New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Navigating Chatgpt History

by letta-ai in letta-ai/skills

Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand.

MITAuto-check passed

Install Navigating Chatgpt History

skills CLI
$ npx skills add letta-ai/skills --skill navigating-chatgpt-history -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install letta-ai/skills navigating-chatgpt-history --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/letta-ai/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/letta/navigating-chatgpt-history .claude/skills/navigating-chatgpt-history && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
navigating-chatgpt-history
GitHub stars
149
Token cost
~1.3k tokens
SKILL.md length
489 words
Files
8 (incl. scripts, references)
Skills in repo
22
Repo updated
First seen
Licence
MIT

At a glance

Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand.

  • Works in 5 steps: Anchor yourself in existing MemFS notes → Inspect before mining → Narrow, then render → …
  • Recalling what a past assistant knew
  • SKILL.md covers Good fits, Default posture, Archive layout in MemFS and Scripts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3

What it does

Navigating Chatgpt History is an agent skill from letta-ai/skills. 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.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/repository-layout.md`, `scripts/common.py` and `scripts/inspect-export.py`).

It works with OpenAI and Letta. The repository describes itself as: A shared repository for skills. Intended to be used with Letta Code, Claude Code, Codex CLI, and other agents that support skills. The licence is MIT.

When your agent uses it

  • Recalling what a past assistant knew
  • Searching old conversations
  • Rendering specific chats
  • Seeding reference memory from export sidecars

Example prompts

  • “Use the navigating-chatgpt-history skill to navigate archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand”
  • “/navigating-chatgpt-history”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Anchor yourself in existing MemFS notes
  2. Inspect before mining
  3. Narrow, then render
  4. Write findings to progressive memory first
  5. Promotion rule

What it can do on your machine

Read from SKILL.md and the folder at commit 6785511. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 6 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Navigating Chatgpt History loads about 1.3k tokens when it runs, and up to ~1.6k if it reads all its reference files. Until then it costs about 85 tokens; SKILL.md has 489 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~85
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from letta-ai/skills at commit 6785511, republished under its MIT licence (© letta-ai). 489 words, ~1,293 tokens.

Download SKILL.mdSave it as .claude/skills/navigating-chatgpt-history/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
navigating-chatgpt-history
description
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.

  1. read the MemFS archive index first if it exists: reference/chatgpt/index.md
  2. inspect the export with scripts/inspect-export.py
  3. search or list before rendering broad ranges
  4. preserve findings to reference/chatgpt/ first
  5. 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
  • reference/chatgpt/transcripts/NNN-slug.md — curated high-signal conversation summaries
  • reference/chatgpt/notes/ — topic-specific notes mined later

Prefer progressive memory. Keep active memory small.

Scripts

scripts/inspect-export.py

Use first. It inventories the export and reads sidecars such as memories.json, projects.json, and users.json.

bash
python3 scripts/inspect-export.py <export-path>
python3 scripts/inspect-export.py <export-path> --output /tmp/export-summary.md
scripts/list-conversations.py

Use to browse by title, recency, or message count.

bash
python3 scripts/list-conversations.py <export-path> --limit 25
python3 scripts/list-conversations.py <export-path> --title-contains Letta --sort messages
scripts/search-conversations.py

Use when titles are not enough.

bash
python3 scripts/search-conversations.py <export-path> --query "Recovery Bench"
python3 scripts/search-conversations.py <export-path> --query TFCC --role user --limit 20
scripts/render-conversation.py

Use for one conversation once you know the index.

bash
python3 scripts/render-conversation.py <export-path> --index 212
python3 scripts/render-conversation.py <export-path> --index 212 --compact-nontext --output /tmp/chat-212.md
scripts/render-range.py

Use only for focused batches after search narrows the field.

bash
python3 scripts/render-range.py <export-path> --start-index 210 --end-index 220 --output-dir /tmp/chat-range

Workflow

1. Anchor yourself in existing MemFS notes

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.

Show full SKILL.md (204 more words)Show less
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:

  1. list-conversations.py for browse
  2. search-conversations.py for content lookup
  3. render-conversation.py for deep read
  4. 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.

© letta-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in letta/navigating-chatgpt-history of letta-ai/skills.

  • SKILL.md
  • references/repository-layout.md
  • scripts/common.py
  • scripts/inspect-export.py
  • scripts/list-conversations.py
  • scripts/render-conversation.py
  • scripts/render-range.py
  • scripts/search-conversations.py

Open the folder on GitHubat commit 6785511

Compare with similar skills

Navigating Chatgpt History next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Navigating Chatgpt History compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Navigating Chatgpt History this skillletta-ai/skills149—~1.3kAutomated safety check: PassMIT
Image Generationletta-ai/letta-code3.6k—~1.3kAutomated safety check: PassApache-2.0
Geo Fundamentalswasp-lang/wasp19k9 repos~861Automated safety check: PassMIT
AI SDKvercel-labs/ai-facts16820 repos~1.2kAutomated safety check: PassNone
AI Image Generation and Editingzhayujie/CowAgent47k—~1.3kAutomated safety check: PassMIT
PR Design DocOpenHands/OpenHands91k—~2.4kAutomated safety check: PassMIT

Similar skills

  • Image Generation

    letta-ai/letta-code

    Generate images from text prompts (and optionally edit/remix input images).

    3.6k GitHub stars~1.3k tokensUpdated today
    Media & CreativeAuto-check passed

  • Geo Fundamentals

    wasp-lang/wasp

    Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).

    19k GitHub starsUsed in 9 repos~861 tokens
    Marketing & SEOAuto-check passed

  • AI SDK

    vercel-labs/ai-facts

    Official

    Answer questions about the AI SDK and help build AI-powered features.

    168 GitHub starsUsed in 20 repos~1.2k tokens
    AI & LLM EngineeringAuto-check passed

  • AI Image Generation and Editing

    zhayujie/CowAgent

    Generates or edits images from text prompts through a Python script that picks an image backend based on which API keys are configured.

    47k GitHub stars~1.3k tokensUpdated yesterday
    Media & CreativeAuto-check passed

  • PR Design Doc

    OpenHands/OpenHands

    For a non-trivial pull request, write a self-contained HTML design doc under the temporary .pr/ directory and link a visibility-appropriate preview in the PR description, so maintainers grasp the…

    91k GitHub stars~2.4k tokensUpdated today
    DevelopmentAuto-check passed

  • SEO Geo

    ReScienceLab/opc-skills

    SEO & GEO (Generative Engine Optimization) for websites. An agent skill from ReScienceLab/opc-skills.

    1.8k GitHub starsUsed in 4 repos~2.1k tokens
    Marketing & SEOAuto-check passed

More from letta-ai/skills

All 22 skills in this repo

  • Visual Identity

    letta-ai/skills

    Build and maintain a persistent visual identity for your agent using Flux Kontext Pro.

    149 GitHub stars~2.6k tokensUpdated 9 days ago
    Auto-check passed

  • AI News

    letta-ai/skills

    Fetch and summarize recent AI news from curated RSS feeds (Hugging Face, VentureBeat, The Verge, OpenAI, Anthropic, DeepMind, etc.) and YouTube channels (Yannic Kilcher, Two Minute Papers, AI…

    149 GitHub stars~582 tokensUpdated 9 days ago
    Auto-check passed

  • Creating Letta Code Channels

    letta-ai/skills

    Builds and debugs Letta Code channels, including first-party channel adapters and dynamic user channel plugins under ~/.letta/channels.

    149 GitHub stars~1.1k tokensUpdated 9 days ago
    Auto-check passed

  • Letta Configuration

    letta-ai/skills

    Configure LLM models and providers for Letta agents and servers.

    149 GitHub stars~1.3k tokensUpdated 9 days ago
    Auto-check: notes

  • Letta Filesystem To Memfs

    letta-ai/skills

    Migrates deprecated Letta Filesystem folders/files to MemFS using markdown document corpora, chunking, local lexical search, and QMD semantic search via the memfs-search skill.

    149 GitHub stars~1.3k tokensUpdated 9 days ago
    Auto-check passed

  • Memfs Search

    letta-ai/skills

    Semantic search over agent memory files. An agent skill from letta-ai/skills.

    149 GitHub stars~901 tokensUpdated 9 days ago
    Auto-check passed

Works with

Questions about Navigating Chatgpt History

What does Navigating Chatgpt History do?

Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand. Navigating Chatgpt History is an agent skill from letta-ai/skills. Navigates archived ChatGPT or Claude-style conversation exports and a MemFS reference archive on demand.

When should I use Navigating Chatgpt History?

Navigating Chatgpt History fits situations like: recalling what a past assistant knew; searching old conversations; rendering specific chats; seeding reference memory from export sidecars.

How do I install Navigating Chatgpt History in Claude Code?

Run `npx skills add letta-ai/skills --skill navigating-chatgpt-history -a claude-code`. Or copy the skill folder (letta/navigating-chatgpt-history in letta-ai/skills) into .claude/skills/navigating-chatgpt-history in your project. Claude Code loads it when a task matches its description.

How do I install Navigating Chatgpt History in Codex?

Run `npx skills add letta-ai/skills --skill navigating-chatgpt-history -a codex`. Or copy the skill folder (letta/navigating-chatgpt-history in letta-ai/skills) into .agents/skills/navigating-chatgpt-history in your project. Codex loads it when a task matches its description.

Can I use Navigating Chatgpt History in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add letta-ai/skills --skill navigating-chatgpt-history -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/navigating-chatgpt-history, .gemini/skills/navigating-chatgpt-history, .github/skills/navigating-chatgpt-history and .opencode/skills/navigating-chatgpt-history in your project.

What does Navigating Chatgpt History need to run?

Going by SKILL.md and its folder, Navigating Chatgpt History needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Navigating Chatgpt History access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Navigating Chatgpt History safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Navigating Chatgpt History use?

Navigating Chatgpt History is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Navigating Chatgpt History use?

About 1.3k tokens (SKILL.md is roughly 5.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 289 tokens, read only when the agent opens those files.

What are the alternatives to Navigating Chatgpt History?

Skills that share tags, products or a category with Navigating Chatgpt History: Image Generation (letta-ai/letta-code, 3.6k stars), Geo Fundamentals (wasp-lang/wasp, 19k stars), AI SDK (vercel-labs/ai-facts, 168 stars) and AI Image Generation and Editing (zhayujie/CowAgent, 47k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Navigating Chatgpt History?

letta-ai (a GitHub organization) maintains it in letta-ai/skills, which has 149 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 1, 2026.

Source: letta-ai/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.