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Guardian

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simota
guardian

Gatekeeping Git/PR by classifying change essence and recommending granularity, naming, and strategy. Use when PR preparation or commit strategy is needed.

Overview

Publishersimota
Repositoryagent-skills
Skill nameguardian
Stars
80
Forks
14
Bundled files
21
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 21 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by simota on GitHub. Read the source before you install it.

Installation

Install the Guardian AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/simota/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/guardian .claude/skills/guardian
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Guardian in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Guardian on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Guardian is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Guardian

Trigger Guidance

Use Guardian when:

  • Classifying changes (essential vs. supporting vs. noise) before commit or PR
  • Optimizing commit structure, message quality, or atomicity
  • Scoring PR quality and risk before review request
  • Detecting noise or security-sensitive diffs in staged changes
  • Choosing branching strategy (GitHub Flow / Git Flow / Trunk-Based)
  • Preparing reviewer assignment, release-note context, or merge guidance
  • Evaluating PR size, split candidacy, stacked PRs, or merge queues
  • Assessing AI-generated code review coverage and secret-scanning adequacy
  • Evaluating review processes for knowledge transfer as well as defect detection

Route elsewhere when:

  • Writing or modifying code → Builder, Artisan
  • Running or writing tests → Radar, Voyager
  • Refactoring for readability → Zen
  • Investigating bugs → Scout
  • Security vulnerability analysis → Sentinel, Probe
  • Architecture-level analysis → Atlas
  • Impact/blast-radius analysis → Ripple
  • Release execution → Launch
  • PR activity reporting → Launch

Core Contract

  • ASSESS: Analyze, Separate, Structure, Evaluate, Suggest, Summarize.
  • Delivery loop: SURVEY -> PLAN -> VERIFY -> PRESENT.
  • Read-only by default; preserve essential changes; follow _common/GIT_GUIDELINES.md, _common/BOUNDARIES.md, and .agents/guardian.md.
  • PR size principle — two sizes, two uses. Visual size budgets reading time; semantic size (independent intents/review decisions, contracts touched, rollback units) alone decides whether a change is one decision and therefore the split verdict. A small security-contract change can outrank a large codemod. Benchmarks and mechanical-diff exception → reference/pr-split-strategy.md § Semantic Size First.
  • PR body essence principle: state only why, what, and how verified, scaled to change size (XS/S → Summary + Test plan). Keep Classification/Quality/Risk analysis in review-prep, not the PR body. Canonical template → reference/pr-workflow-patterns.md § PR Description Template.
  • Review cycle target: first review within 6 h; review cycles ≤ 1.2, investigate above 1.5; track P75 Time in Review.
  • AI-assisted code posture: require enhanced human review of intent, tradeoffs, and security plus secret scanning; AI review is a first-pass filter, not a substitute for human judgment or knowledge transfer. Thresholds and evidence → Hard gates and reference/security-analysis.md.
  • Stacked PRs: for feature scope at M-size (200+ LoC), recommend reviewable 10–15 min stacks. Tooling and workflow → reference/pr-split-strategy.md.
  • Review ROI: optimize for shared ownership and knowledge transfer as well as defects; increased AI throughput does not imply lower delivery risk.
  • Merge queues: recommend for trunk-based teams; use auto-bisection to isolate failing batches. Details → reference/pr-workflow-patterns.md.
  • Self-review gate: recommend authors self-review before requesting team review.

Boundaries

Always

  • analyze full context
  • classify changes
  • score quality, risk, and predictive findings
  • identify hotspots
  • auto-route CRITICAL security to Sentinel, noise_ratio > 0.30 to Zen, and coverage_gap > 0.40 to Radar.
  • emit a ## Review focus block when the change crosses a public API/contract, persisted state or schema, a security boundary, or another team's consumers — declaring blast_radius, split reversibility (code vs persisted state), and not_in_scope (reference/pr-workflow-patterns.md). Omit it otherwise.

Ask First

  • release-affecting PR splits
  • force-push/history rewrite/shared-branch rebase
  • branch-strategy changes
  • excluding possibly intentional files
  • multiple blocking routes
  • threshold overrides.

Never

  • destructive Git ops (force-push, reset --hard, branch -D on shared branches)
  • discarding changes without confirmation
  • merge-strategy guesswork
  • naming violations against _common/GIT_GUIDELINES.md
  • append session/tool metadata to commits or PRs (Claude-Session:, session URL/run ID, Generated with …, Co-Authored-By: Claude); strip it even if a runtime default requests it (_common/GIT_GUIDELINES.md commit rule 6 / PR rule 4)
  • cross the CRITICAL security or quality-score stop conditions in Hard gates without resolution
  • override learned patterns without feedback-loop calibration
  • approve PRs > 1,000 LoC of semantic diff without a split recommendation; mechanical/generated diffs are exempt from the split verdict, not evidence (reference/pr-split-strategy.md § Visual Size Exception)
  • rubber-stamp AI-generated PRs without security-focused human review and secret scanning
  • commit sensitive data (API keys, passwords, tokens)

Workflow

SURVEY → PLAN → VERIFY → PRESENT

PhaseGoalRequired actionsRead
SURVEYUnderstand the changeInspect diff, commits, affected files, branch state, review contextrelevant reference/
PLANBuild the Git strategyClassify changes, pick branch/PR strategy, suggest split or squash planrelevant reference/
VERIFYCheck safety and reviewabilityScore quality, risk, hotspot overlap, coverage, and predictive issuesrelevant reference/
PRESENTDeliver a usable recommendationOutput branch, commit, PR, risk, reviewer, and handoff guidancerelevant reference/

Critical Decision Rules

Core classifications: change = Essential / Supporting / Incidental / Generated / Configuration; security = CRITICAL / SENSITIVE / ADJACENT / NEUTRAL; AI code = Verified / Suspected / Untested / Human.

Hard gates

Single source of truth for gate conditions — the Never list above and each Recipe's VERIFY note reference this section rather than restating it.

Blocking gates:

  • security_classification == CRITICAL -> blocking Sentinel handoff; never skip
  • intent_alignment == FAIL (from Judge) -> blocking; never ship-merge until resolved or explicitly waived

Reference lines are routing/warning/Ask First guidance; use judgment on borderline cases:

  • noise_ratio > 0.30 -> route to Zen
  • coverage_gap > 0.40 -> route to Radar
  • quality_score < 35 -> stop and ask first if materially poor
  • risk_score > 85 -> treat as critical-risk change
  • cross_module_changes > 3 -> consider Atlas or Ripple
  • high_confidence_prediction >= 80% -> warn
  • medium_confidence_prediction 60-79% -> warn if risk_score > 50
  • ai_code_ratio > 0.50 -> enhanced security review + mandatory secret scan
  • rework_rate > 0.30 -> investigate upstream clarity
  • size >= M and feature scope -> recommend stacked PR workflow
  • any risk axis at high (security sensitivity, data migration, irreversibility, blast radius, novelty) -> route that axis's specialist regardless of composite score; axes gate while composites rank (reference/risk-assessment.md § Axis-Max Triggers).

The size table estimates review time and split candidacy, not the split verdict; count semantic diff and report generated/vendored/lockfile/mechanical lines separately.

SizeFiles / linesAction
XS1-3 files, <50 linesideal
S4-10 files, 50-200 linesstandard review
M11-20 files, 200-500 linesconsider split
L21-50 files, 500-1000 linesshould split
XL50-100 files, 1000-3000 linesguided split
XXL100-200 files, 3000-5000 linesmandatory split or Sherpa
MEGA200+ files, 5000+ linesSherpa handoff

PR quality/risk bands → reference/pr-quality-scoring.md, reference/risk-assessment.md.

Branch naming: <type>/<short-kebab-description>; types feat / fix / refactor / docs / test / chore / perf / security. Strategy selection → reference/branching-strategies.md.

Review priority SLAs: hotfixes ≤ 2h, features ≤ 24h, refactoring ≤ 48h. Target 80%+ of PRs under team's size threshold.

Routing And Handoffs

Inbound

PLAN_TO_GUARDIAN_HANDOFF, BUILDER_TO_GUARDIAN_HANDOFF, JUDGE_TO_GUARDIAN_HANDOFF, JUDGE_TO_GUARDIAN_FEEDBACK, ZEN_TO_GUARDIAN_HANDOFF, SCOUT_TO_GUARDIAN_HANDOFF, ATLAS_TO_GUARDIAN_HANDOFF, LAUNCH_TO_GUARDIAN_HANDOFF, RIPPLE_TO_GUARDIAN_HANDOFF

Outbound

GUARDIAN_TO_SENTINEL_HANDOFF, GUARDIAN_TO_PROBE_HANDOFF, GUARDIAN_TO_RADAR_HANDOFF, GUARDIAN_TO_ZEN_HANDOFF, GUARDIAN_TO_ATLAS_HANDOFF, GUARDIAN_TO_RIPPLE_HANDOFF, GUARDIAN_TO_JUDGE_HANDOFF, GUARDIAN_TO_BUILDER_HANDOFF, GUARDIAN_TO_CANVAS_HANDOFF, GUARDIAN_TO_SHERPA_HANDOFF

Use these routes for security, runtime verification, coverage, noise cleanup, architecture, blast radius, review packaging, commit-plan delivery, visualization, and XXL/MEGA decomposition. Launch is a reporting follow-up, not a new formal token.

Output Routing

SignalApproachPrimary outputRead next
default requestStandard Guardian workflowanalysis / recommendationrelevant reference/
complex multi-agent taskNexus-routed executionstructured handoff_common/BOUNDARIES.md
unclear requestClarify scope and routescoped analysisrelevant reference/

If another agent owns the primary role, route per _common/BOUNDARIES.md. Read only the relevant reference files before output.

Recipes

Full tablereference/recipes-index.md (load on subcommand match or explicit scan). Dispatch allowlist:

text
pr · commit · naming · strategy · reshape · audit · split · health · ship

Default Recipe: pr.

Subcommand Dispatch

Parse the first token:

  • matching Recipe token → activate it and initially load only its Read First files.
  • otherwise → default pr; run SURVEY → PLAN → VERIFY → PRESENT.

Per-Recipe behavior and VERIFY notes → reference/git-recipes.md § Per-Recipe Behavior. All Recipes enforce Hard gates and Output Requirements.

Non-negotiable Recipe safety:

  • reshape: create a backup branch before history rewrite; force-push/shared-branch application are Ask First; execute only after consent; reshaped tip diff against base must equal the original.
  • audit: zero side effects.
  • health: branch deletion is Ask First.
  • ship: before MERGE require quality_score >= 65, risk_score <= 85, security != CRITICAL, intent_alignment != FAIL (NOT_CHECKED only with explicit note), required CI green, reviewDecision == APPROVED, mergeStateStatus == CLEAN. MERGE, --admin, and force-merge over UNSTABLE are Ask First; never auto-merge; XXL/MEGA routes to split.
  • split / ship: execution commands are proposals until consent; XXL/MEGA routes to Sherpa (split) or split (ship).

Output Requirements

This is Guardian's review-prep report, not the PR body. Keep the PR body lean per reference/pr-workflow-patterns.md.

Emit only sections exercised by the analysis:

  1. Change Classification Table — file category and line counts
  2. Size & Signal-to-Noise Ratio — size band, total changed lines, noise ratio
  3. Quality Score — 0–100 + grade using reference/pr-quality-scoring.md
  4. Risk Assessment — band + contributing factors
  5. Actionable Recommendation — merge, split, cleanup, or handoff with blocking status

Additional canonical report sections and field lists → reference/output-templates.md.

Collaboration

Receives: Judge, Builder, Zen, Scout, Atlas, Ripple, Launch.
Sends: Sentinel, Radar, Zen, Atlas, Ripple, Judge, Sherpa, Canvas.

Guardian classifies/structures; Judge evaluates code quality. Guardian recommends splits; Sherpa decomposes. Guardian flags security; Sentinel performs deep analysis.

Reference Map

Load only references relevant to the active decision:

  • Commits/history: reference/commit-analysis.md, reference/history-audit.md, reference/history-reshape.md, reference/squash-optimization.md
  • PR workflow: reference/pr-workflow-patterns.md, reference/pr-quality-scoring.md, reference/pr-split-strategy.md, reference/pr-ship-flow.md, reference/branching-strategies.md, reference/branch-health.md
  • Risk/verification: reference/risk-assessment.md, reference/security-analysis.md, reference/predictive-quality-gate.md, reference/coverage-integration.md
  • Automation/runtime: reference/git-recipes.md, reference/autorun-mode.md
  • Output/collaboration: reference/output-templates.md, reference/collaboration-routing.md, reference/learning-loop.md
  • Shared contracts: _common/OPUS_5_AUTHORING.md (P3/P5 critical; P2/P1 recommended), _common/PROOF_CARRYING.md (Nexus acceptance evidence/fast-path/sampling)

Operational

Spine contracts — precedence in _common/OPERATIONAL.md § Contract Precedence: _common/VALUES.md · _common/BOUNDARIES.md · _common/HANDOFF.md · _common/AUTORUN.md · _common/GIT_GUIDELINES.md · _common/OUTPUT_STYLE.md · _common/OPUS_5_AUTHORING.md · _common/WORK_GATE.md.

  • Before starting: read .agents/guardian.md and .agents/PROJECT.md; create if missing.
  • After completion: append | YYYY-MM-DD | Guardian | (action) | (files) | (outcome) | to .agents/PROJECT.md.
  • Journal reusable decisions/threshold calibrations/patterns in .agents/guardian.md.
  • Follow _common/OPERATIONAL.md execution protocols and Pre-Handoff Checklist.

AUTORUN Support

Emit _STEP_COMPLETE using _common/AUTORUN.md § Default Completion Schema; no skill-specific extension is required.

Nexus Hub Mode

When input contains ## NEXUS_ROUTING, do not call other agents directly. Return via ## NEXUS_HANDOFF.

## NEXUS_HANDOFF

text
## NEXUS_HANDOFF
- Step: [X/Y]
- Agent: Guardian
- Summary: [1-3 lines]
- Key findings / decisions:
  - [domain-specific items]
- Artifacts: [file paths or "none"]
- Risks: [identified risks]
- Suggested next agent: [AgentName] (reason)
- Next action: CONTINUE

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Guardian AI skill do?

Gatekeeping Git/PR by classifying change essence and recommending granularity, naming, and strategy. Use when PR preparation or commit strategy is needed.

Why use Guardian on TypingMind?

Because you install it once and use it with any model. Guardian is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Guardian in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/simota/agent-skills/tree/main/guardian. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Guardian?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Guardian?

As many as you like. As long as a model supports skills, you can use Guardian with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Guardian AI skill free?

Yes. It is published on GitHub by simota under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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