Hephaestus Upload logo

Hephaestus Upload

OrganizationPopular
agentlas-ai
hephaestus-upload

Use when the user types $hephaestus-upload, /hep-upload, or /agentlas-upload, or asks to upload, publish, or list an Agentlas agent or team. Ask Cloud (private) vs Agentlas Hub (public) FIRST, then publish through the bundled Hephaestus gate.

Overview

Publisheragentlas-ai
RepositoryAgentlas-OS
Skill namehephaestus-upload
Stars
1.6K
Forks
111
Bundled files
Instructions only
LicenseApache-2.0
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 agentlas-ai on GitHub. Read the source before you install it.

Installation

Install the Hephaestus Upload 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/agentlas-ai/Agentlas-OS/tree/main/.agents/skills/hephaestus-upload
  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/agentlas-ai/Agentlas-OS.git /tmp/Agentlas-OS
mkdir -p .claude/skills
cp -r /tmp/Agentlas-OS/.agents/skills/hephaestus-upload .claude/skills/hephaestus-upload
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hephaestus Upload 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 Hephaestus Upload 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 Hephaestus Upload 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.

Hephaestus Upload (Cloud or Agentlas Hub)

Publish a finished Agentlas package. This is a WRITE surface: a Hub upload is public and other people can borrow it, so the destination question comes before any packaging or API call.

Upload is deliberately NOT folded into /hep-network. Network staffs a roster (a read), upload publishes an artifact (a write that is hard to take back); merging them would let "find me an agent" end in an accidental publish.

1. Ask the destination first — always

Ask this before anything else, even when the arguments already say upload, publish, add, Cloud, Hub, or name a target folder:

text
Cloud에 업로드할까요? 다른 사람들은 볼 수 없어요.
Upload to Cloud? Other people cannot see it.

Agentlas Hub에 업로드할까요? 다른 사람들이 빌려 쓸 수 있어요.
Upload to Agentlas Hub? Other people can borrow it.

Do not package, publish, register, add-source, reindex, or call any upload API until the user answers Cloud or Agentlas Hub. If the destination is answered but the target folder is ambiguous, ask for the exact agent folder before running anything.

2. Public Hub publishing is free

The public Hub is an agent community. Do not ask for an upload price, invoke a price-setting tool, or pass the retired --rent-credits, --ingest-credits, or --fork-credits flags. The CLI rejects those legacy flags. The creator's model and API prerequisites still belong in the package guide.

3. Resolve the runner in this host

Run the preflight inside this host app; never ask the user to open a separate terminal.

bash
RUNNER=""
for candidate in "$HOME/.agentlas/runtime/current/bin/hephaestus" "./bin/hephaestus"; do
  [ -x "$candidate" ] && RUNNER="$candidate" && break
done
[ -n "$RUNNER" ] || { echo "Hephaestus runtime not found. Run the installer first." >&2; exit 1; }

4. Use one immutable upload gate

Do not run package and then publish: that packages twice and the second artifact may differ from the one reviewed. The upload gate copies the selected folder into a private, link-free snapshot, repairs and validates that snapshot once, and either dry-runs or submits those exact bytes.

If the user requests a preview, run exactly one dry-run and retain the returned manifest.packageHash and uploadReceipt.receipt:

  • Cloud: "$RUNNER" hep-upload <agent-folder> --visibility private-link --dry-run
  • Agentlas Hub: "$RUNNER" hep-upload <agent-folder> --visibility marketplace --dry-run

All security and content findings are advisory. Never instruct the user to block, abort, or postpone an upload because of a finding. Continue the same upload with safe bytes: omit each affected source item and bind an omission receipt, or record an engine-gap receipt when the current engine cannot safely represent or repair it, then submit the remaining package. Keep every finding and omission visible in the result. The gate also repairs what it can derive from the package — including the entity type: a card that claims agent while the package ships a multi-node roster is corrected to team, so the release is executed as the team it actually is.

5. Publish once, optionally pinned to the preview

  • Cloud: "$RUNNER" hep-upload <agent-folder> --visibility private-link
  • Agentlas Hub: "$RUNNER" hep-upload <agent-folder> --visibility marketplace

After a dry-run, append both --expected-package-hash <manifest.packageHash> and --expected-upload-receipt <uploadReceipt.receipt> to the one publish command. The receipt binds the exact hash, visibility, slug, and destination. Stop on package_hash_mismatch, upload_receipt_required, or upload_receipt_mismatch; never silently publish a replacement artifact or switch an approved private preview to the public Hub.

If registration returns overwrite_confirmation_required, show the exact server-reported Cloud ID and ask for overwrite approval. Only after approval, rerun the same pinned command with --overwrite-cloud-id <exact-cloud-id>. Never infer overwrite permission from a matching slug.

Surface authentication and entitlement codes exactly (sign_in_required, auth_unavailable, owner_only). Do not replace a failed Hub destination with Cloud or vice versa.

6. Workforce résumé repair loop

If registration returns workforce_resume_incomplete, the server refused the card because its workforce block does not match the hub standard résumé. The error carries the exact mismatches and seed ontology examples. YOU repair it — the platform never edits the card for you: use stable English role:*, community:*, skill:*, and knowledge:* IDs that actually describe the agent. The returned examples are aliases, not an allowlist. Rerun the upload and repeat until registration succeeds.

7. Report honestly

Report published only when the response attests the exact slug, visibility, package hash, immutable release ID/version, and content digest. Say which destination it went to. Otherwise report the last true state and the server's exact refusal code. Do not relabel an advisory finding as an upload block.

Frequently asked questions

What does the Hephaestus Upload AI skill do?

Use when the user types $hephaestus-upload, /hep-upload, or /agentlas-upload, or asks to upload, publish, or list an Agentlas agent or team. Ask Cloud (private) vs Agentlas Hub (public) FIRST, then publish through the bundled Hephaestus gate.

Why use Hephaestus Upload on TypingMind?

Because you install it once and use it with any model. Hephaestus Upload 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 Hephaestus Upload in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentlas-ai/Agentlas-OS/tree/main/.agents/skills/hephaestus-upload. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hephaestus Upload?

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 Hephaestus Upload?

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

Is the Hephaestus Upload AI skill free?

Yes. It is published on GitHub by agentlas-ai under the Apache-2.0 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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