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post-session会后

Agent Skill

post-session 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

1,273

周安装

52

GitHub Stars

37

下载量

408
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:post-session(会后)
来源仓库:https://github.com/terrylica/cc-skills
仓库路径:skills/post-session
安装命令:
npx skills add https://github.com/terrylica/cc-skills --skill post-session
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/terrylica/cc-skills --skill post-session

简介

用于查找、检索和筛选相关信息以支持会后跟进。

  • 适合根据关键词或任务场景快速定位候选结果。
  • 可辅助整理会议纪要、识别待办事项或推荐后续行动。
  • 安装前建议确认权限范围和维护状态。post-session 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 注意是否会触发联网、命令执行或文件读写操作。

SKILL.md

/asciinema-tools:post-session

Complete post-session workflow: finalize orphaned recordings → convert to text → AI-powered summarize.

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

Arguments

ArgumentDescription
filePath to.cast file (or auto-detect)
--finalizeInclude finalize step (stop processes, compress)
-q, --quickQuick analysis (keyword grep + brief summary)
-f, --fullFull analysis (convert + AI deep-dive summarize)
--summarizeInclude AI summarize step (iterative deep-dive)
--outputSave findings to markdown file

Workflow Modes

Quick Mode (-q)

[file] → convert → keyword grep → brief summary

Full Mode (-f)

[file] → convert → AI summarize (iterative deep-dive)

Complete Mode (--finalize --full)

stop processes → compress → push → convert → AI summarize

Execution

Phase 1: Discovery

AskUserQuestion:
  question: "What would you like to do?"
  header: "Workflow"
  options:
    - label: "Quick analysis (Recommended)"
      description: "Convert + keyword search + brief summary"
    - label: "Full AI analysis"
      description: "Convert + iterative AI deep-dive with guidance"
    - label: "Complete workflow"
      description: "Finalize orphans + convert + AI summarize"
    - label: "Finalize only"
      description: "Stop processes and push to orphan branch"

Phase 2: File Selection

If no file specified, discover available recordings:

/usr/bin/env bash << 'DISCOVER_EOF'
echo "=== Running asciinema processes ==="
ps aux | grep -E "asciinema rec" | grep -v grep | while read -r line; do
  PID=$(echo "$line" | awk '{print $2}')
  CAST=$(echo "$line" | grep -oE '[^ ]+\.cast' | head -1)
  if [[ -n "$CAST" ]]; then
    SIZE=$(ls -lh "$CAST" 2>/dev/null | awk '{print $5}' || echo "?")
    echo "  [RUNNING] PID $PID: $CAST ($SIZE)"
  fi
done

echo ""
echo "=== Recent .cast files ==="
find ~/eon -name "*.cast" -size +1M -mtime -7 2>/dev/null | while read -r f; do
  SIZE=$(ls -lh "$f" | awk '{print $5}')
  MTIME=$(stat -f "%Sm" -t "%m-%d %H:%M" "$f" 2>/dev/null)
  echo "  $f ($SIZE, $MTIME)"
done | head -10

echo ""
echo "=== Recent .txt files (already converted) ==="
find ~/eon -name "*.txt" -size +100M -mtime -7 2>/dev/null | while read -r f; do
  SIZE=$(ls -lh "$f" | awk '{print $5}')
  echo "  $f ($SIZE)"
done | head -5
DISCOVER_EOF
AskUserQuestion:
  question: "Which recording to analyze?"
  header: "Select"
  options:
    # Dynamically populated from discovery
    - label: "{filename} ({size})"
      description: "{path}"

Phase 3: Finalize (if selected)

Chain to /asciinema-tools:finalize:

  1. Stop running asciinema processes
  2. Verify file integrity
  3. Compress with zstd
  4. Push to orphan branch

Phase 4: Convert

/usr/bin/env bash << 'CONVERT_EOF'
CAST_FILE="$1"
TXT_FILE="${CAST_FILE%.cast}.txt"

echo "Converting: $CAST_FILE"
echo "Output: $TXT_FILE"

if asciinema convert -f txt "$CAST_FILE" "$TXT_FILE"; then
  ORIG=$(ls -lh "$CAST_FILE" | awk '{print $5}')
  CONV=$(ls -lh "$TXT_FILE" | awk '{print $5}')
  echo "✓ Converted: $ORIG → $CONV"
else
  echo "✗ Conversion failed"
  exit 1
fi
CONVERT_EOF

Phase 5: Analysis

Quick mode: Keyword grep + brief summary

# Run curated keyword searches
grep -c -i "error\|fail\|exception" "$TXT_FILE"
grep -c -i "success\|complete\|done" "$TXT_FILE"
grep -c -i "sharpe\|drawdown\|backtest" "$TXT_FILE"
# ... summarize counts

Full mode: Chain to /asciinema-tools:summarize

  • Initial guidance via AskUserQuestion
  • Strategic sampling (head/middle/tail)
  • Iterative deep-dive with user guidance
  • Synthesis into findings report

Phase 6: Output

AskUserQuestion:
  question: "Analysis complete. What next?"
  header: "Output"
  options:
    - label: "Display summary"
      description: "Show findings in terminal"
    - label: "Save to markdown"
      description: "Write findings to {filename}_findings.md"
    - label: "Continue exploring"
      description: "Deep-dive into specific sections"
    - label: "Done"
      description: "Exit workflow"

Example Usage

# Interactive mode - auto-detect and guide
/asciinema-tools:post-session

# Quick analysis on specific file
/asciinema-tools:post-session session.cast -q

# Full AI analysis with output
/asciinema-tools:post-session session.cast -f --output findings.md

# Complete workflow including finalize
/asciinema-tools:post-session --finalize -f

Related Commands

  • /asciinema-tools:daemon-status - View status and find unhandled files
  • /asciinema-tools:finalize - Finalize orphaned recordings
  • /asciinema-tools:convert - Convert.cast to.txt
  • /asciinema-tools:summarize - AI-powered deep analysis
  • /asciinema-tools:analyze - Keyword-based analysis

Troubleshooting

IssueCauseSolution
No recordings foundNo.cast files in ~/eonCheck recording directory with find ~/eon
File discovery emptyWrong search pathManually specify file path as argument
Convert failsCorrupted.cast fileRun /asciinema-tools:finalize first
AI summarize timeoutRecording too largeUse -q for quick analysis first
Orphan branch push failsGit authentication issueCheck GitHub token with echo $GH_TOKEN

Post-Execution Reflection

After this skill completes, reflect before closing the task:

  1. Locate yourself. — Find this SKILL.md's canonical path before editing.
  2. What failed? — Fix the instruction that caused it.
  3. What worked better than expected? — Promote to recommended practice.
  4. What drifted? — Fix any script, reference, or dependency that no longer matches reality.
  5. Log it. — Evolution-log entry with trigger, fix, and evidence.

Do NOT defer. The next invocation inherits whatever you leave behind.

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

36.86%
按下载量换算150

Claude

27.76%
按下载量换算113

Cursor

19.96%
按下载量换算81

Gemini CLI

10.05%
按下载量换算41

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/terrylica/cc-skills --skill post-session 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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