Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand. Use when recalling what a past assistant knew, searching old conversations, rendering specific chats, seeding reference memory from export sidecars, or mining historical context without doing a full import.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand. Use when recalling what a past assistant knew, searching old conversations, rendering specific chats, seeding reference memory from export sidecars, or mining historical context without doing a full import.
license
MIT
Navigating Chat History Without Digesting Everything
Use this skill when the goal is referenceable history, not immediate full ingestion.
Good fits
search my exported ChatGPT history for a topic
figure out what the old assistant knew about me
render the conversation where we discussed X
keep this export around as external memory and only mine it when needed
seed MemFS from memories.json or projects.json
Default posture
Treat the export as an archive you can navigate later.
read the MemFS archive index first if it exists: reference/chatgpt/index.md
inspect the export with scripts/inspect-export.py
search or list before rendering broad ranges
preserve findings to reference/chatgpt/ first
promote to system/human.md only when the fact is durable, current, and worth carrying every turn
Do not re-digest the entire archive unless the user explicitly wants that.
Archive layout in MemFS
Keep the external-memory archive under reference/chatgpt/.
Recommended files:
reference/chatgpt/index.md — source exports, schema notes, known paths, retrieval strategy
reference/chatgpt/export-YYYY-MM-DD.md — inventory and sidecar summary for one export
reference/chatgpt/chatgpt-memory-summary-YYYY-MM-DD.md — content from memories.json
reference/chatgpt/projects-YYYY-MM-DD.md — projects sidecar summary when useful
Before touching the raw export, check whether the archive already has:
an export summary
a prior project summary
curated transcripts
a note on the same topic
If yes, use that first.
2. Inspect before mining
Run inspect-export.py to answer:
what export shape is this?
how many conversations are there?
does memories.json already contain a synthesized memory block?
does projects.json hold useful background?
For large archives, this often answers the question before raw conversation mining is needed.
3. Narrow, then render
Prefer this sequence:
list-conversations.py for browse
search-conversations.py for content lookup
render-conversation.py for deep read
render-range.py only when several adjacent conversations matter
Do not render dozens of chats just because you can.
4. Write findings to progressive memory first
When a conversation matters, summarize it into:
reference/chatgpt/transcripts/ for high-signal conversation summaries
reference/chatgpt/notes/ for topic notes
Only then decide whether anything belongs in system/human.md.
5. Promotion rule
Promote to active memory only when the fact is:
explicit or strongly evidenced
current rather than historical-only
likely useful across many future conversations
low-risk to keep in context every turn
Everything else can stay in reference/chatgpt/.
Reference files
Read references/repository-layout.md when creating or extending the MemFS archive layout.
Notes on export formats
This skill is designed for newer exports that contain conversations.json with chat_messages, while still handling older shard-based exports with conversations-*.json and mapping graphs.
When in doubt, start with inspect-export.py instead of assuming the schema.
