跳到正文
cccskills
免费在 GitHub 公开

actual-setup

Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Set up Actual Computer (actual.inc) inference in Hermes.

集成工具OpenAI
查看安装方法

适用场景

  • Connecting Hermes with an Actual inference key
  • Running models through a local Actual daemon
  • Downloading and loading local models
  • Troubleshooting request errors and empty responses

包含的文件(2)

  • SKILL.md6.6 KB
  • references/opencode.md3.6 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Actual Computer Setup Skill

Sets up actual.inc (Actual Computer) as a Hermes inference provider. Actual turns the user's own hardware into a private inference cluster and exposes an OpenAI-compatible API two ways: a hosted end-to-end-encrypted relay at https://api.actual.inc (authenticated with an ac_ key), and a local on-device daemon at http://127.0.0.1:8080 (no auth on loopback). This skill does not install the Actual daemon for the user — device authorization requires a human in a browser.

When to Use

  • User wants to add actual.inc as an inference provider (cloud relay or local).
  • User has an ac_ key and wants Hermes routed through their Actual cluster.
  • User wants fully-local, on-device inference via the Actual daemon.
  • Troubleshooting: Actual requests failing with cryptic 400s or empty streams.

Prerequisites

  • Hermes has first-class actual provider support (provider id actual, aliases actual-computer, actualcomputer, aci). Do NOT configure Actual as a custom_providers / providers.actual.* entry on current Hermes — the built-in provider owns the name and handles base-url normalization, the Responses transport, and local no-auth automatically.
  • Relay mode: an Actual account and an ac_ inference key from https://actual.inc/user/keys.
  • Local mode: the user has installed the daemon (curl -fsSL "https://actual.inc/install" | bash) and completed device authorization by running actual once and opening the printed https://actual.inc/device?code=... URL in a browser. Relay that URL to the user and WAIT — never invent an email or authorize on their behalf. Codes expire in 5 minutes; re-run actual for a fresh one.

How to Run

Relay / API mode

  1. Put the key in .env (secrets only — never config.yaml): append ACTUAL_API_KEY=ac_... to ~/.hermes/.env.
  2. Verify the key and discover models with terminal:
    curl -s https://api.actual.inc/v1/models -H "Authorization: Bearer $ACTUAL_API_KEY"
    
  3. Select provider + model:
    hermes config set model.provider actual
    hermes config set model.default "MODEL_ID_FROM_DISCOVERY"
    
  4. Verify end-to-end:
    hermes chat -Q -q "Reply with exactly: ACTUAL_OK" --provider actual -m MODEL_ID
    

Local mode

  1. Human has installed + authorized the daemon (see Prerequisites).
  2. Download and load a model (scriptable once authorized):
    actual models search "qwen2.5 0.5b instruct gguf" --limit 8 --no-prompt
    # Downloads REQUIRE an explicit quantization (409 ambiguous_model_download otherwise):
    actual models download "Qwen/Qwen2.5-0.5B-Instruct-GGUF/Q4_K_M"
    actual models list        # note the INSTALLED name (differs from download id)
    actual models load "qwen2.5-0.5b-instruct-q4_k_m"   # load by installed name
    
  3. Point Hermes at the daemon. ACTUAL_BASE_URL with a loopback host flips the built-in provider into local no-auth mode automatically — no key needed: append ACTUAL_BASE_URL=http://127.0.0.1:8080 to ~/.hermes/.env, then:
    hermes config set model.provider actual
    hermes config set model.default "INSTALLED_MODEL_NAME"
    
  4. Verify (reduced toolset — see context-window pitfall below):
    hermes chat -Q -q "Reply with exactly: LOCAL_OK" --provider actual -m INSTALLED_NAME -t file,web
    

Quick Reference

ThingValue
Hosted relayhttps://api.actual.inc/v1 (normalized from bare host automatically)
Local daemonhttp://127.0.0.1:8080/v1 (no auth on loopback)
Key env varACTUAL_API_KEY (ac_...)
Base URL env varACTUAL_BASE_URL (loopback host ⇒ local no-auth mode)
Provider id / aliasesactual / actual-computer, actualcomputer, aci
TransportResponses API (codex_responses) — built-in, do not override
Cluster pinningX-Cluster-ID header via providers.actual.extra_headers in config.yaml
Model size guide0.5B Q4_K_M ~470MB (toy), 7-8B Q4_K_M ~4.5GB (daily driver), 32B ~20GB

Pitfalls

  1. reasoning_effort trap (handled by Hermes since the first-class provider). Actual's SGLang/vLLM backends accept only none/low/medium/high/max; xhigh/ultra used to fail with a cryptic Expecting value: line 1 column 1 (char 0) (a wrapped HTTP 400). The built-in provider clamps xhigh→high and ultra→max on the wire. If a request still 400s this way on an old Hermes, set a per-model cap: agent.reasoning_overrides.<model>: high in config.yaml.
  2. Context-window overflow on small local models. Hermes' default toolset is ~26k tokens of schemas plus a ~9k-token system prompt. A model loaded with a 32k context overflows before the first turn, and llama.cpp-family servers emit a bare data: [DONE] — Hermes reports Provider returned an empty stream with no finish_reason. This is NOT an SSE bug. Fixes: restrict tools (-t file,web), load the model with a larger n_ctx, or pick a >=64k-context model for the full toolset. Upstream tracking: #51448 (do not file new issues; add evidence there). Related but distinct: #65631 (HTTP-200 SSE carrying a 400), #56516 (reasoning-only streams).
  3. Download ids vs installed names. actual models download takes repo/QUANT and 409s without an explicit quantization; actual models load takes the INSTALLED name from actual models list.
  4. Reasoning models returning empty content. GLM/Qwen reasoning variants emit thinking in a separate reasoning field and can burn a small max_tokens entirely on reasoning. Give generous max_tokens before assuming failure.
  5. Do not create a custom provider named actual. Older setup guides (pre first-class support) wrote providers.actual.* config blocks. On current Hermes the built-in provider wins the name; stale custom blocks are ignored or conflict. Remove them and use the env vars + model.provider flow above.

Verification

# Relay:
hermes chat -Q -q "Reply with exactly: ACTUAL_OK" --provider actual -m MODEL
# Local (small model — reduced toolset):
hermes chat -Q -q "Reply with exactly: LOCAL_OK" --provider actual -m MODEL -t file,web
# Provider status (local no-auth shows key_source=local-offline):
hermes status

For other OpenAI-compatible clients (e.g. OpenCode), see references/opencode.md.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

1password

免费

Set up the 1Password CLI and use stored passwords or API keys in commands and configuration templates. Choose an authentication method, retrieve secret references, and pass credentials to processes through environment variables.

中文简介正在准备中。当前显示英语简介。

  • Installing and configuring the 1Password CLI
  • Running commands with stored API keys
  • Filling configuration templates with stored secrets
NousResearch/hermes-agent25.5万2026年10月18日 更新

Build an Excel workbook linking the income statement, balance sheet, and cash flow statement. Populate historical figures, create formula-based forecasts, compare scenarios, and check that balances and cash totals agree across all three statements.

中文简介正在准备中。当前显示英语简介。

  • Populating a financial model template with historical data
  • Building linked forecasts across three financial statements
  • Comparing base, upside, and downside scenarios
NousResearch/hermes-agent25.5万2026年10月18日 更新

Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.

中文简介正在准备中。当前显示英语简介。

  • Move a single-GPU training script to multiple GPUs
  • Configure mixed precision training
  • Set up DeepSpeed or FSDP to reduce memory use
NousResearch/hermes-agent25.5万2026年10月18日 更新

Test an app from the perspective of a frustrated, less technical user. Find confusing steps and barriers, separate actionable usability problems from personal complaints, and produce screenshots, suggested fixes, and prioritized tickets.

中文简介正在准备中。当前显示英语简介。

  • Reviewing usability before a launch or demo
  • Checking the experience of a new user
  • Finding friction in signup and subscription flows
NousResearch/hermes-agent25.5万2026年10月18日 更新

Resolve Git merge conflicts between two agents by comparing both changes and their stated goals. Combine compatible work, explain choices where designs differ, and verify the merged result with project builds or tests.

中文简介正在准备中。当前显示英语简介。

  • Resolving conflicts between branches edited by two agents
  • Integrating changes from parallel worktrees
  • Reviewing reasons for conflicting design choices
NousResearch/hermes-agent25.5万2026年10月18日 更新

agentmail

免费

Give an AI agent its own email inbox using the AgentMail CLI. Send and receive messages, handle replies and attachments, complete email verification codes, and trigger ongoing workflows when new mail arrives.

中文简介正在准备中。当前显示英语简介。

  • Creating an email address for an AI agent
  • Sending messages and handling replies or attachments
  • Completing verification with codes received by email
NousResearch/hermes-agent25.5万2026年10月18日 更新

查看 NousResearch 的全部技能

报告此技能的问题