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aleph

/aleph - External memory workflow for large local data.

Overview

PublisherHmbown
Repositoryaleph
Skill namealeph
Stars
218
Forks
25
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Hmbown on GitHub. Read the source before you install it.

Installation

Install the Aleph AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Hmbown/aleph/tree/main/plugins/aleph/skills/aleph
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Hmbown/aleph.git /tmp/aleph
mkdir -p .claude/skills
cp -r /tmp/aleph/plugins/aleph/skills/aleph .claude/skills/aleph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aleph in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Aleph on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Aleph is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

/aleph - External Memory Workflow

Core rule: keep whole contexts out of the prompt. Return only focused slices or compact derived results.

This plugin bundles the Aleph MCP launcher, /aleph skill, aleph-expert agent, and an install-check hook. It assumes the aleph executable is already installed and available on PATH.

Note: tool names may appear as mcp__aleph__load_workspace_manifest in some clients.

The 5-Phase Loop

1. Load

Pick the correct front door:

  • Large repo or codebase: load_workspace_manifest(...)
  • Single large file: load_file(...)
  • Inline or generated content: load_context(...)

Repo-scale default:

text
load_workspace_manifest(paths=["src", "tests"], context_id="repo")
rg_search(
  pattern="FastAPI|APIRouter|router\\.",
  paths=["src", "tests"],
  load_context_id="routes"
)
load_file(path="pyproject.toml", context_id="pyproject")

Single-file default:

text
load_file(path="/absolute/path/to/large.log", context_id="log")
search_context(pattern="ERROR|WARN", context_id="log")
peek_context(context_id="log", start=1, end=60, unit="lines")

Do not start repo work by reading files one by one when load_workspace_manifest(...) is the right first move.

2. Orient

  • Use search_context(...) to find relevant regions
  • Use peek_context(...) to inspect small ranges
  • Use semantic_search(...) for meaning-based lookup
  • Use chunk_context(...) when navigability matters
  • Use rg_search(...) to sweep repo trees quickly

Search before peeking. Pull only the slices you need.

3. Compute

  • Use exec_python(...) for heavier analysis with ctx bound in the sandbox
  • Aleph defaults to output_feedback="full"; exec_python(...) is not print-only
  • In full mode Aleph can return stdout, stderr, error text, and a rendered return value
  • If output volume becomes distracting, optionally use configure(output_feedback="metadata")
  • Use built-in helpers such as search, peek, lines, chunk, extract_*, semantic_search, and cite
  • Store compact derived values in variables like summary, counts, matches, or result
  • Retrieve only those derived values with get_variable(...)
  • Treat get_variable("ctx") as blocked for plugin workflows; use bounded slices or compact derived variables instead

Example:

text
exec_python(code="""
matches = search(r"ERROR|WARN")
counts = {"matches": len(matches)}
summary = f"{counts['matches']} matching lines"
""", context_id="log")
get_variable(name="summary", context_id="log")

4. Recurse

Real recursion helper signatures:

text
sub_query(prompt, context_slice=None)
sub_query_batch(prompt, context_slices, limit=None)
sub_query_map(prompts, context_slices=None, limit=None, parallel=True)
sub_aleph(query, context=None)

Runtime guidance:

  • Use sub_query_batch(...) for one prompt over many slices
  • Use sub_query_map(...) for distinct prompts, keeping parallel=True unless you need sequential execution
  • Use configure(sub_query_share_session=true) when nested agents need access to parent contexts
  • For depth 3+, use configure(sub_query_timeout=300, sandbox_timeout=300)

5. Converge

  • Use evaluate_progress(...) when the answer is not yet stable
  • Loop back through orient and compute when confidence is low
  • Use summarize_so_far(...) if the trajectory is getting long
  • Use finalize(answer=..., confidence=..., context_id=...) when done

Depth Invocation

Users can request a specific recursion depth with /aleph N target.

InvocationDepthStrategy
/aleph file.py1Direct file analysis with load_file, search_context, peek_context, exec_python
/aleph repo/1Repo analysis with load_workspace_manifest, rg_search, targeted load_file, exec_python
/aleph 2 file.py2Fan-out with sub_query_batch or sub_query_map
/aleph 3 file.py3Recursive sub_aleph with longer timeouts
/aleph 4 file.py4Deep recursion with explicit timeout tuning

Escalation rule:

  • Start at depth 1
  • Move to depth 2 when the answer needs chunk-level fan-out
  • Move to depth 3 or 4 only when nested structure or recursive synthesis is required

Repo vs File Workflow

When the user points at a repo, codebase, or project tree:

  • Start with load_workspace_manifest(...)
  • Use rg_search(...) to locate candidate files
  • Load only the files you actually need with load_file(...)
  • Compute inside Aleph instead of pasting file contents into the prompt

When the user points at one large file:

  • Start with load_file(...)
  • Search, peek, and compute inside Aleph
  • Pull back only the answer, a small slice, or a compact derived value

Anti-Patterns

  • Using read_file(...) as the default entry point for large files or repos
  • Loading a whole repo file-by-file when load_workspace_manifest(...) is the better first step
  • Treating get_variable("ctx") as a valid plugin workflow
  • Pasting raw file or repo content into the prompt when Aleph can search or compute instead
  • Claiming that exec_python(...) only returns print() output
  • Pinning a nested API backend in the checked-in plugin wrapper

Frequently asked questions

What does the Aleph AI skill do?

/aleph - External memory workflow for large local data.

Why use Aleph on TypingMind?

Because you install it once and use it with any model. Aleph is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Aleph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Hmbown/aleph/tree/main/plugins/aleph/skills/aleph. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aleph?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Aleph?

As many as you like. As long as a model supports skills, you can use Aleph with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aleph AI skill free?

Yes. It is published on GitHub by Hmbown under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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