agentmods.dev

platform-diagnostics

platform-diagnostics is a skill for Claude Code, Codex from xi-zhao/OpenQuantum. It costs 52 tokens per session (916 once invoked), scanned A, original, MIT.

A read-only diagnostic procedure for checking whether an OpenQuantum platform's user interface, task runner, skills, and models are connected correctly. It produces a report with evidence for each check.

In plain words
What is it for?
Use it to inspect configuration, test model discovery and function calls, verify skill visibility, and run approved end-to-end quantum-harness checks.
Why use it?
It helps locate broken connections or incomplete setup before release or when troubleshooting the platform.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

not rated 75repo changed 25d ago A scan SkillSpector: pass 52 tokens original MIT

Good fit Use it to inspect configuration, test model discovery and function calls, verify skill visibility, and run approved end-to-end quantum-harness checks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xi-zhao/openquantum/platform-diagnostics
View source ↗ xi-zhao/OpenQuantum
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.

Any agent
npx skills add xi-zhao/OpenQuantum --skill platform-diagnostics
Clone the repo
git clone --depth 1 https://github.com/xi-zhao/OpenQuantum

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 platform-diagnostics

README.md
[![agentmods](https://agentmods.dev/badge/skills/xi-zhao/openquantum/platform-diagnostics/github.svg)](https://agentmods.dev/skills/xi-zhao/openquantum/platform-diagnostics)
Your own site
<a href="https://agentmods.dev/skills/xi-zhao/openquantum/platform-diagnostics"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/platform-diagnostics/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for platform-diagnostics

Your own site · 80×15
<a href="https://agentmods.dev/skills/xi-zhao/openquantum/platform-diagnostics"><img src="https://agentmods.dev/badge/skills/xi-zhao/openquantum/platform-diagnostics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 916 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00052 $0.00916
Opus 5.5 $0.00021 $0.00366
Sonnet 5.5 $0.00010 $0.00183
Haiku 4.5 $0.00005 $0.00092

Measured 25d ago against content hash e0e66fc39d6e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

platform-diagnostics 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 25d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (evals/run-evals.mjs, validators/validate-report.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/platform-diagnostics/SKILL.md · 52 lines

What it actually says

OpenQuantum 平台诊断

本能力只诊断四层架构，不实现量子业务功能。默认执行只读检查；除非用户明确授权，不修改配置、 不启动长期后台进程、不写入生产系统。

核心对象

诊断对象是一份 platform-diagnostics 报告。报告中的每个检查项必须有可复核证据；Capability Validator 根据检查结果推导的是诊断报告聚合状态，不是 Scientific Acceptance，也不能由模型自行宣称。

开始前读取并按 Capability Contract v1 校验 capability.yaml。生成报告时读取 artifacts/diagnostic-report.schema.json；验收时运行 validators/validate-report.mjs。

检查流程

先确定本次诊断范围和已有运行证据，只执行与问题相关的检查。未执行项按原 schema 标为 not_checked 并写明原因；局部修改不要求自动启动全部探针或外部模型请求。

  1. UI：确认 UI 只通过 Harness transport adapter 发出会话命令，不直接调用 Model Provider 或 MCP Server。
  2. Harness 配置：运行 npm run harness:config，确认组合能展开且默认模型属于 OpenQuantum route。
  3. Harness Host：检查 Host 根页面、session/list 和 session/modelCatalog Harness RPC；未启动时记录 not_checked， 不把静态配置当成运行证据。
  4. Skill：通过 Harness skills/list 或当前成功加载的 Skill 上下文证明项目 Skill 可发现。
  5. Model：只有本次任务要求且已授权外部模型请求时，运行 npm run models:probe -- --provider openquantum-public，分别验证目录、文本生成和 强制函数调用。不要把“接口返回 200”替代工具调用验收。
  6. 端到端：仅在任务要求科研链路验收且已授权外部模型请求时，运行 npm run e2e:quantum-harness -- --provider openquantum-public，确认 真实模型在 Harness Session 中产生 QGS tool/call / tool/result，且 Result Commit 与中央 Acceptance 通过复核。绕过 Harness MCP Client 直接调用 MCP Server SDK 不能替代这条证据。 本地连通/协议问题可使用仓库已有的 Harness 本地模型 fixture；标记其为协议证据，不能替代真实 Provider 或科学验收。已有凭据不等于获准请求外部模型，不切换现有模型路由。
  7. 可选路由：私有网关不可达时记录 warn 或可选检查项 fail；只要它不是当前任务的硬性要求， 不得覆盖公开主路由的有效证据。
  8. 按 schema 生成 JSON 报告，不得写入 API Key、Authorization header、完整 Prompt 或敏感科研数据。
  9. 运行 node .agents/skills/platform-diagnostics/validators/validate-report.mjs <report.json>。 只有 Validator 成功，报告才算形成有效评分。

判定规则

  • 任一必需检查为 fail：整体 blocked。
  • 无必需失败，但存在 warn、not_checked 或可选失败：整体 degraded。
  • 所有检查均为 pass：整体 ready。
  • pass 必须有运行证据；推测、配置存在、模型自述都不算证据。
  • 非 pass 检查必须给出 nextAction。

输出先给结论，再给证据和下一步。不要把运行成功等同于科学验收；具体量子能力的 Scientific Validator 只产生运行时 observations，Acceptance Profile 定义规则，central Acceptance Builder 推导验收；开发期 eval evidence 只用于回归和发布门禁。

Files

What ships with it

10 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. 25d ago Changed · +4 lines e0e66fc39d6e
  2. 1mo ago First seen · 48 lines · 52 tokens per session scan A 5d7603a815bd

Subscribe to this mod's changes

platform-diagnostics is a skill published in the GitHub repository xi-zhao/OpenQuantum (75 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 916 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-30.

Debuggingagentic-aiai-agentdeepseekdeepseek-harnessmcp-servermodel-context-protocol
Related

Other skills, from other repositories

opik-explain

Root-cause a specific Opik trace, or a pattern across traces, and return a grounded explanation. Uses the hosted Opik MCP when it is connected, and falls back to SDK scripting otherwise. Returns the root cause, the evidence spans as clickable Opik UI links, and one suggested next step. Use for "why did this trace…

comet-ml/opik-mcp · 113 tokens

triaging-incidents

Creating, triaging, updating, and analyzing ServiceNow incidents, including trend reports. Assess severity, check affected CIs, find related incidents, and recommend assignments. Use when the user mentions incidents, INC numbers, outages, service disruptions, ticket creation, triage, priority, severity, ITSM…

jschuller/mcp-server-servicenow · 89 tokens

error-handling

A set of rules for deciding how software should report failures and what information error messages should contain.

pingfanfan/hello-dsh · 51 tokens

ask-good-questions

Guidance for asking technical questions and reporting bugs so another developer can reproduce and answer them. It covers versions, environment details, minimal reproduction steps, expected and actual results, errors, and attempted fixes.

pingfanfan/hello-dsh · 61 tokens

perf-optimize

A performance-improvement guide that requires measuring real behaviour before changing code. It explains how to identify bottlenecks, test one change at a time, and check that the change did not break anything.

pingfanfan/hello-dsh · 44 tokens

diagnose-connectivity

Work out why a 3x-ui client cannot connect, in the order that finds the cause fastest — scope the outage, check the core, the inbound, the client's own limits, routing and outbounds, then the node. Use whenever someone reports "it stopped working", a client cannot connect, a site is unreachable through the proxy, or…

pyworkload/3x-ui-mcp · 83 tokens