configuring-compute

configuring-compute is a skill for Claude Code, Codex from desirecore/market. It costs 80 tokens per session (1,578 once invoked), scanned A, original, MIT.

A governed setup tool for configuring DesireCore's compute providers, which supply models or other computing services.

In plain words
What is it for?
Use it to inspect providers, turn them on or off, synchronize available models, and add or replace a provider's API key.
Why use it?
It provides a controlled way to enable providers, sync their models, and manage API keys without exposing stored secrets.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/desirecore/market/configuring-compute
Any agent
npx skills add desirecore/market --skill configuring-compute
Clone the repo
git clone --depth 1 https://github.com/desirecore/market

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for configuring-compute

README.md
[![agentmods](https://agentmods.dev/badge/skills/desirecore/market/configuring-compute.svg)](https://agentmods.dev/skills/desirecore/market/configuring-compute)
Your own site
<a href="https://agentmods.dev/skills/desirecore/market/configuring-compute"><img src="https://agentmods.dev/badge/skills/desirecore/market/configuring-compute.svg" alt="Measured on agentmods" height="20"></a>
Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,578 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00080 $0.01578
Opus 5 $0.00040 $0.00789
Sonnet 5 $0.00016 $0.00316
Haiku 4.5 $0.00008 $0.00158

Measured 4d ago against content hash 06a1881a74aa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

configuring-compute scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/configuring-compute/SKILL.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Configure Compute

Configure DesireCore compute through governed tools. Do not call the local /api/compute/* management endpoints from Bash, HttpRequest, or scripts: those endpoints intentionally require a trusted renderer Origin and instance token.

Security contract

  • API keys are write-only for agents. Never read secrets.json, request ComputeCredential(action='get', raw=true), or repeat a key in tool results or chat.
  • Use ComputeCredential(action='set') to create or replace the key of an existing user-managed provider. If the provider has no credential reference, the tool creates and attaches one without returning the plaintext. This path is approval-gated and audited; approval cards, tool events, receipts, and session history redact the sensitive value. System-managed credentials are not writable.
  • Pass a value to ComputeCredential(action='set') only when the user already supplied the replacement key in the current request. If the agent must never handle the plaintext, use the GUI to focus the password field, let the user type into it directly, then continue the save flow. Never ask for the key in ordinary chat or read the masked field back into the model.
  • credentialMode=none means the provider needs no key. Ollama is treated as none even when an older config does not declare the field.
  • If the user asks for the current key, explain that the agent can replace it but cannot read it back. The human-only UI reveal flow remains separate.

Workflow for an existing provider

First confirm ManageCompute is available through the current tool catalog. If it is absent (for example on an older installed client), use the governed GUI workflow below for the whole task; do not fall back to local HTTP.

  1. Call ManageCompute(action='list'). Record the exact provider ID, enabled state, credential mode, status, and model count.
  2. If credential mode is required and the user supplied a new key in the current request, call ComputeCredential(action='set', providerId=..., value=...). Do not echo the value. If the agent must not handle plaintext, use the human-entry GUI flow below. For none, skip this step.
  3. Call ManageCompute(action='set_enabled', providerId=..., enabled=true).
  4. Call ManageCompute(action='sync_models', providerId=...). For Ollama this discovers locally installed models; for supported cloud providers it merges the built-in model list.
  5. Call InspectModels to verify the intended model is selectable. When the user asked for a real test, run one short fixed-model conversation and verify the run receipt names the requested provider/model.

Read the full file on GitHub · 134 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 134 lines · 80 tokens per session scan A 06a1881a74aa

Subscribe to this mod's changes

configuring-compute is a skill published in the GitHub repository desirecore/market (2 stars, last pushed 4d ago), licensed MIT. It adds 80 tokens to every session and 1,578 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

designing-architectures

Architecture knowledge reference covering API design, security architecture, cloud-native patterns, caching strategies, message queues, and data security. Use when designing system architecture, APIs, or cloud-native infrastructure.

telagod/code-abyss · 43 tokens

data-distributed-compute

Use this skill when designing distributed compute for Hadoop MapReduce, Spark, Dask, Ray, YARN, or K8s resource management. This skill enforces: execution model selection, cluster topology, shuffle optimization, data locality, speculative execution, and resource tuning. Do NOT use for: single-node compute, GPU-only…

j4flmao/agent-skills · 85 tokens

com.googleapis.compute-mcp-mcp

Provides capabilities that let LLM agents perform a range of infrastructure management tasks.

Friz-zy/ai-capability-registry · 23 tokens

colab-remote

Orchestrate Google Colab Pro/Pro+ GPU instances as remote training backends via SSH. Compounds /agent-browser (to launch Colab sessions and install colab-ssh) with SSH (to operate the runtime remotely). Use when: (1) launching a Colab notebook for GPU training, (2) running training jobs on Colab from the local…

broomva/skills · 180 tokens

tao-run-on-kubernetes

Kubernetes execution platform — submits TAO container jobs as k8s Jobs with NVIDIA GPU scheduling; single-pod for one node, Indexed Jobs for multi-node distributed training. Use when running on EKS / GKE / AKS / on-prem clusters with the NVIDIA GPU Operator installed, or when integrating TAO into an existing…

NVIDIA-TAO/tao-skill-bank · 80 tokens

configuration

Skill "configuration" from chaterm/terminal-skills, covering openclaw 配置管理, 概述, 核心配置文件, application.yml 结构 and /opt/openclaw/conf/application.yml.

chaterm/terminal-skills · 8 tokens