Agent skill

Letta Filesystem To Memfs

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.

MITAuto-check passedDocuments & Office

Install Letta Filesystem To Memfs

skills CLI

$ npx skills add letta-ai/skills --skill letta-filesystem-to-memfs -a claude-code

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

GitHub CLI

$ gh skill install letta-ai/skills letta-filesystem-to-memfs --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/letta-filesystem-to-memfs .claude/skills/letta-filesystem-to-memfs && 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/

Paste into Claude Code

Install the "letta-filesystem-to-memfs" agent skill from https://github.com/letta-ai/skills/tree/main/letta/letta-filesystem-to-memfs into .claude/skills/letta-filesystem-to-memfs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "letta-filesystem-to-memfs", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/letta-ai/skills/tree/main/letta/letta-filesystem-to-memfs

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add letta-ai/skills --skill letta-filesystem-to-memfs -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install letta-ai/skills letta-filesystem-to-memfs --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/letta/letta-filesystem-to-memfs .agents/skills/letta-filesystem-to-memfs && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "letta-filesystem-to-memfs" agent skill from https://github.com/letta-ai/skills/tree/main/letta/letta-filesystem-to-memfs into .agents/skills/letta-filesystem-to-memfs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "letta-filesystem-to-memfs", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add letta-ai/skills --skill letta-filesystem-to-memfs -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install letta-ai/skills letta-filesystem-to-memfs --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/letta/letta-filesystem-to-memfs .cursor/skills/letta-filesystem-to-memfs && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "letta-filesystem-to-memfs" agent skill from https://github.com/letta-ai/skills/tree/main/letta/letta-filesystem-to-memfs into .cursor/skills/letta-filesystem-to-memfs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "letta-filesystem-to-memfs", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/letta-ai/skills.git --path letta/letta-filesystem-to-memfs

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add letta-ai/skills --skill letta-filesystem-to-memfs -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install letta-ai/skills letta-filesystem-to-memfs --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/letta/letta-filesystem-to-memfs .gemini/skills/letta-filesystem-to-memfs && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "letta-filesystem-to-memfs" agent skill from https://github.com/letta-ai/skills/tree/main/letta/letta-filesystem-to-memfs into .gemini/skills/letta-filesystem-to-memfs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "letta-filesystem-to-memfs", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install letta-ai/skills letta-filesystem-to-memfs

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add letta-ai/skills --skill letta-filesystem-to-memfs -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/letta/letta-filesystem-to-memfs .github/skills/letta-filesystem-to-memfs && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "letta-filesystem-to-memfs" agent skill from https://github.com/letta-ai/skills/tree/main/letta/letta-filesystem-to-memfs into .github/skills/letta-filesystem-to-memfs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "letta-filesystem-to-memfs", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add letta-ai/skills --skill letta-filesystem-to-memfs -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install letta-ai/skills letta-filesystem-to-memfs --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/letta-ai/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/letta/letta-filesystem-to-memfs .opencode/skills/letta-filesystem-to-memfs && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "letta-filesystem-to-memfs" agent skill from https://github.com/letta-ai/skills/tree/main/letta/letta-filesystem-to-memfs into .opencode/skills/letta-filesystem-to-memfs/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "letta-filesystem-to-memfs", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: letta-filesystem-to-memfs
GitHub stars: 149
Token cost: ~1.3k tokens
SKILL.md length: 385 words
Files: 6 (incl. scripts, references)
Skills in repo: 22
Repo updated: Oct 1, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

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.

Works in 5 steps: Extract PDFs/docs to markdown text. → Chunk the text into stable markdown… → Write a small pinned index under… → …
Replacing folders.files.upload
SKILL.md covers Quick workflow, Layout, Old API mapping and Heuristics, plus 2 more sections
Runs Python scripts from its folder; calls uv and git; reaches arxiv.org

What it does

Letta Filesystem To Memfs is an agent skill from 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. Use when replacing folders.files.upload, working with PDFs or document QA, or emulating openfile, grepfile, and searchfile behavior.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/design.md`, `scripts/ingest_documents.py` and `scripts/letta_fs_to_memfs.py`).

It sits in Documents & Office. It works with Letta and Git. 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

Replacing folders.files.upload
Working with PDFs
Emulating openfile
Searchfile behavior

Example prompts

“Use the letta-filesystem-to-memfs skill to migrate deprecated Letta Filesystem folders/files to MemFS using markdown document corpora, chunking…”
“/letta-filesystem-to-memfs”

Requirements

Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

Extract PDFs/docs to markdown text.
Chunk the text into stable markdown files under documents///chunks/.
Write a small pinned index under system/filesystem/.md.
Index only the corpus chunk files in QMD for semantic search.
Review the MemFS git diff. Commit only if asked.

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 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- uv
- git
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- arxiv.org
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

Letta Filesystem To Memfs loads about 1.3k tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 85 tokens; SKILL.md has 385 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: ~2.1k

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). 385 words, ~1,296 tokens.

Download SKILL.mdSave it as .claude/skills/letta-filesystem-to-memfs/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: letta-filesystem-to-memfs
description: 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. Use when replacing folders.files.upload, working with PDFs or document QA, or emulating open_file, grep_file, and search_file behavior.
license: MIT

Letta Filesystem to MemFS

Use this skill when a user wants the old Letta Filesystem behavior: upload documents, chunk them, attach them to an agent, and let the agent search/open them.

MemFS is not the same product. It is git-backed markdown memory. To mimic the old workflow, use the bundled CLI:

Extract PDFs/docs to markdown text.
Chunk the text into stable markdown files under documents/<corpus>/<doc>/chunks/.
Write a small pinned index under system/filesystem/<corpus>.md.
Index only the corpus chunk files in QMD for semantic search.
Review the MemFS git diff. Commit only if asked.

Quick workflow

bash

# Set this to the skill directory shown in the skill load header.
SKILL_DIR="/path/to/letta-filesystem-to-memfs"

# From any directory. MEMORY_DIR should point at the target agent's memfs repo.
uv run --with pymupdf \
  "$SKILL_DIR/scripts/letta_fs_to_memfs.py" ingest \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs \
  --source ./docs/ \
  --source ./guide.pdf \
  --source https://arxiv.org/pdf/2310.08560

cd "$MEMORY_DIR"
git status --short
git diff --stat

Search the chunk corpus lexically:

bash

uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" search \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs \
  "memory hierarchy" \
  -n 5

Semantic search with QMD. The CLI creates a corpus-scoped QMD collection over chunk files only:

bash

uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd setup \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs

uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd query \
  --memory-dir "$MEMORY_DIR" \
  --corpus product-docs \
  "memory hierarchy" \
  -n 5

Use qmd reindex after changing corpus files, and qmd search / qmd vsearch when you specifically want keyword-only or vector-only search.

Layout

The ingest script writes:

txt

system/filesystem/<corpus>.md
  Pinned index and operating instructions for the corpus.

documents/<corpus>/manifest.md
  Corpus manifest.

documents/<corpus>/<doc-slug>/manifest.md
  Per-document metadata and chunk table.

documents/<corpus>/<doc-slug>/chunks/chunk-0001.md
  Chunk content with frontmatter metadata.

documents/<corpus>/chunks.jsonl
  Machine-readable chunk export for custom indexing or debugging.

Old API mapping

Old Filesystem	MemFS mimic
`folders.create`	`--corpus <name>` creates `documents/<corpus>/`
`folders.files.upload`	`letta_fs_to_memfs.py ingest --source <file-or-directory-or-url>`
OCR/chunk/embed job	Extract + chunk locally; `qmd setup` / `qmd reindex` for semantic search
`agents.folders.attach`	Enable MemFS, then review and sync repo changes when appropriate
`open_file`	Read chunk markdown files by path
`grep_file`	`rg` or `letta_fs_to_memfs.py search`
`search_file`	`letta_fs_to_memfs.py qmd query` over the corpus chunk collection

Show full SKILL.md (180 more words)Show less

Heuristics

Use system/filesystem/<corpus>.md for the small always-visible index only.
Do not pin full documents into system/; it will bloat the prompt.
Keep chunks outside system/, usually under documents/<corpus>/....
Passing a directory to --source recursively ingests supported files (.pdf, .md, .txt, .json, .csv, .html, .xml).
Use --glob / --exclude for messy directories. Defaults exclude .git, node_modules, .venv, and __pycache__.
URL downloads default to --max-download-mb 100; set 0 for unlimited.
Re-ingesting the same document slug replaces that document's old chunk directory, so stale chunks do not survive chunk-size changes.
Use chunk sizes around 2,000-4,000 characters with 200-500 character overlap.
Use the CLI's qmd subcommands when the user needs semantic search over many chunks.
Preserve source URLs, checksums, page markers, chunk numbers, and corpus names in the generated files.

CLI reference

bash

uv run --with pymupdf "$SKILL_DIR/scripts/letta_fs_to_memfs.py" ingest --help
uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" search --help
uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd setup --help
uv run "$SKILL_DIR/scripts/letta_fs_to_memfs.py" qmd query --help

Compatibility wrappers remain for older examples:

bash

uv run --with pymupdf "$SKILL_DIR/scripts/ingest_documents.py" --memory-dir "$MEMORY_DIR" --corpus docs --source ./docs
uv run "$SKILL_DIR/scripts/search_corpus.py" --memory-dir "$MEMORY_DIR" --corpus docs --query "refund policy"

PDF notes

The ingest script uses PyMuPDF when it sees a PDF. Run it with uv run --with pymupdf ....

For scanned PDFs or complex tables, use the tools/extracting-pdf-text skill first, then ingest the extracted markdown/text file with this skill.

See references/design.md for design notes and edge cases.

Files

SKILL.md and 5 other files (scripts, references) in letta/letta-filesystem-to-memfs of letta-ai/skills.

SKILL.md
LICENSE
references/design.md
scripts/ingest_documents.py
scripts/letta_fs_to_memfs.py
scripts/search_corpus.py

Open the folder on GitHubat commit 6785511

Compare with similar skills

Letta Filesystem To Memfs 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.

Letta Filesystem To Memfs compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Letta Filesystem To Memfs this skillletta-ai/skills	149	—	~1.3k	Automated safety check: Pass	MIT	9 days ago
Managing Shared Memoryletta-ai/letta-code	3.6k	—	~1.4k	Automated safety check: Pass	Apache-2.0	today
Syncing Memory Filesystemletta-ai/letta-code	3.6k	—	~1.9k	Automated safety check: Pass	Apache-2.0	yesterday
Portaljs Add Datasetdatopian/portaljs	2.4k	1 repos	~1.6k	Automated safety check: Pass	MIT	3 days ago
Submit Mentors To CommunityJunieXD/AutoEmailSender	150	—	~471	Automated safety check: Pass	GPL-3.0	2 days ago
Spfx Releasepnp/docker-spfx	134	—	~572	Automated safety check: Notes	MIT	3 mo ago

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Questions about Letta Filesystem To Memfs

What does Letta Filesystem To Memfs do?

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. Letta Filesystem To Memfs is an agent skill from 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.

When should I use Letta Filesystem To Memfs?

Letta Filesystem To Memfs fits situations like: replacing folders.files.upload; working with PDFs; emulating openfile; searchfile behavior.

How do I install Letta Filesystem To Memfs in Claude Code?

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

How do I install Letta Filesystem To Memfs in Codex?

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

Can I use Letta Filesystem To Memfs 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 letta-filesystem-to-memfs -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/letta-filesystem-to-memfs, .gemini/skills/letta-filesystem-to-memfs, .github/skills/letta-filesystem-to-memfs and .opencode/skills/letta-filesystem-to-memfs in your project.

What does Letta Filesystem To Memfs need to run?

Going by SKILL.md and its folder, Letta Filesystem To Memfs needs Python for the scripts in its folder and the command-line tools its instructions call (uv and git). Our summary lists: Python 3.

Does Letta Filesystem To Memfs access the network?

SKILL.md names 1 domain. In commands or code: arxiv.org; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Letta Filesystem To Memfs 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 Letta Filesystem To Memfs use?

Letta Filesystem To Memfs 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 Letta Filesystem To Memfs 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 803 tokens, read only when the agent opens those files.

What are the alternatives to Letta Filesystem To Memfs?

Skills that share tags, products or a category with Letta Filesystem To Memfs: Managing Shared Memory (letta-ai/letta-code, 3.6k stars), Syncing Memory Filesystem (letta-ai/letta-code, 3.6k stars), Portaljs Add Dataset (datopian/portaljs, 2.4k stars) and Submit Mentors To Community (JunieXD/AutoEmailSender, 150 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Letta Filesystem To Memfs?

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.

Install Letta Filesystem To Memfs

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Letta Filesystem to MemFS

Quick workflow

Layout

Old API mapping

Heuristics

CLI reference

PDF notes

Files

Compare with similar skills

Similar skills

More from letta-ai/skills

Related

Questions about Letta Filesystem To Memfs