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guardrailsguardrails 监控告警

Agent Skill

guardrails 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

285

周安装

12

GitHub Stars

2

下载量

1
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:guardrails(guardrails 监控告警)
来源仓库:https://github.com/adewale/guardrails-skill
仓库路径:skills/guardrails
安装命令:
npx skills add https://github.com/adewale/guardrails-skill --skill guardrails
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/adewale/guardrails-skill --skill guardrails

简介

提供验证关卡,确保关键检查通过后才允许 Agent 继续操作,提升流程安全性。

  • 在编码生命周期中自动触发 linting、测试、类型检查和扫描等前置条件。
  • 当检查失败时提供诊断建议而非简单重试,保护高风险配置不被误改。
  • 需确认是否涉及文件读写或命令执行,避免在生产环境触发未授权操作。
  • guardrails 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Guardrails

Verification gates that block the agent from proceeding until checks pass. The agent already knows what linting, testing, type checking, and security scanning are. This skill doesn't re-teach those concepts. Instead, it specifies:

  • When each check runs in the agent lifecycle (hooks that block progress)
  • What to do when stuck (diagnostic escalation instead of retrying)
  • What not to touch (config protection, high-risk action gating)
  • How to accumulate knowledge (lessons learned, agent tool library)

These are the behaviors a coding agent wouldn't exhibit without being told. Everything else — which linter to use, how to structure a unit test — the agent already knows from training and discovers from the project's existing config.

Bundled resources: references/tool-building.md contains the diagnostic tool and notation catalogs with worked examples. references/language-defaults.md is a lookup table for tool selection by ecosystem. Read these when directed, not proactively.


Scope

Not every check applies to every project. The common base applies everywhere: lint, format, types, SAST, dependency audit, secrets scan, dead code detection, duplicate code detection, coverage, unit tests. Beyond that, add layers by project type:

  • Backend / API: integration tests. Usually: property-based, contract, performance.
  • Frontend: integration tests. Usually: screenshot, E2E, accessibility.
  • Full-stack: integration tests. Usually: screenshot, E2E, contract, accessibility.
  • Library / SDK: Usually: property-based, mutation.
  • Data pipeline: integration tests. Usually: property-based, performance.
  • Infrastructure / IaC: integration tests (plan/apply validation).

Scale up as the project matures. A prototype needs the common base. A production system serving users needs the full suite.

Common base: lint, format, types, SAST, dependency audit, secrets scan, dead code detection, duplicate code detection, coverage, unit tests. Dead code and duplicate code tools by ecosystem are listed in references/language-defaults.md.

Lifecycle Hooks

Everything above is just guidance — the agent might follow it, might not. The hooks below are what make guardrails real. They block the agent from proceeding until checks pass. The distinction matters: a lint rule that runs in 20ms and fails the build gives the agent concrete, unambiguous feedback. A prose instruction in a skill file sits in the context window competing for attention with everything else.

Test Runner Discovery

The enforcement system needs two commands in the target project — one fast, one full:

Fast check — Runs in seconds. Fires on every agent stop. Covers: format check, lint, type check, unit tests.

Full suite — Runs before every commit. Covers: everything in the fast check plus integration tests, property tests, secrets scan, dead code detection, duplicate code detection, coverage floor.

Discover, don't impose. Every ecosystem has its own convention: npm scripts (npm test, npm run test:unit), Makefile targets (make test, make check), Go commands (go test./...), pytest (pytest tests/unit), bun scripts, Cargo, etc. During SessionStart, find what the project already uses by inspecting package.json scripts, Makefile targets, CI workflow steps, and README instructions. Adapt to the project's existing runner — don't create a new one alongside it.

If no test runner exists at all, create one using the project's native tooling. Consult references/language-defaults.md for tool selection. The runner must exit non-zero on failure.

SessionStart

Fires when the agent boots up. Discovery and baseline before touching anything.

  • Record git state (HEAD SHA, working tree status) as a session baseline.
  • Discover existing guardrails: inspect config files, CI pipelines, test directories. Identify the project type (Scope section) and determine which checks apply.
  • Read existing test files to learn conventions — naming, structure, assertion style, mocking, fixtures. Match them.
  • Read LESSONS_LEARNED.md if it exists. Apply project-specific lessons.
  • Check for script/agent-tools/. Read the directory and description comments to know what diagnostic tools are available from previous sessions.

Defer to existing conventions. When the project has tooling or patterns in place, follow them — even when they conflict with this skill. Do not switch tools or frameworks without explicit user approval.

Stop

Fires every time the agent returns control. Prevents declaring success without verification.

  • Run the fast check. Block if it fails.
  • If production code changed, verify tests also changed. Untested code is unverified code, regardless of whether it "looks right."

Thrashing circuit breaker. If the fast check fails and the agent has already attempted the same fix twice, it cannot try a third direct fix:

  1. After attempt 1 fails: Try a direct fix. Normal.
  2. After attempt 2 fails: Stop. Build a diagnostic tool or switch to a notation (read references/tool-building.md). Two failed attempts means the agent's model of the problem is wrong — re-reading the same code produces the same wrong model. New information or a different representation breaks the loop.
  3. After attempt 3 fails (with diagnostic): Stop entirely. Report to the user: what's failing, what was tried, what the diagnostic revealed, why it isn't converging.

The breaker resets when the user provides new direction. Persist useful diagnostics to script/agent-tools/.

Completion check. After the fast check passes, check your task list. If planned work items remain and you are not blocked, continue to the next slice — do not hand off. Hand off only when: all items are done, the circuit breaker fired, or context is running low (in which case, commit completed slices and report remaining items).

Work in slices, but finish the job. Verify frequently — a good slice is 1-3 production files plus tests. An agent that writes 15 files then discovers a type error in file 3 has wasted 4-15. But verifying a slice is not completing the task. Do not hand off to the user until all planned work items are done or you are explicitly blocked.

Commit (PreToolUse + git hooks)

Fires on any git commit, whether from the agent or a human. Same enforcement for both.

  • Run the full suite. Block if it fails.
  • Secrets scan on staged files.
  • Verify commit message follows project conventions.
  • Integration/deployment check: Verify the change is reachable, not just correct. Code that passes tests but isn't wired into the application is a deployment gap. Confirm: new modules imported? Routes registered? Migrations included? Entry points updated? If nothing invokes the new code, that's a failure even with passing tests.
  • Bug fix retrospective: When the commit fixes a bug (fix: prefix or fixing a reported issue), answer three questions before committing:

1. Detection gap — Why didn't existing tests or checks catch this? What was missing from the verification layer? 2. Prevention — What concrete artifact was added to prevent recurrence? A new test case, type constraint, assertion, or lint rule — not just a prose note. 3. Pattern scan — Search the codebase for the same error pattern. If siblings exist, fix them in this commit or track them explicitly. Block the commit if any question is unanswered.


Tool Building and Notation

When direct fixes fail, the agent needs new information or a different problem representation. Read references/tool-building.md for the full catalog of diagnostic tools, notations, and three worked examples.

The circuit breaker (Stop hook) mandates reading this reference after 2 failed attempts. But the best use is *proactive*: recognize the problem shape during planning and build scaffolding before writing code. Common patterns:

  • Modifying a function's interface → build a call site inventory first
  • Complex conditional logic (>3 booleans or >4 branches) → build a decision matrix
  • State machine behavior → build a state transition table
  • Data transformation pipeline → trace sample values through each stage
  • Unfamiliar codebase area → build a codebase index before editing

Diagnostic tools often graduate into permanent infrastructure — reproduction scripts become test cases, schema checkers become health checks, dependency mappers feed the integration check. Building a diagnostic tool is not a detour; it's often the most direct path to completing the task.


Config Protection

The agent does not edit its own guardrail configuration. An agent under pressure to make tests pass has an incentive to weaken the tests rather than fix the code. Block the agent from modifying: test scripts, lint/format/type config, CI definitions, pre-commit config, and coverage thresholds. If config needs changing, propose it to the user.


High-Risk Action Gating

Some operations have blast radius beyond the current code change. Even if the user's request implies them, the agent stops and asks because these are irreversible or affect systems beyond the codebase:

  • Destructive data operations (DROP TABLE, deleting records, truncating logs)
  • Permission and access changes (auth rules, IAM policies, CORS)
  • Deployment actions (pushing to production, triggering deploys, scaling)
  • Bulk or irreversible operations (mass updates, migrations, force-pushes)
  • Financial or billing changes (anything committing to spend)

Describe the operation and blast radius, then wait for confirmation.


Lessons Learned

The agent accumulates project-specific knowledge across sessions. Without a persistent record, each session starts from zero and risks repeating the same mistakes.

Maintain LESSONS_LEARNED.md in the project root. Append an entry when encountering: a guardrail failure requiring multiple attempts, a non-obvious project convention, a surprising tool behavior, a deployment gap tests didn't catch, a bug fix retrospective that revealed a detection gap, a thrashing episode, or a diagnostic tool that proved useful.

Entry template:

### YYYY-MM-DD — [short title]
**Context:** What task was being performed.
**What happened:** The surprising behavior or failure.
**Resolution:** How it was resolved.
**Rule:** [One-line directive for future sessions to follow.]

Example:

### 2026-02-14 — Custom validation framework
**Context:** Adding input validation to /users endpoint.
**What happened:** Started with express-validator, broke existing tests.
Project uses a custom rule-based validator in src/validation/ — not documented
anywhere except inline comments in rules.js.
**Resolution:** Rewrote validation using the custom framework (validate() + rules).
**Rule:** Always check src/validation/ for this project's validation pattern
before reaching for a third-party library.

Commit to version control. If a lesson reveals a missing guardrail, propose adding one — don't just document the workaround.


Agent Responsibilities (Not Hookable)

Behaviors the agent follows because the reasoning is sound, not because a hook enforces them.

Planning: Define completion criteria — what "done" looks like for this task (files changed, tests added, routes wired, etc.). Decide testing layers before writing code. Check problem shape against the proactive table in references/tool-building.md. Assess security relevance.

Code writing: Format and type-check incrementally. If the same type error reappears after one fix, that signals a structural misunderstanding — switch to a notation rather than continuing to guess.

Handoff / PR: Report what was verified, which layers were skipped and why, security findings, and coverage delta. Do not present work as complete if any hook failed.

适合场景

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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

35.94%
按下载量换算0

Claude

26.85%
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Cursor

19.6%
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Gemini CLI

8.81%
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安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

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