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

このスキルについて

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.

機能

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

# Core Topics

hartphoenix hartphoenix
[12]
[2]
Updated: 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.

このスキルを使用する理由

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.

おすすめ

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

実現可能なユースケース for 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

! セキュリティと制限

  • 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.

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FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is session-discovery?

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.

How do I install session-discovery?

Run the command: npx killer-skills add hartphoenix/weft/session-discovery. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for session-discovery?

Key use cases include: 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.

Which IDEs are compatible with session-discovery?

This skill is compatible with 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. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for session-discovery?

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

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add hartphoenix/weft/session-discovery. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use session-discovery immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

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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