Blind Spot Scan

What it does

When your agent uses it

• Asking what thinking blind spots showed up in yesterday's AI conversations
• Wanting a targeted book chapter to correct a recurring thinking habit
• Scanning a specific past date for blind spots

Example prompts

• “Scan my blind spots from yesterday's conversations.”
• “Run the blind spot scan for last Friday's conversations and pick a WeRead chapter.”
• “What did I leave unexamined in yesterday's chats with the AI?”

Requirements

• A WeRead API key in `WEREAD_API_KEY`
• Local session files of the previous day's AI conversations
• Network access to the WeRead API

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1绕开点——一个难题他打开了，又飞快合上。证据：问题抛出，转头换话题，或一句"这个先不管"滑过去。
2. 2空转框架——反复换视角切同一个问题，没有一个落地。证据：一个问题被套上三四副框架，每副都浅尝辄止，最后没结论。
3. 3单一取景框——一整天只调用一两副 f（逢事就"约束""进化""博弈"），没试过别的镜子。这是他最隐蔽的盲区：越趁手的 f，越挡住别的 f 能照见的东西。（继刚看世界 = f(x)，四轴是他默认的四副 f；正因为默认，才最该查他今天是不是又只用了其中一副。）
4. 4未检前提——一个默认假设贯穿全程，他当硬约束在用，从没问过它是不是假墙。证据：某个"本来就该这样"的说法，反复出现却从未被他自己质疑。
5. 5相邻空缺——从他问的东西反推，本该问、却没问的那个角落。不是凭空猜，是他自己的线索指向那儿。

What it can do on your machine

Read from SKILL.md and the folder at commit 9e75497. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (TypeScript), which the agent can run.

  Shell commands in SKILL.md call:

  • jq
  • curl
  • bun

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • weread.qq.com
  • i.weread.qq.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • WEREAD_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Blind Spot Scan loads about 1.7k tokens when it runs. Until then it costs about 111 tokens; SKILL.md has 225 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~111

When it runs · the whole SKILL.md, loaded when a task matches

~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from lijigang/ljg-skills at commit 9e75497, republished under its MIT licence (© lijigang). 225 words, ~1,743 tokens.

name

ljg-blind

description

盲区扫描——读昨天你与 AI 的全部对话，照出暴露的思维盲区（不是不懂的知识，是让某类真相一直看不见的思维习惯），再从微信读书挑一本书的一章精准补上，落成一篇完整分析笔记。Use when user says '扫盲区', '盲区', '照盲区', '看看我的思维盲区', '我昨天想漏了什么', 'blind spot', 'ljg-blind', '/ljg-blind', or wants yesterday's AI conversations analyzed for cognitive blind spots plus a targeted weread chapter to fill them. 可选参数：传一个日期(YYYY-MM-DD)扫那天而非昨天。NOT FOR 纵向深钻一个观点（用 ljg-think）、一个领域降秩（用 ljg-rank）、陪读一篇文本（用 ljg-read）、找一个领域的约束（用 ljg-constraint）。

user_invocable

true

# 默认昨天；用户传了 YYYY-MM-DD 就用那天（macOS BSD date）
target=${1:-$(date -v-1d +%Y-%m-%d)}

target=<上一步的日期>
out=/tmp/ljg-blind-${target}.txt
: > "$out"
previous_dir=$(date -j -v-1d -f '%Y-%m-%d' "$target" '+%Y/%m/%d')
target_dir=$(date -j -f '%Y-%m-%d' "$target" '+%Y/%m/%d')

for day_dir in "$previous_dir" "$target_dir"; do
  session_dir="$HOME/.codex/sessions/$day_dir"
  [ -d "$session_dir" ] || continue
  find "$session_dir" -type f -name '*.jsonl' -print0
done | while IFS= read -r -d '' session_file; do
  # Codex 将子 agent 与顶层 task 放在同一日期目录；从 session_meta 排除它们。
  if jq -e 'select(.type=="session_meta") |
      (.payload.source | type == "object" and has("subagent"))' \
      "$session_file" >/dev/null 2>&1; then
    continue
  fi

  jq -r --arg target "$target" '
    def local_epoch:
      sub("\\.[0-9]+Z$"; "Z") | fromdateiso8601;
    select(.type=="response_item"
      and .payload.type=="message"
      and .payload.role=="user")
    | (.timestamp | local_epoch) as $epoch
    | select(($epoch | strflocaltime("%Y-%m-%d")) == $target)
    | ([.payload.content[]?
        | select(.type=="input_text")
        | .text] | join("\n")) as $text
    | select($text | length > 0)
    | select(($text | test("^(<recommended_plugins>|<environment_context>|<task-notification>)")) | not)
    | select(($text | test("(?i)(ljg-blind|扫盲区)")) | not)
    | "[" + ($epoch | strflocaltime("%H:%M")) + "] " + $text
  ' "$session_file" 2>/dev/null >> "$out"
done

wc -m "$out"

curl -s -X POST https://i.weread.qq.com/api/agent/gateway \
  -H "Authorization: Bearer $WEREAD_API_KEY" -H "Content-Type: application/json" \
  -d '{"api_name":"/store/search","keyword":"<核心词>","count":8,"skill_version":"1.0.3"}' \
  | jq -r '.results[].books[]?.bookInfo | "\(.bookId)\t\(.title)\t\(.author)\t评分\(.newRating)"'

curl -s -X POST https://i.weread.qq.com/api/agent/gateway \
  -H "Authorization: Bearer $WEREAD_API_KEY" -H "Content-Type: application/json" \
  -d '{"api_name":"/book/chapterinfo","bookId":"<bookId>","skill_version":"1.0.3"}' \
  | jq -r '.chapters[] | select(.level<=2) | "\(.chapterUid)\t[\(.level)] \(.title)\t\(.wordCount)字"'

bun "${HOME}/.agents/skills/ljg-blind/Tools/WeReadWebUrl.ts" "<bookId>" "<chapterUid>"

https://weread.qq.com/web/reader/{encodedBookId}k{encodedChapterUid}

bun "${HOME}/.agents/skills/ljg-blind/Tools/WeReadWebUrl.ts" "<bookId>"

#+title: 盲区扫描 · {一句话点出这个盲区}
#+date: [YYYY-MM-DD Weekday HH:MM]
#+filetags: :blind:weread:topology:

* 昨天你在想什么
<1-2 段。当天对话的思维地形——哪几件事、绕着哪个核心在转。给证据：哪几句话看出来的。不流水账，抓主线。>

* 照出来的盲区
<挑出的那 1 个盲区。先一句话点破它是哪种（绕开点 / 空转框架 / 单一取景框 / 未检前提 / 相邻空缺）。再 2-3 段说清：它具体长什么样、昨天哪几处暴露了它、为什么你自己看不见。要点到杠杆——补上之后会打开什么。>

* 这一章给你
- 书：《书名》 — 作者
- 章节：<章节标题>
- 时长：约 N 分钟
- 链接：[[https://weread.qq.com/web/reader/ENCODED_BOOKkENCODED_CHAPTER][在微信读书网页版阅读本章]]
- 为什么是它：<3-4 句。把盲区和这一章对上——这章具体讲什么、怎么补这个盲区。不要泛泛说"开阔视野"，要说清这一章的哪个东西正对这个盲区的哪个缺口。>

© lijigang, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in skills/ljg-blind of lijigang/ljg-skills.

• SKILL.md
• Tools/WeReadWebUrl.help.md
• Tools/WeReadWebUrl.ts

Open the folder on GitHubat commit 9e75497