reflect — AI 에이전트 세션 반영 reflect, spire_csharp_analyzers, community, AI 에이전트 세션 반영, ide skills, 대화 기록, Bash 명령, 파일 추가, 대화 분석, 대화 관리

v1.0.0

이 스킬 정보

로깅 및 디버깅 에이전트에 적합합니다. 포괄적인 세션 분석 및 Bash 명령 통합이 필요합니다. AI 에이전트의 세션 반영은 대화 내용을 기록하고 분석하는 기능

기능

Bash 명령을 사용하여 반영 내용을 추가
파일 경로 지정 지원
대화 기록과 분석 구현
대화 주제 반영 선택
효율적인 대화 관리

# Core Topics

Houtamelo Houtamelo
[0]
[0]
Updated: 3/17/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

This page remains useful for teams, 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
Review Score
8/11
Quality Score
42
Canonical Locale
en
Detected Body Locale
en

로깅 및 디버깅 에이전트에 적합합니다. 포괄적인 세션 분석 및 Bash 명령 통합이 필요합니다. AI 에이전트의 세션 반영은 대화 내용을 기록하고 분석하는 기능

이 스킬을 사용하는 이유

에이전트가 Bash 명령을 사용하여 세션 반성을 수행하고, 자세한 로그를 실시간으로 파일에 추가하고, `cat >>` 명령과 같은 프로토콜을 통해 세션 활동에 대한 투명한 통찰력을 제공할 수 있는 능력을 부여합니다.

최적의 용도

로깅 및 디버깅 에이전트에 적합합니다. 포괄적인 세션 분석 및 Bash 명령 통합이 필요합니다.

실행 가능한 사용 사례 for reflect

특정 주제에 대한 세션 반성을 수행하여 목표를 명확한 분석을 수행
디버깅을 위해 포괄적인 세션 로그를 생성
반성을 파일에 추가하여 나중에 검토하고 통찰력을 얻기 위한

! 보안 및 제한 사항

  • 반성 파일을 작성하기 위해 파일 시스템에 대한 액세스가 필요
  • Bash 명령의 기능에 의존
  • 로그 기록을 위해 `$REFLECTION_FILE_PATH`를 설정해야 함

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs 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

FAQ & Installation Steps

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

? Frequently Asked Questions

What is reflect?

로깅 및 디버깅 에이전트에 적합합니다. 포괄적인 세션 분석 및 Bash 명령 통합이 필요합니다. AI 에이전트의 세션 반영은 대화 내용을 기록하고 분석하는 기능

How do I install reflect?

Run the command: npx killer-skills add Houtamelo/spire_csharp_analyzers/reflect. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for reflect?

Key use cases include: 특정 주제에 대한 세션 반성을 수행하여 목표를 명확한 분석을 수행, 디버깅을 위해 포괄적인 세션 로그를 생성, 반성을 파일에 추가하여 나중에 검토하고 통찰력을 얻기 위한.

Which IDEs are compatible with reflect?

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 reflect?

반성 파일을 작성하기 위해 파일 시스템에 대한 액세스가 필요. Bash 명령의 기능에 의존. 로그 기록을 위해 `$REFLECTION_FILE_PATH`를 설정해야 함.

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 Houtamelo/spire_csharp_analyzers/reflect. 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 reflect 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.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

reflect

Install reflect, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Session Reflection

Reflect on what has happened in this session so far. Be honest and specific.

If $ARGUMENTS is provided, focus your reflection on that topic. Otherwise, cover everything.

Writing the reflection

Append the reflection to the file at $REFLECTION_FILE_PATH using a Bash command (e.g. cat >> "$REFLECTION_FILE_PATH" <<'EOF' ...).

If the write fails because the file doesn't exist, tell the user — the SessionStart hook may not have run.

The appended content must follow this structure:

---

## Reflection — {current time}

**Topic**: {$ARGUMENTS if provided, otherwise "General"}

### What went well
- Good decisions made.
- Problems solved efficiently.
- Tools that were properly used.

### What didn't go well
- Bad communication: unclear user messages, information that could have been shared at the start of the session.
- Obstacles hit, bad approaches taken, things that were harder than they should have been.
- Tooling that did not work as expected.

### Suggestions
**Be specific**: Reference file paths, agent names, or workflow steps.

Concrete changes to:
- Project infrastructure.
- Agent instructions.
- Skills.
- Conventions.
- Workflow that would prevent the problems above from recurring.
- Tooling that should be added to the Utilities Project that would help the implementer agent.
- Tooling that could be provided by MCP servers.

Constraints

  • Be honest — don't pad "what went well" to soften criticism.
  • Be specific — vague observations like "things were slow" are useless. Say what was slow and why.
  • Be actionable — every suggestion should be something that can actually be changed in a file or workflow step.
  • Reflect from memory only — do not read files or explore the codebase to inform your reflection.

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