cheat-status

A project progress dashboard that reads the current state and scans relevant files to show what work remains. It is designed for a prediction-and-review project.

In plain words
What is it for?
Use it to inspect the state file, prediction files, candidate list, scoring notes, and usage log, then produce a list of what to do today.
Why use it?
It replaces manual checking of several files with one summary of progress, pending reviews, stale work, and possible maintenance tasks.

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/xbuilderlab/cheat-on-content/cheat-status
Any agent
npx skills add XBuilderLAB/cheat-on-content --skill cheat-status
Clone the repo
git clone --depth 1 https://github.com/XBuilderLAB/cheat-on-content

Made for: Claude Code, Codex.

Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,804 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 $0.00082 $0.02804
Opus 5 $0.00041 $0.01402
Sonnet 5 $0.00016 $0.00561
Haiku 4.5 $0.00008 $0.00280

Measured 3d ago against content hash b86f6ff45fe3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

cheat-status 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 3d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/cheat-status/SKILL.md · 163 lines

How it starts

The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/cheat-status — 状态看板

读 state file + 扫描用户项目 → 汇总当前进度 → 输出"今天该做什么"清单。

Overview

[用户:状态]
  ↓
[Phase 1: 读 .cheat-state.json + 扫文件系统]
  ↓
[Phase 2: 计算派生指标]
  ↓
[Phase 3: 检测建议触发器(升级 / bump / 清算)]
  ↓
[Phase 4: 输出看板]

Constants

  • SQLITE_UPGRADE_THRESHOLD = 30 — calibration_samples 达到 N 时建议升 SQLite
  • CLEANUP_LINE_THRESHOLD = 600 — rubric_notes.md 行数超 N 时建议清算
  • STALE_PREDICTION_DAYS = 30 — in_progress prediction 超 N 天未发布提示清理

Inputs

来源 用途
.cheat-state.json 主要状态
predictions/*.md 校准样本数 / pending retros
candidates.md 候选池规模
rubric_notes.md 行数 / 当前版本
.cheat-cache/usage.jsonl(如有) meta-logging 数据,用于"距上次 bump 多少次预测"

Workflow

Phase 1: 读状态

state = read_json('.cheat-state.json')
if not state:
    return "你还没初始化。请先跑 /cheat-init。"

predictions = glob('predictions/*.md')
candidates_count = parse_candidates_md_entries()
rubric_lines = wc -l rubric_notes.md

Phase 2: 派生指标

指标 算法
Buffer 数 len(state.shoots)
Buffer 颜色 cadence-protocol.md 派生:buffer_days = buffer_count × target_publish_cadence_days<1 红 / 1-2 橙 / 3-5 绿 / >5 蓝。如 target_publish_cadence_days=null → 颜色禁用
Confidence 等级 state-management.md confidence 表 派生:从 calibration_samples 整数派生 emoji + 标签
最早一拍至今天数 now - state.shoots[0].shot_at,用于警告"拍了 N 天没发"
校准样本数 predictions 中含完整复盘段(实绩数据非空)的文件数
待复盘 state.pending_retros 中已过 RETRO_WINDOW_DAYS 的
池大小 candidates.md 中 tier!=skip 的 entry 数
上次 bump 至今几次预测 predictions 中 published_at > state.last_bump_at 的数量
同向偏差队列 state.consecutive_directional_errors
in_progress 陈旧度 now - state.in_progress_session.started_at(如有)

Phase 3: 检测建议触发器

按优先级(高→低)逐项检查:

  1. Buffer 颜色 = 🔴 红 → 第一行高优先级警戒:"buffer 已 0/1 篇,下个发布日可能断更——今天必须拍 ≥1 条。说'推荐选题'我只推 top 1 稳分(不推实验性)"
  2. Buffer 颜色 = 🔵 蓝 → 高优先级提示:"buffer 已 N 篇积压。暂停拍摄,先发存货 + 复盘。说'已发布 ...'我帮你出队"
  3. state.shoots 中最早一项 shot_at > 14 天 → "你有视频拍了 N 天还没发——议题时效流失风险,建议尽快发或弃稿"
  4. in_progress 陈旧 (>= STALE_PREDICTION_DAYS) → 高优先级提示"清理或 publish"
  5. 待复盘 ≥ 1 → 高优先级"今天该复盘 X 篇"
  6. pool_status=none + calibration_samples=0 + 距 init >24h → "🌱 你 init 完已经 N 天但还没拍——是因为没选题吗?跑 /cheat-seed 5 分钟拿 5 个候选 + 5 个 draft" 高优先级
  7. Claude 判断系统性偏差信号不是死磕 ≥3 同向) → 提示"建议跑 /cheat-bump"
    • 默认参考:连续 ≥3 次同向偏差
    • 但 Claude 可以更早:1 次极端偏差(≥10x)或 2 次同向 + 评论区强反向证据
    • 也可以更晚:3 次同向但每次幅度都 <25%(可能只是噪声)
    • 提示时显式标注:"本次是 [default-aligned] / [judgment-driven]"
  8. calibration_samples 跨入新 confidence 等级(0→1, 2→3, 5→6, 10→11, 20→21)→ 提示"🎉 confidence 升级:<旧等级> → <新等级>。bucket 中枢精度从 ±X% 提到 ±Y%"。仅作通知,无任何用户必须确认的操作——所有 skill 都已经按 calibration_samples 自动调整
  9. calibration_samples 跨过 5 → "你的 rubric 形态可以第一次正式 bump 了。回顾 rubric_notes.md 看观察记录段是否有 ≥3 样本支持的 pattern → 跑 /cheat-bump"
  10. calibration_samples 跨过 10 → "可以跑 /cheat-bump --bucket-only --scheme percentile 让 bucket 边界改用 percentile(永远自洽)"
  11. calibration_samples 跨过 SQLITE_UPGRADE_THRESHOLD 且 data_layer=markdown → "建议跑 tools/md-to-sqlite.py"(planned — batch 3, not yet available)
  12. rubric_notes.md 行数 > CLEANUP_LINE_THRESHOLD → "建议清算观察段(手动或下次 bump 触发)"
  13. calibration_samples ≥ 5 + pool_status=none → "可以开始建立选题池了"
  14. calibration_samples ≥ 15 + pool_status=none → "强烈建议建池:/cheat-trends 或手动建 candidates.md"
  15. state.hooks_installed=false → "你的 immutability 是君子协定,建议跑 /cheat-init 装 hook"
  16. state.last_bump_self_audited=true → "上次 bump 是自审。建议配置 mcp__llm-chat__chat 后下次 bump 走外部审"
  17. state.rubric_form_mismatch=true → "你的 content_form 不是 opinion-video,用了内置观点 rubric。前几篇预测会更不准,下次 bump 时建议自行调整权重适配你的形态"
  18. state.benchmark_status=pending → "🎯 你 init 时答应等下找对标账号但还没找。跑 /cheat-learn-from 导入 ≥3 条对标视频,工具就有 anchor 了"
  19. state.benchmark_status=imported + Claude 判断用户数据信号已超过 benchmark → "📊 你的真实数据已经成为主信号,benchmark 影响淡出"
  • 默认参考:calibration_samples ≥ 10
  • 但 Claude 可以更早:N=5 但用户的 (打分, 实绩) 配对里出现 ≥3 条与 benchmark pattern 不一致的——说明你的账号已经走出对标的路径
  • 也可以更晚:N=15 但用户的样本都很相似,没足够多样性 → benchmark 仍有信号价值
  • 提示是通知不是 gate——benchmark.md 永远保留作 sanity check,cheat-seed 仍可读

Read the full file on GitHub · 163 lines

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. 3d ago First seen · 163 lines · 82 tokens per session scan A b86f6ff45fe3

Subscribe to this mod's changes

cheat-status is a skill published in the GitHub repository XBuilderLAB/cheat-on-content (6,715 stars, last pushed 2d ago), licensed MIT. It adds 82 tokens to every session and 2,804 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 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens

chat-perf

Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.

microsoft/vscode · 51 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens