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Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown. Use when doing a monthly retrospective or planning the month ahead.

Generate a daily standup summary from recent Claude Code sessions — completed work grouped by project (cwd), session costs from the pricing engine, tool invocations, error/compaction/APIError events, and turn velocity metrics from session metadata (turn_count, total_turn_duration_ms).

Configure, launch, validate, and troubleshoot CCAM's comprehensive MCP server for Claude Code, Codex, and other MCP hosts. Use when installing dependencies, building the server, selecting stdio, HTTP, or REPL transport, setting mutation/destructive policy, supplying dashboard authentication, or checking complete tool registration.

Inspect and install CCAM monitoring hooks for Claude Code and Codex. Use when onboarding a provider, repairing missing hooks, checking which provider is active, or validating that installation preserved unrelated user hooks.

Import Claude Code or Codex history and move complete CCAM datasets between machines. Use when rescanning provider history, importing a copied directory, uploading JSONL or archives, exporting a backup, restoring it idempotently, or verifying that tokens, workflows, runs, rules, and pricing survived.

Inspect and safely edit the Claude Code and Codex configuration surfaces exposed by CCAM. Use when auditing skills, agents, commands, plugins, marketplaces, MCP servers, hooks, settings, memory, keybindings, profiles, rules, or instruction files, and when a backup-backed allowlisted edit is required.

Configure and validate CCAM webhook targets across supported chat, incident, automation, and generic providers. Use when listing provider requirements, creating or updating a target, scoping it to alert rules, sending a test notification, reviewing delivery history, or deleting a target.

Configure and troubleshoot CCAM Remote Data Sources that collect Claude Code and Codex history over SSH. Use when adding, editing, testing, syncing, or removing a remote machine, verifying provider paths, or deciding whether to retain or purge imported sessions.

Inspect fired CCAM alerts and manage alert rules for token thresholds, event patterns, inactivity, and status duration. Use when acknowledging alerts, creating or editing a rule, checking cooldowns, or connecting alert rules to webhook targets.

Compare two sessions side-by-side using Agent Monitor data — per-model token usage (input/output/cache_read/cache_write + compaction baselines), pricing engine cost breakdowns, workflow intelligence (complexity scores, tool flow transitions, subagent effectiveness), session metadata (thinking_blocks, turn_count, turn_duration_ms, usage_extras), and full event timelines with all 10+ event types.

Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session. Splits history into an earlier baseline window and a recent window and reports which metrics are getting worse, by how much, and where. Use when checking whether things are degrading or trending in the wrong direction.

Detect recurring patterns using the Agent Monitor's workflow intelligence — toolFlow transitions (tool A → B frequency matrices), recurring workflow patterns, agent co-occurrence pairs, model delegation habits, error propagation paths by agent depth, and compaction triggers. Use to discover habitual usage patterns and anti-patterns.

Suggest concrete optimizations for Claude Code usage based on historical session data. Covers cost reduction, speed improvement, error prevention, and workflow efficiency. Use for data-driven optimization planning.

Benchmark one session (or a small recent set) against the rolling average using Agent Monitor data — cost, total tokens, tool count, and workflow complexity score — and report where each metric lands as a percentile of the population. Tells you whether a session was normal, cheap, or an outlier. Use when judging whether a session was typical or out of band.

Identify anomalous sessions using Agent Monitor data — cost outliers from the pricing engine, token anomalies (cache miss spikes, compaction baseline surges), unusual event type ratios (PreToolUse/PostToolUse gaps, APIError clusters), behavioral deviations from workflow intelligence (complexity score outliers, error propagation anomalies), and sessions with abnormal metadata (extreme turn_count, high thinking_blocks, zero turn_duration).

Search a Claude Code session transcript for a string or regex pattern and show every matching message with surrounding context. Reads /api/sessions/:id/transcript and resolves sessions via /api/sessions?limit= from the Agent Monitor dashboard. Use when hunting for a specific message, prompt, tool call, or error inside a session's conversation.

Debug a specific session by inspecting its full event chain (PreToolUse, PostToolUse, Stop, SubagentStop, Compaction, APIError, TurnDuration, Notification events), agent hierarchy (recursive parent/child tree with subagent_type and depth), token usage with compaction baselines, workflow intelligence data (orchestration DAG, error propagation by depth), and session metadata (thinking_blocks, turn_count, total_turn_duration_ms).

Diagnose Claude Code hook installation, delivery, and ingestion issues. Checks hook configuration, connectivity, event flow, and identifies common problems. Use when events are not appearing in the dashboard.

Trace the full event chain for one Claude Code session into an ordered timeline of every event type with tool_name and summary, highlighting gaps, out-of-order events, and failures. Reads /api/events?session_id= and /api/sessions/:id from the Agent Monitor dashboard. Use when debugging what a session actually did, step by step.

Polls the Agent Monitor /api/stats endpoint several times over a short window and reports the live deltas in active_sessions, active_agents, events_today, and ws_connections so you can see activity moving in real time. Use when watching the dashboard for live changes rather than a one-time snapshot.

Probes each major Agent Monitor API route — /api/stats, /api/analytics, /api/sessions, /api/pricing/cost, /api/workflows/runs, /api/cc-config/overview — and reports each one's HTTP status, latency, and response shape, flagging which are reachable. Use to verify a dashboard install is wired up correctly.

Quick dashboard health and status overview — checks the Agent Monitor API (port 4820), reports session/agent/event counts from /api/stats, confirms WebSocket connectivity, reads the redacted hook status returned by /api/settings/info, and shows data freshness (last event timestamp). Use to verify the monitoring system is operational.

Forecast Claude Code spend to the end of the week or month from the daily session trend on the Agent Monitor dashboard — moving average of daily spend × days remaining, added to spend-to-date. Uses /api/analytics daily_sessions, /api/pricing/cost, and /api/sessions for a per-day cost curve. Use when projecting cost or asking "where will my spend land".

Estimate the dollars saved by routing eligible Claude Code work to a cheaper model family, using the Agent Monitor pricing engine. Re-prices each model's token mix at the target family's rates and quantifies the delta. Uses /api/pricing (rates), /api/pricing/cost (current per-model spend), /api/sessions, and /api/analytics. Use when hunting for cost cuts or comparing model tiers.

Run a daily spend check-in against a daily budget on the Agent Monitor dashboard — today's spend vs the daily target, pace through the day, and the projected overage if the current pace holds. Uses /api/pricing/cost and /api/sessions (grouped by started_at = today). Use for a quick daily spend check-in or a morning/evening budget pulse.

Review the configured cost alert rules and the alerts currently fired on the Agent Monitor dashboard, then explain exactly what tripped and why. Uses /api/alerts (fired feed) and /api/alerts/rules (definitions). Use when checking spend alerts or asking why a cost alarm went off.

Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.

Inventory the installed skills and which plugins contribute them, then flag overlap with the user's own skills — read through the Agent Monitor Config Explorer API. Reads /api/cc-config/skills and /api/cc-config/plugins. Use when managing skills: deduping, deciding what to keep, or tracing a skill back to the plugin that ships it.

Inventory hooks across the user, project, and project-local settings plus the ~/.claude/hooks scripts directory — read through the Agent Monitor Config Explorer API — and flag hooks that POST to the network or run arbitrary commands. Reads /api/cc-config/hooks and /api/cc-config/hook-scripts. Use when auditing hook safety.

Run a full audit of the user's Claude Code configuration via the Agent Monitor Config Explorer API: counts per surface (user vs project), duplicate or overlapping skills and subagents, hooks that run shell commands, and which surfaces are read-only vs mutable. Reads /api/cc-config/overview, /skills, /agents, /commands, /hooks, and /settings. Use when reviewing your Claude Code setup for sprawl, duplication, or risk.

Analyze Claude Code usage trends over time using the Agent Monitor's analytics API — daily session counts, daily event counts, token volumes by type, model distribution, tool usage rankings, and agent/event type distributions across 365-day retention windows.

Generate a comprehensive session report with per-model token usage (input, output, cache_read, cache_write including compaction baselines), cost breakdown via the pricing engine, tool invocations, agent hierarchy, compaction events, API errors, turn durations, and thinking block counts. Use when reviewing a specific session or summarizing activity over a date range.

Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.

Break down Claude Code usage by model family (Opus / Sonnet / Haiku) from the Agent Monitor dashboard — each family's share of tokens, share of cost, and the spots where an expensive model is doing cheap work. Pulls per-model token and cost splits from /api/pricing/cost, current rates from /api/pricing, fleet token totals from /api/analytics, and per-session model assignment from /api/sessions. Use when deciding model routing or whether to downshift work to a cheaper tier.

Break down Claude Code costs using the Agent Monitor pricing engine. Shows per-model costs (input, output, cache_read, cache_write at $/Mtok rates), per-session costs, daily trends, and compaction baseline token recovery. Use when analyzing spending, comparing model costs, or planning budgets.

Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.

Implement a feature safely end-to-end in this repository. Use when adding or changing functionality across backend, frontend, or MCP with required verification and documentation updates.

Operate and maintain the local MCP server for this project. Use when creating MCP host config, troubleshooting tool connectivity, modifying tool domains, or adjusting safety policy flags.

抖音视频 → 下载 → 转录 → 存为 Markdown 的完整工作流。 支持短链接和完整链接；优先无 cookie 直采，被风控时回退 yt-dlp + 浏览器 cookie（无需登录）。

内容加工：给任意采集产物（转录稿 / 文章 / 笔记）自动补「摘要 + 要点 + 标签 + 价值判断」， 写进 frontmatter 并在正文顶部插入摘要块。是「加工层」通用能力，惠及仓库所有采集 skill。

哔哩哔哩视频 → 下载 → 转录 → 存为 Markdown 的完整工作流。 支持 BV 号和完整链接，无需登录。

Local vision CLIs: glance (describe/ask/OCR an image), ground (locate a target, pixel box), detect (element inventory), trace (image to SVG geometry), crop (cut a pixel box to a file), and scripts/html_shot.py (HTML file to image). Use for any task involving an image — questions, text, splitting and transcribing long screenshots or chat histories, locating elements, comparing, rebuilding as HTML/SVG, digitizing a sketch or diagram, reading values off a chart, operating a GUI from screenshots — and to re-check an image yourself when a description you were given lacks a detail.

Drafting and refining academic rebuttals for top-tier AI/CS conferences (NeurIPS, ICML, ICLR, CVPR, ECCV, AAAI, ARR, KDD, UAI, AISTATS, TMLR, etc.). Use this skill whenever the user needs to respond to reviewer comments, write a rebuttal, handle reviewer feedback, clarify technical misunderstandings, present additional experimental results, or deal with borderline accept/reject decisions. Also trigger when the user mentions keywords like "rebuttal", "reviewer", "review response", "author response", "camera-ready", "rebut", "AC", "area chair", "meta-review", or discusses conference review scores. Trigger for Chinese-language requests too, e.g. "写rebuttal", "回复审稿人", "审稿意见", "rebuttal怎么写", "reviewer说我的baseline不够".

Perform deep, multi-source research using Google Gemini's Deep Research Agent. Use this skill whenever the user asks for comprehensive research, literature reviews, competitive analysis, market research, technology surveys, or any investigation that requires synthesizing information from many web sources. Also trigger when the user says "deep research", "research this thoroughly", "do a comprehensive study on", or wants a structured report with evidence gathered from across the web — even if they don't mention Gemini by name.

Use when building reusable Obsidian glossary notes from an existing paper source manifest, optionally with a raw-sections override, especially when a reader needs a reviewed shortlist before glossary notes and article links are changed.

Generate a high-quality deep-reading note for a single paper and write it into an Obsidian-style vault. Use when the user gives a paper title, DOI, URL, arXiv ID, Zotero item, or local PDF and wants a polished Markdown note with strong structure, evidence-based analysis, and figure placeholders.

Analyze, standardize, validate, and sync locally maintained skills into agent skill directories with a `skills` CLI-aligned workflow. Use this skill when Codex needs to turn ad-hoc prompt or rules folders into reusable `SKILL.md`-based skills, install or sync one or more local skills from `./skills` into Claude, Cursor, CodeBuddy, Codex, or similar agent directories, or manage local skill path mappings and symlink or copy installation behavior.

Direct, generate, edit, compare, and review visual assets with current Google Gemini image models. Use for image creation, image editing, reference-based consistency, product and character visuals, text-bearing graphics, grounded diagrams, video-derived images, and multi-model image portfolios.

Flutter Web版をビルドしてユーザーにプレビューURLを案内する。playwright-cliでアクセス確認も行う。

ccpocketのストア掲載用スクリーンショットや説明文・リリースノートを作成・更新するときに使う。

iOSシミュレーターで動くFlutterアプリをExpo Device HubとTailscale HTTPSでスマホのブラウザへ配信・操作するときに使う。

MANDATORY for every coding agent (Claude Code, Codex, or any other) — keep this repository's documentation in sync after any change to behavior, configuration, interfaces, events, schema, or features. Use automatically at the end of change-sets that alter an env var, event type, hook or state behavior, API, schema, WebSocket message, MCP tool, CLI command, or user-facing feature, and whenever the user asks to update documentation.

Autonomous Goal-directed Iteration. Apply Karpathy's autoresearch principles to ANY task. Loops autonomously — modify, verify, keep/discard, repeat. 9 subcommands: plan, debug, fix, security, ship, scenario, predict, learn.

deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.

Reusable git delivery workflows derived from local slash commands (commit, push, PR, release notes, GitHub Actions failure triage with worktree-based fixes, and isolating your own changes from a shared dirty workspace).

Conduct a retrospective on a coding session.

Create, amend, or backprop bugs into SPEC.md at repo root. Sole mutator of the project spec. Triggers when the user asks to write a spec, start a new spec, distill a spec from existing code, add invariants, amend sections (§G, §C, §I, §V, §T, §B), or record a bug via backprop. Common phrasings: "write the spec for...", "new spec", "bug: ...", "amend §V.3", "distill spec from code", "spec this idea". Reads and follows FORMAT.md for the caveman encoding rules and pipe-table shape of §T and §B.

Adversarial senior review of the spec before any code is written. Constructs a skeptical reviewer whose authority comes from the codebase, §R research, and live best-practice — then tries to REFUTE the spec, not rubber-stamp it. Every finding cites evidence (file:line or source); unverifiable ones are flagged. Survivors harden §V; the run ends in an explicit go / no-go gate. Triggers before building anything high-blast-radius, when the user says "review the spec", "red-team this", "is this plan sound", "senior review", or invokes /ck:review.

Gather external knowledge the spec needs and distill it into §R — the durable research log — so build grounds in facts instead of hallucinating library behavior. Each finding cites a source; unsourced claims are flagged, never written as fact. Triggers when a spec decision hinges on a library/API/best practice the agent is unsure of, when the user says "research this", "what's the best lib for…", "check current best practice", or invokes /ck:research. Defers the §R write to the spec skill.

Read-only drift detector. Diffs SPEC.md against current code and reports violations grouped by severity. Writes nothing — suggests remedies via the spec or build skills but never invokes them. Triggers when the user asks to check drift, audit the spec, verify invariants, or ask whether code still matches the spec. Phrasings: "check drift", "audit the spec", "does the code still match §V", "check invariants", "spec vs code".