Lov Env Management

凭据管家 · Credential Keeper

把散落的开发环境变量收进“平台 → 账号 → Key”账本，明确每个 Key 的生命周期、验证状态和当前投影目标，并在不回显秘密的前提下同步 Shell 或用户图形会话环境。

Triggers

Activate when

• 用户说“管理我的环境变量”“同一个平台有多个账号和 API Key”“把这个 Key 轮换到 zsh”或“打开环境变量 Dashboard”。
• User asks to “manage API keys”, “rotate an environment variable”, “switch the active account”, or “audit expiring keys”.
• 用户需要维护 Key 的生效时间、过期时间、远端验证结果，或在多个 Key 中选择一个投影到指定变量。

Do not activate when

• 用户只要给当前单次命令临时传一个环境变量；直接使用当前 Shell 的 invocation-scoped environment。
• 用户要把云端 Secret 写入 Vercel、GitHub Actions、Supabase 或 Kubernetes；使用目标平台的 Secret 管理流程，本 Skill 只可提供经过确认的变量名清单。
• 用户要管理登录密码、信用卡或恢复码；使用密码管理器。1Password 仅作为本 Skill 的可选秘密来源。
• 用户要新增 zsh 快捷命令或函数而不是环境变量；交给 lov-zsh-alias。

Security Invariants

• 绝不把 Key 值写入聊天、Profile、日志、Dashboard、URL、命令参数或版本库。
• 新增 Key 只接受隐藏交互输入或标准输入；CLI 故意不提供 --value。
• Registry 只保存脱敏元数据。默认 macOS 使用 Keychain，其他系统使用权限为 0600 的本地 vault；也支持 op:// 引用和已有环境变量引用。
• ~/.zshenv 只写一个受哨兵标记管理的 source 块，实际导出位于权限为 0600 的生成文件。
• 同一 target 和变量名只允许一个 binding。切换账号或 Key 是替换 binding，不是复制多份 export。
• Dashboard 只绑定 127.0.0.1，不展示秘密，也不提供秘密录入框；所有写操作需要当前进程生成的临时 token 和同源请求。
• 不自动把 Key 写入 /etc/environment、LaunchAgent plist 或仓库 .env 文件。系统投影仅作用于当前用户会话，并要求显式确认进程环境暴露风险。

User Profile (cross-session)

每次运行先读取 skill.yaml 声明的 user-profile/v1。Profile 只保存安全的工作偏好，例如默认秘密后端、过期预警天数、Shell rc 文件和 Dashboard 是否自动打开；所有 Key 值及秘密引用都留在专用存储，不进入 Profile。

当用户直接声明需要跨 session 保存的非秘密偏好时，通过 scripts/profile_store.py record --skill-id lov-env-management --confirm 写入 records 并报告保存路径。凭据、token、cookie 和任何 secret-like 字段不得写入 Profile。完整契约见 references/user-profile.md。

Skill Group Composition

运行前阅读 references/skill-composition.md。外部 Skill 只通过脱敏变量清单或秘密引用进行可选交接，不是隐藏依赖。

Workflow (MANDATORY)

Step 0: Resolve context and inspect without secrets

1. Resolve SKILL_DIR, then run profile_store.py read for safe defaults.
2. Resolve storage with this precedence: current CLI flag, LOV_ENV_HOME, Skill Profile, then the platform-safe config directory.
3. Start with list or audit; never search broad home-directory globs for credentials.
4. Separate requested targets: current command, zsh startup, current user GUI session, or a platform-specific remote Secret store.

python3 "$SKILL_DIR/scripts/env_manager.py" list
python3 "$SKILL_DIR/scripts/env_manager.py" audit

Step 1: Model platform, account, and Key identity

Use stable kebab-case IDs. A Key locator is platform/account/key, for example openai/personal/primary or openai/work/rotation-2026-08.

Add every rotated Key as a new record so history stays attributable. Do not overwrite an existing key ID. Choose a secret backend automatically unless the user named one:

1. keychain on macOS when available;
2. file vault with mode 0600 elsewhere;
3. op for an explicit op:// reference;
4. env only when an existing variable is the intended source.

python3 "$SKILL_DIR/scripts/env_manager.py" add \
  --platform openai --account personal --key rotation-2026-08 \
  --env-var OPENAI_API_KEY --backend auto \
  --status standby --expires-at 2026-12-31

The command prompts without echo. For automation, pipe the value and add --secret-stdin; never place it in a shell argument.

Step 2: Maintain lifecycle and validity evidence

Read references/data-model.md before changing lifecycle fields.

• active: eligible for a binding.
• standby: stored for rotation but not preferred.
• disabled: temporarily excluded.
• revoked: permanently excluded; keep metadata for audit history.
• Effective health additionally accounts for not_before, expires_at, and the latest validation result.

Use status for administrative state and probe for remote evidence. probe sends the Key only in an HTTPS header, blocks redirects, never stores a response body, and records only origin, status code, time, and redacted result.

python3 "$SKILL_DIR/scripts/env_manager.py" status \
  openai/personal/rotation-2026-08 --status active

python3 "$SKILL_DIR/scripts/env_manager.py" probe \
  openai/personal/rotation-2026-08 \
  --url https://api.openai.com/v1/models --auth bearer

If a service cannot be probed generically, use mark-validation --result valid|invalid|unknown after a real platform-specific check and record a non-secret note.

Step 3: Bind exactly one Key per target variable

Bindings are independent for shell and system. The binding is the atomic switch point for multiple accounts and multiple keys.

python3 "$SKILL_DIR/scripts/env_manager.py" bind \
  --target shell --env-var OPENAI_API_KEY \
  openai/personal/rotation-2026-08

Reject expired, invalid, disabled, revoked, or not-yet-valid Keys. Only use --allow-unhealthy when the user explicitly accepts the reason and the resulting risk.

Step 4: Preview, apply, and verify environment projection

Read references/environment-targets.md. Every sync is a preview unless --apply is present.

python3 "$SKILL_DIR/scripts/env_manager.py" sync-shell --rcfile "$HOME/.zshenv"
python3 "$SKILL_DIR/scripts/env_manager.py" sync-shell --rcfile "$HOME/.zshenv" --apply
python3 "$SKILL_DIR/scripts/env_manager.py" audit

After applying zsh projection, verify in a new clean zsh process by checking presence or a fingerprint only; never print the value. sync-system targets the current user login session, not machine-wide state, and requires --acknowledge-process-env-risk --apply.

Step 5: Use the local Dashboard

python3 "$SKILL_DIR/scripts/env_manager.py" dashboard --open

The Dashboard shows accounts, effective health, expiry windows, validation age, and target bindings. It can switch bindings and update administrative or validation status, but adding, reading, exporting, or deleting secret values remains terminal-only. See references/dashboard.md.

Step 6: Report evidence

Report only:

• platform/account/key locators;
• variable names and selected targets;
• effective health, dates, validation age, and fingerprint when requested;
• files changed, their modes, backup path, and audit result;
• copyable context_id for errors.

Never report a raw Key, secret reference path, generated export content, or a command containing a secret.

CLI Summary

All commands support --json. Mutating output names changed records but never secret values.

Dependencies

• Python 3.9+ standard library.
• Optional macOS security command for Keychain storage.
• Optional 1Password CLI op for op:// secret references.
• zsh for sync-shell; macOS launchctl or Linux systemctl --user for sync-system.

Validation

python3 "$SKILL_DIR/scripts/validate_skill.py" "$SKILL_DIR"
python3 -m unittest discover -s "$SKILL_DIR/tests" -v

通用反馈闭环

用户在 Skill 驱动任务中提出修改意见时，继续当前产物前必须执行：

1. 先判断意见是 task-specific（仅本次）还是 reusable（可跨任务复用）。
2. task-specific 只修改当前任务，不改 Skill。
3. reusable 先确定作用域：领域规则先更新对应 canonical Skill；适用于所有 Skill 的规则先更新共享规范。
4. 完成规则更新、版本、lint 与分发核验后，再把修改应用到当前任务。
5. reusable 修改会使此前的“确认”“继续”“发吧”失效；完成当前产物修改和回读后必须停下，等待用户下一步指示，不自动进入发布、提交或其他外部写入。