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reflect技能安全扫描

Agent Skill

reflect 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

5,333

周安装

220

GitHub Stars

公开资料未说明

下载量

1,742
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reflect(技能安全扫描)
来源仓库:https://github.com/camacho/ai-skills
仓库路径:skills/reflect
安装命令:
npx skills add https://github.com/camacho/ai-skills --skill reflect
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/camacho/ai-skills --skill reflect

简介

reflect 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 适用于提交前检查阶段,强制合并 .branch-context.md 到 MEMORY.md 后方可完成任务。
  • 自动标记时间戳并分析会话内 git 变更与用户反馈,生成结构化复盘报告。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • reflect 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Phase gate: COMMIT checkpoint. Do not declare task complete until Step 8 passes.

Execute all steps in order. Escalate to the user only at explicit escalation points.

Step 0 — Mark reflect timestamp

date +%s > "${CLAUDE_PROJECT_DIR}/ai-workspace/.last-reflect-ts"

Step 1 — Review session work

git log --oneline -10

Review recent edits and corrections made during this session. If a completed plan exists in ai-workspace/plans/ (Outcomes & Learnings filled in), use it as the primary source. Otherwise derive learnings directly from git history and session events. Build an explicit inventory of candidate learnings before moving on.

Step 1b — Verify issue closure

Scan commit messages for closing keywords (case-insensitive): closes, close, closed, fixes, fix, fixed, resolves, resolve, resolved followed by #N.

For each issue number found:

gh issue view N --json state --jq '.state'

Warn if any referenced issue is still open. Do not block — warn and continue.

Step 2 — Classify learnings

SignalDestination
References this repo's files, paths, configs, or project conventionsMEMORY.md only
General pattern, tool behavior, or preference applicable across projectsBasic Memory vault only
Both (project-specific instance of a cross-project pattern)Both — cross-reference each entry to the other

Step 2b — Comment learnings on related issues

Plan has Issue: #N in frontmatter: post learnings directly as a comment on that issue. No confirmation needed — the link is explicit.

No plan or no Issue field: run gh issue list --state open --json number,title,labels --limit 50, fuzzy-match keywords against open issue titles. Confirm with user before posting to any fuzzy match. A bad auto-comment is worse than a missed one.

Step 3 — Write to MEMORY.md (concurrent write protocol)

MEMORY.md lives on main and may receive concurrent writes. Never write from the current worktree.

REFLECT_DIR="$CLAUDE_SESSION_DIR/memory-reflect"
[ -d "$REFLECT_DIR" ] && rm -rf "$REFLECT_DIR"
git clone --depth 1 "$(git remote get-url origin)" "$REFLECT_DIR"

Retry loop (max 3 attempts):

  1. Read $REFLECT_DIR/ai-workspace/MEMORY.md as it exists now.
  2. Re-derive entries from the current file state — skip anything already present, avoid duplicates. Do not auto-merge. Do not replay old diffs.
  3. Add new entries. Prune entries older than 30 days. Keep under 200 lines — if over, summarize the oldest section (never silently delete).
  4. Commit and push:
git -C "$REFLECT_DIR" add ai-workspace/MEMORY.md
git -C "$REFLECT_DIR" commit -m "reflect: update MEMORY.md [$(date +%Y-%m-%d)]

Co-Authored-By: Claude <model>"
git -C "$REFLECT_DIR" push
  1. On non-fast-forward rejection: git -C "$REFLECT_DIR" pull --rebase, return to step 1.
  2. After 3 failures, stop and escalate — do not proceed: MEMORY.md write failed after 3 retries. Manual merge required at $REFLECT_DIR/ai-workspace/MEMORY.md.

Step 4 — Write to Basic Memory vault

Use mcp__basic-memory__search_notes first to avoid duplicates, then mcp__basic-memory__write_note or mcp__basic-memory__edit_note. Use [[wiki-links]] for connections. Tag every note with the current project name.

If Basic Memory MCP is not connected, log the skip in the output summary and continue. Do not block.

Step 5 — ADR check

If a significant architectural decision, convention, or recurring pattern emerged, prompt:

Should this become an ADR? Candidate: "". (y/n)

If yes: create ai-workspace/decisions/ADR-NNN-<slug>.md using the template at ai-workspace/decisions/TEMPLATE.md. Increment the highest existing ADR number.

Step 6 — Create issues from surfaced work

Check for duplicates first (open and recently closed):

gh issue list --state open --limit 100 --json number,title
gh issue list --state closed --limit 20 --json number,title

Source (a) — Reflect TODOs: gotchas, follow-ups, technical debt surfaced during Steps 1–5. Create a GitHub issue for each with no duplicate.

Source (b) — Scratchpad (ai-workspace/scratchpad.md): for each line matching - []... → create issue → rewrite as - [x] → #N. Leave plain bullets untouched — they are reference notes.

Step 7 — Finalize plan

Rename the active plan to .done.md. Verify Outcomes & Learnings is filled in first.

mv ai-workspace/plans/<name>.md ai-workspace/plans/<name>.done.md

This commit can go on the primary worktree — .done.md plan files are in the allowlist (see task-branches.md). Skip if no active plan.

Step 8 — Phase gate

Read .branch-context.md:

  • MEMORY.md not updated +.branch-context.md had content → soft block: warn and do not declare task complete until resolved.
  • .branch-context.md missing or empty → warn but do not block.

Output summary

LayerAction
MEMORY.mdN entries added, N pruned (X lines total)
Basic MemoryN notes written/updated (or: skipped — MCP unavailable)
Issues closed#N, #N (verified) / #N still open (warned)
Issues created#N "", #N ""
Issues commented#N
ADRCreated ADR-NNN / Skipped
PlanFinalized.done.md / None
Phase gateMEMORY.md updated: yes/no —.branch-context.md: found/missing

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.72%
按下载量换算587

Claude

33.49%
按下载量换算583

Cursor

19.75%
按下载量换算344

Gemini CLI

9.51%
按下载量换算166

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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