Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/rafeeqinea-reflect
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/reflect
Tools it pre-approves 5
From allowed-tools in SKILL.md: the agent may use these without asking you each time.
Read,Write,Edit,Glob,Grep
What's inside
- Skill name
reflect- Description
- End-of-session reflection. Analyze what happened in this conversation, extract learnings, update memory and instructions. Use when user says "reflect", "what did you learn", "session review", or at the end of a productive session.
- License
- MIT
- Allowed tools
- Read,, Write,, Edit,, Glob,, Grep
- Instructions
- 74 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Reflect — Session Learning Loop ## Trigger `/reflect` ## Purpose Analyze the current conversation, extract what worked and what didn't, and persist improvements for future sessions. This is how I get better over time. ## Workflow ### Step 1: Session Analysis Review the full conversation and identify: - **Tasks completed**: What did the user ask for? Did I deliver? - **Mistakes made**: Where did I give wrong information, take the wrong approach, or waste time? - **Corrections received**: What did the user correct me on? (These are the highest-signal learnings) - **New knowledge gained**: Facts, preferences, patterns discovered this session - **Approaches that worked well**: What should I do more of? ### Step 2: Classify Learnings Sort each learning into one of these categories: | Category | Persists to | Example | |----------|------------|---------| | **Behavioural** | CLAUDE.md | "User wants me to stop hedging" | | **Project fact** | MEMORY.md | "CG converges in 167 iters at N=64" | | **Bug fix / pattern** | memory topic file | "Windows encoding issue with nlm login" | | **Skill gap** | New skill or skill update | "User frequently asks for LaTeX tables" | ### Step 3: Update Files For each learning: 1. **Check for duplicates** — Read the target file first. Don't write what's already there. 2. **Check for contradictions** — If new info contradicts existing memory, UPDATE the old entry, don't add a second conflicting one. 3. **Apply the update**: - CLAUDE.md: Add/modify behavioural instruction (keep total under 50 lines) - MEMORY.md: Add/modify fact (keep total under 200 lines) - Topic files: Create or update as needed - Skills: Modify SKILL.md if a skill needs improvement ### Step 4: Report Show the user exactly what was updated: ``` ## Session Reflection ### What I learned - [learning 1] - [learning 2] ### Files updated - CLAUDE.md: [what changed and why] - MEMORY.md: [what changed and why] - [other files]: [what changed and why] ### No action taken on - [things observed but not significant enough to persist] ``` ## Rules - NEVER add speculative or unverified information to memory - NEVER bloat files — every line must earn its place - If CLAUDE.md exceeds 50 lines after update, consolidate — but the merged version MUST contain ALL information from originals ### HARD CONSTRAINT: Never degrade - Only add or improve — never delete working instructions, memories, or user preferences - Never weaken an existing rule or remove a user correction - Never replace specific instructions with vague ones - If unsure whether a change improves or degrades — don't make it, flag it for the user - Monotonic improvement only — the ratchet only turns one way - If MEMORY.md exceeds 200 lines, move details to topic files and link from MEMORY.md - User corrections are the HIGHEST priority learnings — always persist these - Be honest in the report — don't hide mistakes
From rafeeqinea/claude-academic-toolkit, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Mar 9, 2026 (7 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Read a source file and explain every line in depth.
SkillDFreshness: DClaude Code - 0.409score
self-improve
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Deep self-analysis and improvement.
SkillDFreshness: DClaude Code - 0.406score
debug
rafeeqinea
Structured debugging workflow that teaches the debugging process, not just fixes the bug.
SkillDFreshness: DClaude Code +9 - 0.406score
benchmark
rafeeqinea
Systematic benchmarking and comparison of algorithms, models, or approaches under identical conditions.
SkillDFreshness: DClaude Code +9 - 0.406score
experiment
rafeeqinea
Run, track, and compare ML experiments with structured logging.
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notebook-query
rafeeqinea
Query NotebookLM notebooks directly from Claude Code for grounded, citation-backed answers.
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Cite this page
reflect skill. Agent Mart. https://agentmart.dev/skills/rafeeqinea-reflect (last updated 2026-10-24)