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cc-canary抄送金丝雀

Agent Skill

cc-canary 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

420

周安装

17

GitHub Stars

53

下载量

132
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:cc-canary(抄送金丝雀)
来源仓库:https://github.com/delta-hq/cc-canary
仓库路径:skills/cc-canary
安装命令:
npx skills add https://github.com/delta-hq/cc-canary --skill cc-canary
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/delta-hq/cc-canary --skill cc-canary

简介

分析 Claude Code 版本变更的信号与回归问题。

  • 适用于模型性能监控与异常波动诊断场景。cc-canary 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 基于 session 数据生成结构化归因报告。
  • 严禁直接读取会话日志文件,依赖脚本解析。
  • 建议结合人工判断避免误判混杂因素影响。

SKILL.md

cc-canary — long-form regression writeup

Primary question: has Claude regressed on this user's work, and when?

Bundled script scripts/compute_stats.py does ~95% of the work in ~2.5s: scans JSONLs, runs inflection + transition-day detection, builds pre/post aggregates, cross-version comparison, hour-of-day, word frequency, three- period thinking depth, visibility transition, per-turn rates, and abnormalities — then renders a complete markdown skeleton with every table filled. Narrative slots are marked <!-- C:... -->.

Default window: 60d. Accept 7d / 14d / 30d / 60d / 90d / 180d.

Framing — three-bucket classification

  1. Model-side — same user/task, worse outcomes; cross-version worse; reasoning-depth dropping
  2. User-side — project-mix shift; shorter/imperative prompts; new codebase; shortcut vocab rising
  3. Ambiguous — mixed confounds, borderline effect size, either-way-explainable

Your 3-step job

1. Run the script

Bash(python3 <SKILL_DIR>/scripts/compute_stats.py --window {window} --render-md /tmp/cc-canary-skeleton-{window}.md > /dev/null 2>&1)

<SKILL_DIR>: .claude/skills/cc-canary/ → fallback to ~/.claude/skills/cc-canary/.

Flags: --window {Nd} (required); --include-agents (include subagent sessions); --min-user-words N (default 10).

If the script fails: report error, retry once with --include-agents, else stop. Never fall back to hand-computation — that's the slow path.

2. Read the skeleton

Read /tmp/cc-canary-skeleton-{window}.md

3. Fill every <!-- C:... --> placeholder and save

Write ./cc-canary-{YYYY-MM-DD}.md

End your message with the absolute path: Wrote /Users/.../cc-canary-{date}.md · paste-ready.

Narrative placeholders

Each placeholder's inline comment already spells out what to write. Summary:

  • verdict-line — HOLDING / SUSPECTED REGRESSION / CONFIRMED REGRESSION / INCONCLUSIVE + brief justification
  • summary — 2–4 sentences: verdict + inflection + biggest pre→post delta. No counter-evidence hedging.
  • timeline — 1–2 paragraphs on the daily series shape
  • xv-para — 1 paragraph on cross-version (if §3 present)
  • finding-N-class × up to 5 — inline classification label
  • finding-N-reason × up to 5 — 2–3 sentences max, evidence-first. Lead with the strongest number (cross-version Δ, §2 value, appendix rate). No signal-line restating, no rhetorical buildup
  • root-cause — 3–5 paragraphs tying strongest signals together
  • what-would-help — 2–4 concrete bullets
  • appendix-a1…a4, b, c, d, e, f, g, h — 1 paragraph each (see table context in skeleton)
  • meta-note — 2–5 sentences, first person, honest, no claimed feelings, acknowledge the recursion

Verdict calibration

  • HOLDING: ≤1 model-side signal
  • SUSPECTED REGRESSION: 2–3 model-side signals
  • CONFIRMED REGRESSION: ≥3 model-side signals + non-empty cross-version showing decline + session_count ≥ 15 + ≥2 models + inflection.gap_sigma ≥ 1.0
  • INCONCLUSIVE: session_count < 15 OR inflection.method == "fallback_split_half" with overlapping confounds

Cap at SUSPECTED when: only one model; <15 sessions; single-project with project starting mid-window; inflection coincides with a visible user-side event.

All the data you need (session_count, model mix, inflection method, cross-version presence) is rendered as plain text in the skeleton.

Grounding example (for finding-N-reason)

Classification: model-side. Read:Edit dropped 9.0 → 1.0 (-89%, concerning) while cross-version shows opus-4-7 at 0.39 vs opus-4-6 at 1.00 on the same user's workload. No project-mix shift near the inflection — model is defaulting to edit-first.

Hard rules

  • Never read, grep, or glob ~/.claude/projects/**/*.jsonl. Never run jq/awk/wc on session files. Script owns all that.
  • Never touch tables or numbers — they came from real data.
  • Every finding gets a classification label.
  • Hedge when cross-version is empty or session_count < 15.
  • Do not verdict CONFIRMED REGRESSION without the full checklist.
  • Do not save the skeleton as-is — replace every <!-- C:... --> first.

Failure modes

  • Script import error → check python3 -V ≥ 3.8; retry once with --include-agents; else stop.
  • Skeleton < 5KB → likely no sessions in window. Check script error.
  • inflection.method == fallback_split_half → state it; cap at SUSPECTED.
  • Cross-version Δ None → div-by-zero when model-A value is 0; note the confound.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.03%
按下载量换算46

Claude

30.98%
按下载量换算41

Cursor

18.29%
按下载量换算24

Gemini CLI

8.47%
按下载量换算11

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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