this skill when the user asks to:

• refine or optimize Codex memory, instructions, rules, or config
• analyze repeated session patterns or recurring corrections
• reduce context bloat or reorganize guidance for lazy loading
• separate global guidance from project-local overrides

Workflow

1. Collect Evidence

• Start the lifecycle log immediately after invocation:
  • python3 scripts/write_reflection_log.py --cwd "$PWD" start --user-request-summary "<short summary>"
• Prefer passing structured lifecycle payloads to write_reflection_log.py over stdin with --input -.
• If stdin is impractical and a temporary JSON payload file is needed for event or finalize, write it under ~/.codex/cache/memory-refiner/tmp/, not under repo-local .codex/plans/ or other durable artifact directories.
• Use the current conversation as the highest-signal short-term evidence.
• Treat interruption or abort notices in the current conversation (for example Conversation interrupted or turn_aborted) as workflow signals, even if they do not appear in history.jsonl.
• Run python3 scripts/scan_history.py --format markdown to summarize ~/.codex/history.jsonl.
• Run python3 scripts/summarize_reflection_logs.py --cwd "$PWD" --format markdown to summarize recent memory-refiner runs before proposing new guidance.
• Look for repeated preferences, repeated corrections, interruption or abort signals, approval friction, context bloat, stale guidance, and recurring task patterns.

2. Audit Active Memory Surfaces

• Run python3 scripts/list_memory_surfaces.py --cwd "$PWD" --format markdown.
• Record a lifecycle event after evidence collection and memory-surface discovery:
  • python3 scripts/write_reflection_log.py --cwd "$PWD" event --stage evidence --input -
• Read only the files that are relevant to the request.
• Include the current project's local .codex/ when present.
• Include repo-local instruction files such as AGENTS.md when present.

3. Apply Scope Precedence

Use this precedence when evaluating what should win for the current repo:

1. Current project .codex/
2. Repo-local AGENTS.md or similar repo-local instruction files
3. Global ~/.codex

Flag shadowing, duplication, and conflicts across these scopes.

4. Synthesize Recommendations

• Separate findings by scope: global, project-local, and repo-local.
• Keep universal guidance project, language, framework, and technology agnostic unless repeated evidence strongly justifies specificity.
• Treat explicit user statements as higher priority than inferred preferences.
• Do not turn one-off incidents into permanent memory.
• Record another event after recommendations are drafted:
  • python3 scripts/write_reflection_log.py --cwd "$PWD" event --stage recommendations --input -

5. Suggest Before Applying

For each recommendation, provide:

• target file
• scope
• priority
• change type: add, modify, move, delete, or split
• exact proposed change or diff-ready text
• a short rationale tied to evidence

Do not apply changes until the user approves the specific items.

6. Apply Approved Changes

• Apply only the approved subset.
• Re-check for conflicts after editing.
• Re-run surface discovery if the scope layout changed.

7. Finalize And Reflect

• Finalize every memory-refiner run, even if no changes were approved.
• Use python3 scripts/write_reflection_log.py --cwd "$PWD" finalize --input - and provide a structured JSON payload with:
  • user_request_summary
  • history_summary
  • memory_surface_summary
  • recommendations
  • optional notes
• During the run, use event calls for meaningful lifecycle checkpoints such as evidence, surfaces, recommendations, apply, cleanup, error, or interrupted.
• Record recommendation outcomes using statuses such as proposed, approved, applied, rejected, or deferred.
• Run python3 scripts/suggest_log_cleanup.py --cwd "$PWD" --format markdown and include any meaningful stale-log suggestions in the final response when relevant.
• When the stale-log suggestions should be applied immediately, run python3 scripts/suggest_log_cleanup.py --cwd "$PWD" --apply --format markdown.
• Remove any temporary payload files created under ~/.codex/cache/memory-refiner/tmp/ after the corresponding event or finalize command succeeds, and make sure the run finishes without leaving those cache files behind.
• Use prior reflection logs to suppress stale advice, highlight repeated successful recommendations, and call out repeated rejected suggestions only when that history materially improves the recommendation quality.
• This logging is skill-driven, not a separate hidden Codex hook, so the lifecycle logger must be called explicitly as part of the workflow.

In Scope

• ~/.codex/AGENTS.md
• ~/.codex/instructions/**/*.md
• ~/.codex/rules/*.rules
• ~/.codex/config.toml
• ~/.codex/log/memory-refiner/**/*
• ~/.codex/cache/memory-refiner/active/**/*.json
• ~/.codex/skills/*/SKILL.md
• ~/.codex/skills/*/agents/openai.yaml
• current project .codex/**/*.{md,toml,rules,yaml,yml}
• current project AGENTS.md

Out Of Scope By Default

• Claude config or Claude skills
• unrelated repositories' .codex/ directories
• auth, sqlite, logs (except memory-refiner reflection logs), tmp, sessions, caches, and shell history
• raw history dumps when a compact summary is enough

Output Style

• Be compact and evidence-based.
• Separate facts, assumptions, and recommendations.
• Prefer moving specialized guidance out of global or root files into lazy-loaded files when appropriate.