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Generate or review mathematical-modeling competition AI tool usage statements and the anonymous AI工具使用详情.pdf. Use when users provide real AI usage records, or when checking a paper, statement, details PDF, code, and AI records for completeness, anonymity, and consistency.
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Tracks LLM token consumption and usage metrics for billing, monitoring, and optimization. Use this to log token usage, calculate costs, generate invoices, and understand which agents or users consume the most resources.
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ARK 套餐管理(Agent Plan / Coding Plan,个人版 + 企业版):查询持有 / 购买 / 续费 / 模型清单 / 轮换 APIKey,**以及企业版席位的全部管理操作**:列出席位(`plans team seat-list`)、给员工分配席位(`plans team seat-assign`)、查谁绑了哪个 seat、轮换席位 APIKey(`plans team rotate-apikey`)。也负责购买参数的逐步澄清，以及 `payment_failed` + Order ID 后回到已有订单补单/取消，禁止重新 buy。命中关键词:套餐 / 买 / 续费 / 列席位 / 看 seat 绑定 / 谁绑了哪个 seat / 给员工分席位 / 解绑 / 团队席位 admin 视图 / 轮换 APIKey。反触发：Token 额度包/资源包不是 Agent Plan/Coding Plan，应走 arkcli-profile 判断 platform 切面。**用量类问题(还剩多少额度 / 用了几成 / 每个 seat 用了多少)走 [arkcli-usage](../arkcli-usage/SKILL.md)。**动词路由:**列 / 绑 / 分 / 轮换 / 解绑** → 这里;**用 / 消耗 / 多少** → arkcli-usage。另含 harness-status:只读查看本机 AI Agent(claude-code/codex/opencode/openclaw/trae)上 Agent Plan 内置 MCP(豆包搜索/dataPro/OpenViking)装没装、key 就没就绪;命中:我的 MCP 装好了吗 / 查 MCP 安装状态 / 看 agent 上的 MCP / harness 状态。
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Assign the current or given task plan to oneself or to somebody else, with assigning it to oneself tracking the AI usage (time, tokens, and costs) of the session until the task is given back, which adds the accumulated usage to the usage attachment of the task. Use when the user wants to "assign", "take", "pick up", "give back", "hand over", or "unassign" the "task" or "plan".
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Query OpenRouter API costs, credits, and usage breakdown by model, provider, and date for the last 30 days. Triggers on API spending, check costs, openrouter usage, how much have I spent.
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Produces a deep, honest peer-review of how someone uses Claude Code by statistically analyzing their local session data (~/.claude/usage-data/ and ~/.claude/projects/). Trigger whenever the user asks to review, analyze, audit, or critique their own Claude Code usage, workflow, habits, or skill level — including phrases like "analyze my cc usage", "review my cc sessions", "peer review my cc workflow", "deeper than /insights", or any ask for an honest audit of their AI workflow. Also covers portfolio/hiring-manager framings (e.g. "portfolio for Anthropic/OpenAI/xAI"), but ALWAYS ask the user in Step 0 which version to build (self / hr / both) before running — never silently produce an HR report, because that version goes to outsiders and needs explicit privacy setup. The HTML report is laid out ledger-first for SELF (opening band, output/team/leak/trend ledgers, then peer review, 9-dim scoring, try-this-week, case study, claim-indexed evidence). The HR variant is a recruiter version v1: identity card, earned badges (threshold-based, earned-only), allowlist-filtered output ledger, one case study, and a methodology & scope disclosure — no scores, no peer-review memo, no charts.
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Observe the harness itself — (WS1) measure context budget as a deterministic, trendable inventory (tokens per CLAUDE.md / skill / agent, lexical redundancy, per-CI cold-write totals; committed history.jsonl), (WS2) reconstruct the real invocation trace (which skills/agents/tools fired and how they nested) from session transcripts, and (WS-A) roll the per-run CI traces up into a committed weekly usage record (per-skill invocation counts; usage.jsonl) — the usage side of the dead-weight join. Use for "context budget", "how big is CLAUDE.md / our skills", "is the harness bloating", "how do our agent loops actually run", "which skills actually get used", "trace this session", "loop station".
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Guides migration of .NET Framework Thread.Abort usage to cooperative cancellation in modern .NET. USE FOR: modernizing code that calls Thread.Abort, catching ThreadAbortException, replacing Thread.ResetAbort, replacing Thread.Interrupt for thread termination, resolving PlatformNotSupportedException or SYSLIB0006 after retargeting to .NET 6+, migrating ASP.NET Response.End or Response.Redirect(url, true) which internally call Thread.Abort. DO NOT USE FOR: code that only uses Thread.Join, Thread.Sleep, or Thread.Start without any abort, interrupt, or ThreadAbortException usage — these APIs work identically in modern .NET and need no migration. Also not for projects staying on .NET Framework, or Thread.Abort usage inside third-party libraries you do not control.
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Use when working with Devcycle — devCycle feature flag management, targeting rules, environments, variable management, and usage metrics. Covers feature creation, variation configuration, targeting rules, audience segments, and API usage tracking. Use when managing feature flags, configuring targeting rules, reviewing variable usage, or analyzing feature adoption in DevCycle.
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Bundle a question and selected project files for a second AI model to review code, investigate bugs, or assess design options. Preview the files and token usage before running through an API or browser.
Examine superpowers session records to document repeated work, departures from plans, delays, and token usage. Produce an evidence-based report with file and line references, and prepare bug-report material when requested.
Write and review Java services using conventions for naming, immutable data, exception handling, project structure, and tests. Detects Spring Boot or Quarkus from build files and applies the relevant framework rules.
Investigate unexpected costs in the ECC Tools GitHub App by tracing events, queued jobs, analysis workers, and billing logic. Identifies repeated pull request creation, usage-limit bypasses, unintended premium-model calls, and duplicate retries, then verifies targeted fixes.
Manage Docker containers, images, storage volumes, and networks with standard commands. Start multi-service applications with Docker Compose, investigate failures through logs, check disk usage, and review Dockerfiles for improvements.
Connect an application's LLM calls through the Caveman Cloud gateway to measure usage and cost without changing request content. The skill updates connection settings and verifies the integration with one small, real provider request.
Evaluate Caveman optimization observations by selecting a specific code change with operator approval. Compare the original and changed versions on identical inputs, checking both task quality and resource usage before deciding whether to keep the change.
Create an HTML sales presentation supporting contract renewal and additional user seats. Organize delivered value, usage evidence, and return on investment into a proposal that customer advocates can share with finance approvers.
Review Storybook's minimum supported browser versions using Plausible documentation traffic data. The skill presents usage evidence and proposed versions for approval, then updates pinned settings and documentation and opens a pull request with the recorded decision.
Monitor AI provider health, response times, usage quotas, and spending alerts in OmniRoute. Inspect circuit breakers, which temporarily block failing providers, and configure whether reaching a budget limit switches models, blocks requests, or issues warnings.
Manage AI provider connections from the terminal. List configured services, test connections individually or together, rotate credentials, and inspect usage metrics. Validate local configuration without contacting providers when checking its structure.
Manage OmniRoute API keys and provider authentication from the command line. Register, rotate, or revoke keys, inspect usage and expiration, set spending and model restrictions, and start or check OAuth sign-in connections.
Manage AI service connections in OmniRoute through its API. Add, update, remove, and test connections, maintain API keys and sign-in credentials, and inspect usage metrics and connection health.
Manage applications in the Flow Nexus marketplace, from finding and publishing apps to deploying templates with configuration settings. The skill also supports version management, usage analytics, and reviews of application quality and security.
Retrieve a single snapshot of recorded AI costs, including total spending, model breakdowns, the highest-cost session, and budget usage. Export JSON for scripts and dashboards, or Markdown for a quick review.