session-discovery — for Claude Code session-discovery, community, for Claude Code, ide skills, session discovery, local conversation sessions, bun script execution, JSON manifest generation, date-based filtering, project-based filtering, Claude Code

v1.0.0

关于此技能

Ideal for AI Coding Assistants needing efficient conversation session management and analysis. Session-discovery is an AI agent skill that helps developers find and manage local Claude Code conversation sessions, enhancing productivity in AI coding workflows.

功能特性

Run bun script for session discovery
Filter sessions by date using --since and --until flags
Filter sessions by project path substring using --project flag
Return JSON manifest with session metadata
Parse output for session details and diagnostics

# 核心主题

hartphoenix hartphoenix
[12]
[2]
更新于: 3/16/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 10/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
10/11
Quality Score
57
Canonical Locale
en
Detected Body Locale
en

Ideal for AI Coding Assistants needing efficient conversation session management and analysis. Session-discovery is an AI agent skill that helps developers find and manage local Claude Code conversation sessions, enhancing productivity in AI coding workflows.

核心价值

Empowers agents to discover and manage local Claude Code conversation sessions, enhancing productivity in AI coding workflows through JSON manifests and filtering by project path substrings, leveraging flags like --since and --until for date window control.

适用 Agent 类型

Ideal for AI Coding Assistants needing efficient conversation session management and analysis.

赋予的主要能力 · session-discovery

Automating session discovery for efficient coding workflows
Filtering conversation sessions by project or date for targeted analysis
Generating JSON manifests for further processing or integration with other AI tools

! 使用限制与门槛

  • Requires local Claude Code conversation sessions
  • Needs access to the harness root and scripts directory
  • Limited to sessions stored on the local machine

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

session-discovery 是什么?

Ideal for AI Coding Assistants needing efficient conversation session management and analysis. Session-discovery is an AI agent skill that helps developers find and manage local Claude Code conversation sessions, enhancing productivity in AI coding workflows.

如何安装 session-discovery?

运行命令:npx killer-skills add hartphoenix/weft/session-discovery。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

session-discovery 适用于哪些场景?

典型场景包括:Automating session discovery for efficient coding workflows、Filtering conversation sessions by project or date for targeted analysis、Generating JSON manifests for further processing or integration with other AI tools。

session-discovery 支持哪些 IDE 或 Agent?

该技能兼容 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer。可使用 Killer-Skills CLI 一条命令通用安装。

session-discovery 有哪些限制?

Requires local Claude Code conversation sessions;Needs access to the harness root and scripts directory;Limited to sessions stored on the local machine。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add hartphoenix/weft/session-discovery。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    session-discovery 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

Imported Repository Instructions

The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.

Supporting Evidence

session-discovery

Unlock efficient coding with session-discovery, an AI agent skill that finds local Claude Code conversation sessions, filtering by date and project for...

SKILL.md
Readonly
Imported Repository Instructions
The section below is supporting source material from the upstream repository. Use the Killer-Skills review above as the primary decision layer.
Supporting Evidence

Session Discovery

Utility skill that finds Claude Code conversation sessions stored on the local machine. Runs a bun script and returns a JSON manifest.

How to invoke

Run the discovery script from the harness root:

bash
1bun "$(cat ~/.config/weft/root)/scripts/session-discovery.ts" [flags]

Flags

FlagDefaultDescription
--since YYYY-MM-DDtodayStart of date window
--until YYYY-MM-DDtodayEnd of date window
--project <substring>(all)Filter by project path substring

Examples

bash
1SCRIPT="$(cat ~/.config/weft/root)/scripts/session-discovery.ts" 2 3# Today's sessions 4bun "$SCRIPT" 5 6# Sessions since last review 7bun "$SCRIPT" --since 2026-02-25 8 9# Only sessions in the roger project 10bun "$SCRIPT" --project roger 11 12# Two-day window, specific project 13bun "$SCRIPT" --since 2026-02-27 --until 2026-02-28 --project weft-dev

Output format

JSON to stdout. Diagnostics to stderr.

json
1{ 2 "meta": { 3 "claudeDir": "/Users/name/.claude", 4 "since": "2026-02-28", 5 "until": "2026-02-28", 6 "filesScanned": 258, 7 "sessionsMatched": 6, 8 "errors": [] 9 }, 10 "sessions": [ 11 { 12 "sessionId": "uuid", 13 "project": "/Users/name/code/project", 14 "projectEncoded": "-Users-name-code-project", 15 "filePath": "/Users/name/.claude/projects/.../uuid.jsonl", 16 "start": "2026-02-28T00:21:36.816Z", 17 "end": "2026-02-28T01:56:48.087Z", 18 "messageCount": 178, 19 "userMessageCount": 59, 20 "firstPrompt": "first 120 chars of first real user message...", 21 "gitBranch": "feature-branch", 22 "schemaVersion": "2.1.59" 23 } 24 ] 25}

How consuming skills should use the manifest

1. Run the script, parse the output

bash
1bun "$(cat ~/.config/weft/root)/scripts/session-discovery.ts" --since <last-review-date> 2>/dev/null

Parse the JSON from stdout. The meta object tells you how many sessions were found and whether errors occurred.

2. Decide what to read

The manifest gives you file paths and message counts. Use this to decide whether to analyze inline or delegate to sub-agents (per the context management gate in session-review).

  • Small window (1-2 sessions, < 200 total messages): read the JSONL files directly
  • Large window (3+ sessions, or > 500 messages): dispatch sub-agents with specific file paths from the manifest

3. Read session content

Each filePath is a JSONL file. Each line is a JSON object. The relevant message types:

typeContains
userUser messages. message.content is a string or array of {type, text} blocks.
assistantAgent responses. message.content is an array of text and tool_use blocks.
queue-operationSession lifecycle (enqueue/dequeue). Has timestamp but no content.
progressStreaming progress. Skip for analysis.
file-history-snapshotFile state snapshots. Skip for analysis.

Filter to user and assistant types for conversation analysis.

4. Filter noise from user messages

User message content often starts with IDE-injected metadata. Skip blocks matching these patterns:

  • <ide_opened_file>...
  • <system-reminder>...
  • <command-message>...
  • <command-name>...
  • <local-command...

Resilience notes

CLAUDE_CONFIG_DIR: The script checks this environment variable first. If set, it uses that path instead of ~/.claude/. Most users won't have this set.

Path encoding: The /- encoding for project directories is undocumented and could change. The script doesn't depend on decoding being perfect — it just needs to find JSONL files under projects/. The decoded project field is best-effort for display.

Schema changes: The JSONL format is internal to Claude Code and undocumented. The script reads timestamp and type fields. If these change, the script will return empty results (not crash). The schemaVersion field in each session lets consumers detect version mismatches.

30-day retention: Claude Code may delete sessions older than 30 days. The script can only find what's on disk.

Cross-midnight sessions: The script uses interval overlap logic, not simple date matching. A session starting at 23:00 on day N and ending at 01:00 on day N+1 will match queries for either day.

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寻找 session-discovery 的替代方案 (Alternative) 或可搭配使用的同类 community Skill?探索以下相关开源技能。

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