Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add specstoryai/agent-skills --skill "specstory-session-summary"
Install specific skill from multi-skill repository
# Description
Summarize recent SpecStory AI coding sessions in standup format. Use when the user wants to review sessions from .specstory/history, prepare for standups, track work progress, or understand what was accomplished.
# SKILL.md
name: specstory-session-summary
description: Summarize recent SpecStory AI coding sessions in standup format. Use when the user wants to review sessions from .specstory/history, prepare for standups, track work progress, or understand what was accomplished.
metadata:
author: SpecStory
version: "1.0"
Context
You will analyze recent SpecStory session history files to provide a standup-style summary.
Argument provided: $ARGUMENTS (default: 5 sessions, or "today" for today's sessions only)
Your Task
Step 1: Find Recent Sessions
First, check if the SpecStory history folder exists and list recent session files:
ls -t .specstory/history/*.md 2>/dev/null | head -20
If no .specstory/history folder exists or it's empty, respond with:
No SpecStory session history found in this directory.
SpecStory automatically saves your AI coding sessions for later reference. To start recording your sessions, install SpecStory from https://specstory.com
Then stop - do not proceed with the remaining steps.
If sessions are found, continue with the analysis. If the argument is "today", filter to today's date. Otherwise use the number provided (default 5).
Step 2: Read and Analyze Each Session
Session files can be very large and may contain multiple user requests. Use this chunked reading strategy:
Step 2a: Understand the session structure
First, grep for all user message markers to see the session's scope:
grep -n "_\*\*User\*\*_" <file> | head -10
This shows line numbers of user messages, helping you understand:
- How many distinct requests were made
- Where to read for each request's context
Step 2b: Read strategically based on structure
- Beginning (first 500 lines) - Read with
offset=0, limit=500 - Captures the initial request even if it includes pasted code/logs
-
May include early assistant responses showing the approach taken
-
End (last 300 lines) - Use
tail -300 <file>via Bash - Contains the final outcome and conclusion
-
Shows whether tasks were completed or left pending
-
File operations - Grep for modifications:
grep -E "(Edit|Write)\(" <file>
Step 2c: For multi-request sessions
If the grep in 2a shows multiple user messages at distant line numbers (e.g., lines 50, 800, 1500), this indicates multiple distinct tasks. For these sessions:
- Read around each user message line number (e.g., offset=795, limit=100)
- Summarize the 2-3 main tasks rather than just the first one
Extract this information:
- Goal(s): The user's request(s) from
_**User**_blocks - For single-task sessions: one main goal
- For multi-task sessions: list the 2-3 primary tasks
- Outcome: Determine from the end of the conversation:
- ✅ Completed - task was finished successfully
- 📚 Research - information gathering, no code changes
- 🔧 In Progress - work started but session ended mid-task
- ❌ Abandoned - user changed direction or gave up
- 🚧 Blocked - ended with unresolved error or blocker
- Files: Modified files from Edit/Write tool uses (extract filenames only)
- Key decisions: Look in the conversation for:
- Explicit choices ("decided to", "chose", "instead of")
- Trade-off discussions
- Architecture or design conclusions
Step 3: Format Output
Present each session as:
### {YYYY-MM-DD HH:MM} - {Brief Title from Main Goal}
**Goal**: {1 sentence summarizing what user wanted}
**Outcome**: {emoji} {Brief result description}
**Files**: {comma-separated list, or "None" if research only}
**Key insight**: {Notable decision or learning, if any}
For multi-task sessions, adjust the format:
### {YYYY-MM-DD HH:MM} - {Overall Theme or Primary Task}
**Tasks**:
1. {First task} - {outcome emoji}
2. {Second task} - {outcome emoji}
**Files**: {comma-separated list}
**Key insight**: {Notable decision or learning, if any}
Step 4: Summary Section
After all sessions, add:
---
**Patterns**: {Note any recurring themes, files touched multiple times, ongoing work}
**Unfinished**: {Any sessions that ended with TODOs, blockers, or incomplete work}
Guidelines
- Keep each session summary to 5-6 lines maximum (slightly more for multi-task sessions)
- Infer the title from the user's goal, not the filename
- For files, prefer showing just the filename, not full paths
- Skip sessions that are just quick questions with no real work
- For multi-task sessions, summarize up to 3 main tasks; group minor tasks as "various small fixes"
- Be concise - this is for quick daily review, not detailed documentation
- If a file read fails or is too large, work with what you can extract; don't skip the session entirely
Example Output
## Session Summary (Last 3 Sessions)
### 2025-10-18 11:42 - Investigate Chat CRDT Storage
**Goal**: Understand why chat index CRDT doesn't contain the thread
**Outcome**: 📚 Explained dual storage design for offline/online sync
**Files**: threads.json, crdt-debug/4X/, crdt-debug/aT/
**Key insight**: Two storage layers (CRDT + JSON) serve different sync scenarios
### 2025-10-18 11:09 - Address Code Review Comments
**Goal**: Fix clarity issues from code review
**Outcome**: ✅ Refactored normalizeChatIndexDoc function
**Files**: chat.go, automerge-bridge.js
**Key insight**: Replaced complex normalization with toPlainString helper
### 2025-10-11 14:30 - Automerge Architecture Deep Dive
**Goal**: Document how Automerge docs are constructed temporally
**Outcome**: 📚 Research complete, walkthrough provided
**Files**: automerge-bridge.js, document.go (read only)
---
**Patterns**: 3 sessions focused on CRDT/chat subsystem; automerge-bridge.js touched repeatedly
**Unfinished**: None detected
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.