Agent skill

Adding AI Agent Rendering System

by PackmindHub in PackmindHub/packmind

Implement a new Packmind AI agent rendering/deployer pipeline (single-file or multi-file) with type and registry wiring, frontend UI/docs updates, and thorough unit/integration tests to reliably…

Apache-2.0Auto-check passedTesting & QA

Install Adding AI Agent Rendering System

skills CLI
$ npx skills add PackmindHub/packmind --skill adding-ai-agent-rendering-system -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install PackmindHub/packmind adding-ai-agent-rendering-system --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/PackmindHub/packmind.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/.agents/skills/adding-ai-agent-rendering-system .claude/skills/adding-ai-agent-rendering-system && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
adding-ai-agent-rendering-system
GitHub stars
317
Token cost
~4.5k tokens
SKILL.md length
1,450 words
Files
1
Skills in repo
35
Repo updated
First seen
Licence
Apache-2.0

At a glance

Implement a new Packmind AI agent rendering/deployer pipeline (single-file or multi-file) with type and registry wiring, frontend UI/docs updates, and thorough unit/integration tests to reliably…

  • Works in 12 steps: Add RenderMode enum value → Add CodingAgent type → Add CodingAgentArtefactPaths → …
  • Tasks that involve Frontend development
  • SKILL.md covers When to Use, Prerequisites — Gather from User and Recipe Steps
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Adding AI Agent Rendering System is an agent skill from PackmindHub/packmind. Implement a new Packmind AI agent rendering/deployer pipeline (single-file or multi-file) with type and registry wiring, frontend UI/docs updates, and thorough unit/integration tests to reliably support additional coding assistants and distribution formats when introducing a new agent integration or render mode.

Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Testing & QA, covering Frontend development and Integration testing. The repository describes itself as: Packmind seamlessly captures your engineering playbook and turns it into AI context, guardrails, and governance. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Frontend development
  • Tasks that involve Integration testing

Example prompts

  • “/adding-ai-agent-rendering-system”

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Add RenderMode enum value
  2. Add CodingAgent type
  3. Add CodingAgentArtefactPaths
  4. Add to VALID_CODING_AGENTS
  5. Add RenderMode to CodingAgent mapping
  6. Create Deployer class
  7. Implement frontmatter generation
  8. Register deployer in registry
  9. Add AgentConfiguration mapping
  10. Export deployer from package
  11. Handle shared-file supersedence (conditional)
  12. Update GitFileUtils

What it can do on your machine

Read from SKILL.md and the folder at commit 8a10541. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript and markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Adding AI Agent Rendering System loads about 4.5k tokens when it runs. Until then it costs about 87 tokens; SKILL.md has 1,450 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~87
When it runs · the whole SKILL.md, loaded when a task matches
~4.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from PackmindHub/packmind at commit 8a10541, republished under its Apache-2.0 licence (© PackmindHub). 1,450 words, ~4,543 tokens.

Download SKILL.mdSave it as .claude/skills/adding-ai-agent-rendering-system/SKILL.md (or your agent's skills folder).
name
adding-ai-agent-rendering-system
description
Implement a new Packmind AI agent rendering/deployer pipeline (single-file or multi-file) with type and registry wiring, frontend UI/docs updates, and thorough unit/integration tests to reliably support additional coding assistants and distribution formats when introducing a new agent integration or render mode.

Add a new AI agent rendering system to Packmind, supporting both single-file (like AGENTS.md) and multi-file (like Cursor/Continue) patterns, including type definitions, deployer implementation, registry registration, frontend UI integration, documentation updates, and comprehensive tests following Packmind test standards.

When to Use

  • When adding support for a new AI coding assistant (e.g., Continue, Cursor, Claude Code, GitHub Copilot, OpenCode)

  • When implementing a new rendering format for standards and recipes distribution

  • When extending Packmind to support additional AI agent integrations

  • When creating a new deployer that follows the ICodingAgentDeployer interface

Prerequisites — Gather from User

Before proceeding, the following information must be known. If any of these are unclear or not provided, prompt the user to clarify before starting implementation.

  1. What is the agent's identifier key and display name?

    • The identifier key is the snake_case string used in code (CodingAgent type, CodingAgents record, AGENT_FILE_PATHS, etc.). Examples: opencode, gitlab_duo, continue, agents_md.
    • The display name is the human-readable label shown in the frontend UI and CLI. Examples: OpenCode, GitLab Duo, Continue, AGENTS.md.
    • Both values are reused consistently across all steps (type definitions, deployer class, registry, frontend labels, CLI display names, documentation).
  2. In which directories will Standards be rendered?

    • Multi-file example: .agent/rules/ (one file per standard)
    • Single-file example: AGENTS.md, .agent/guidelines.md (all standards aggregated into one file)
    • Hybrid is possible (single-file standards + multi-file commands/skills)
  3. In which directories will Commands be rendered?

    • If the agent supports native slash commands: provide the directory (e.g., .agent/commands/)
    • If the agent does NOT support native slash commands, say so explicitly. Commands will be deployed to .packmind/commands/ as a fallback (same pattern used by GitLab Duo, Junie, and AGENTS.md agents). Users invoke them via @.packmind/commands/command-name.md.
  4. In which directories will Skills be rendered?

    • Example: .agent/skills/
    • If the agent does not support skills, say so explicitly. An empty string will be used in CodingAgentArtefactPaths.
  5. If standards are rendered into a shared file like AGENTS.md: What should be the precedence rules?

    • Multiple agents can write to the same file (e.g., both OpenCode and agents_md write to AGENTS.md).
    • Position in RENDER_MODE_ORDER determines priority — agents later in the array supersede agents earlier when both are active.
    • Specify where this new agent should sit relative to existing agents that share the same file.
  6. What frontmatter format does the agent require? (YAML, Markdown, plain text, none)

  7. What file extensions should be used? (.md, .mdc, .txt, etc.)

  8. What naming convention should be used for files? (e.g., packmind-standard-{slug}.md, standard-{slug}.mdc)

  9. Does the agent require specific frontmatter properties? (name, globs, alwaysApply, description, etc.)

  10. What is the relative path from agent files to .packmind/standards/ directory?

Convention: Throughout all recipe steps below, NEW_AGENT / new_agent / NewAgent are placeholders for the identifier key (from prerequisite 1), and 'New Agent' is a placeholder for the display name. Substitute both consistently in every step.

Recipe Steps

Step 1: Add RenderMode enum value

Add the new AI agent to the RenderMode enum in packages/types/src/deployments/RenderMode.ts. Add the enum value and include it in the RENDER_MODE_ORDER array to ensure proper ordering. Position matters for supersedence: agents earlier in the array are overridden by agents later in the array when they share a file.

typescript
export enum RenderMode {
  // ... existing values
  NEW_AGENT = 'NEW_AGENT',
}

export const RENDER_MODE_ORDER: RenderMode[] = [
  // ... existing values
  RenderMode.NEW_AGENT,
];
Step 2: Add CodingAgent type

Add the new agent identifier to the CodingAgent union type in both packages/types/src/coding-agent/CodingAgent.ts and packages/coding-agent/src/domain/CodingAgents.ts. Also add it to the CodingAgents record object.

typescript
export type CodingAgent =
  | 'packmind'
  | 'junie'
  | 'claude'
  | 'cursor'
  | 'copilot'
  | 'agents_md'
  | 'gitlab_duo'
  | 'continue'
  | 'new_agent';

export const CodingAgents: Record<CodingAgent, CodingAgent> = {
  // ... existing values
  new_agent: 'new_agent',
};
Step 3: Add CodingAgentArtefactPaths

If the agent supports commands, standards, or skills as multi-file artifacts, add it to packages/types/src/coding-agent/CodingAgentArtefactPaths.ts:

  1. Add the agent to the MultiFileCodingAgent type union
  2. Add directory path mappings in the CODING_AGENT_ARTEFACT_PATHS record for each supported artifact type (command, standard, skill). Use empty string for unsupported artifact types.
typescript
export type MultiFileCodingAgent = Extract<CodingAgent, 'claude' | 'cursor' | ... | 'new_agent'>;

export const CODING_AGENT_ARTEFACT_PATHS: Record<MultiFileCodingAgent, { command: string; standard: string; skill: string }> = {
  // ... existing values
  new_agent: {
    command: '.new-agent/commands/',
    standard: '',  // empty if standards are single-file (embedded in a shared file)
    skill: '.new-agent/skills/',
  },
};
Step 4: Add to VALID_CODING_AGENTS

Add the agent identifier to the VALID_CODING_AGENTS array in packages/types/src/coding-agent/validation.ts. This enables validation of the agent string in configuration files.

typescript
export const VALID_CODING_AGENTS: CodingAgent[] = [
  // ... existing values
  'new_agent',
];

Update the corresponding test in validation.spec.ts to include the new agent in test fixtures.

Step 5: Add RenderMode to CodingAgent mapping

Add the mapping from RenderMode to CodingAgent in packages/types/src/deployments/RenderModeCodingAgentMapping.ts in the RENDER_MODE_TO_CODING_AGENT record.

typescript
export const RENDER_MODE_TO_CODING_AGENT: Record<RenderMode, CodingAgent> = {
  // ... existing mappings
  [RenderMode.NEW_AGENT]: CodingAgents.new_agent,
};
Step 6: Create Deployer class

Create a new deployer class in packages/coding-agent/src/infra/repositories/{agentName}/{AgentName}Deployer.ts. Choose a base class based on the agent's rendering pattern:

  • SingleFileDeployer: For agents that aggregate all standards into one file (e.g., AGENTS.md, CLAUDE.md). Can also be extended with multi-file methods for commands/skills (hybrid pattern).
  • MultiFileDeployer: For agents that create one file per standard (e.g., Cursor, Continue).
  • ICodingAgentDeployer: Implement directly for fully custom behavior.

For hybrid agents (single-file standards + multi-file commands/skills), extend SingleFileDeployer and override the artifact methods:

typescript
import { SingleFileDeployer } from '../singleFile/SingleFileDeployer';

export class NewAgentDeployer extends SingleFileDeployer {
  // Implement required methods:
  // - deployRecipes() - deploy standards/recipes content
  // - deploySkills() - deploy skill files to agent directories
  // - deployArtifacts() - deploy command files to agent directories
  // - generateRemovalFileUpdates() - handle artifact removal
  // - generateAgentCleanupFileUpdates() - handle full agent cleanup
}
Step 7: Implement frontmatter generation

In the deployer, implement frontmatter generation based on the agent's requirements. For Continue-style agents, include name, globs (if scope exists), alwaysApply (false if scope, true otherwise), and description (from summary or standard name). For Cursor-style agents, use simpler frontmatter with just globs and alwaysApply. For single-file agents, frontmatter may not be needed.

typescript
// For Continue-style (with name and description)
const frontmatter = standardVersion.scope && standardVersion.scope.trim() !== ''
  ? `---
name: ${standardVersion.name}
globs: ${standardVersion.scope}
alwaysApply: false
description: ${summary}
---`
  : `---
name: ${standardVersion.name}
alwaysApply: true
description: ${summary}
---`;

// For Cursor-style (simpler)
const frontmatter = standardVersion.scope && standardVersion.scope.trim() !== ''
  ? `---
globs: ${standardVersion.scope}
alwaysApply: false
---`
  : `---
alwaysApply: true
---`;
Step 8: Register deployer in registry

Register the new deployer in packages/coding-agent/src/infra/repositories/CodingAgentDeployerRegistry.ts by importing it and adding a case in the createDeployer switch statement. Also add the agent to the canCreateDeployer method.

If the agent supports multi-file artifacts (commands/skills), also add it to the isMultiFileAgent() check if that method exists.

typescript
import { NewAgentDeployer } from './newAgent/NewAgentDeployer';

private createDeployer(agent: CodingAgent): ICodingAgentDeployer {
  switch (agent) {
    // ... existing cases
    case 'new_agent':
      return new NewAgentDeployer(this.standardsPort, this.gitPort);
    default:
      // An unreachable switch arm is our bug, not the caller's: PackmindInternalError
      // keeps the 500 and the stack, puts `agent` in the log as a field, and withholds
      // the message from the response. A bare Error would do none of that.
      throw new PackmindInternalError(
        'unknown_coding_agent',
        { agent },
        `No deployer is registered for coding agent "${agent}".`,
      );
  }
}

private canCreateDeployer(agent: CodingAgent): boolean {
  return (
    // ... existing agents
    agent === 'new_agent'
  );
}
Show full SKILL.md (605 more words)Show less
Step 9: Add AgentConfiguration mapping

Add the new agent to the AGENT_FILE_PATHS record in packages/coding-agent/src/domain/AgentConfiguration.ts. This maps the agent to its main config/standards file path.

typescript
export const AGENT_FILE_PATHS: Record<CodingAgent, string> = {
  // ... existing mappings
  new_agent: 'AGENTS.md',  // or '.new-agent/rules/index.md', etc.
};

Note: If the agent shares a file path with another agent (e.g., both opencode and agents_md map to AGENTS.md), this is valid but requires supersedence handling in Step 11.

Step 10: Export deployer from package

Add the deployer export to packages/coding-agent/src/index.ts so it can be imported by other packages.

typescript
export * from './infra/repositories/newAgent/NewAgentDeployer';
Step 11: Handle shared-file supersedence (conditional)

Skip this step if the agent does not share a config file with another agent.

If two agents write to the same file (e.g., both OpenCode and agents_md write to AGENTS.md), add suppression logic in packages/coding-agent/src/application/DeployerService.ts to ensure only the higher-priority agent writes to the shared file when both are active.

This typically involves:

  1. A suppression method that filters out the lower-priority agent's writes to the shared file
  2. Applying suppression in both aggregateStandardsDeployments() and aggregateArtifactRendering()
  3. Adding tests for the dual-active scenario in DeployerService.spec.ts
Step 12: Update GitFileUtils

Add the new agent to the agentToFile record in packages/deployments/src/application/utils/GitFileUtils.ts. This maps the agent to its main file for git operations. If the file path is shared with another agent, the deduplication in fetchExistingFilesFromGit() handles avoiding duplicate fetches.

typescript
const agentToFile: Record<CodingAgent, string> = {
  // ... existing mappings
  new_agent: 'AGENTS.md',  // or the agent's main file
};

Ensure the relative path from agent files to .packmind/standards/{slug}.md is correct. For files in .continue/rules/ or .cursor/rules/packmind/, use ../../.packmind/standards/. For files at root level like CLAUDE.md, use .packmind/standards/. Adjust based on the actual directory structure.

typescript
// For .continue/rules/ or .cursor/rules/packmind/
link: `../../.packmind/standards/${standardVersion.slug}.md`

// For root-level files
link: `.packmind/standards/${standardVersion.slug}.md`
Step 14: CLI - Agent artifact detection

Add the agent's directory or file to the detection list in apps/cli/src/application/services/AgentArtifactDetectionService.ts. This allows the CLI to detect whether the agent is already set up in a repository.

typescript
const AGENT_ARTIFACT_CHECKS = [
  // ... existing entries
  { agent: 'new_agent', paths: ['.new-agent'] },
];

Add a corresponding test in AgentArtifactDetectionService.spec.ts.

Step 15: CLI - Config agents handler

Add the agent to the CLI's agent configuration in apps/cli/src/infra/commands/config/configAgentsHandler.ts:

  1. Add to SELECTABLE_AGENTS array (alphabetically sorted, Packmind always first/excluded)
  2. Add to AGENT_DISPLAY_NAMES record with a human-readable name
typescript
export const SELECTABLE_AGENTS: CodingAgent[] = [
  'agents_md', 'claude', 'continue', 'copilot', 'cursor', 'gitlab_duo', 'junie', 'new_agent',
];

export const AGENT_DISPLAY_NAMES: Record<CodingAgent, string> = {
  // ... existing entries
  new_agent: 'New Agent',
};

Add tests in configAgentsHandler.spec.ts confirming the agent is in SELECTABLE_AGENTS and has the correct display name.

Step 16: CLI - Parse standard

Add a parser entry for the agent in apps/cli/src/application/utils/parseStandardMd.ts:

  1. Add to AGENT_PARSERS record. For single-file agents that embed standards in a shared file (e.g., AGENTS.md), use () => null since individual standards can't be parsed from the file.
  2. If the agent uses multi-file standards, also add a pattern entry to DEPLOYER_PARSERS.
typescript
// In AGENT_PARSERS
export const AGENT_PARSERS: Partial<Record<CodingAgent, (content: string) => ParsedStandard | null>> = {
  // ... existing entries
  new_agent: () => null,  // single-file agent, cannot parse individually
};

// In DEPLOYER_PARSERS (only for multi-file standard agents)
export const DEPLOYER_PARSERS = [
  // ... existing entries
  { pattern: '.new-agent/rules/packmind-', parse: parseNewAgentStandard },
];
Step 17: Update frontend RenderingSettings

Add the new agent to apps/frontend/src/domain/deployments/components/RenderingSettings/RenderingSettings.tsx by adding entries to RENDER_MODE_TO_VALUE, VALUE_TO_RENDER_MODE, and DEFAULT_FORMATS arrays.

typescript
const RENDER_MODE_TO_VALUE: Record<RenderMode, string> = {
  // ... existing values
  [RenderMode.NEW_AGENT]: 'new-agent',
};

const VALUE_TO_RENDER_MODE: Record<string, RenderMode> = {
  // ... existing values
  'new-agent': RenderMode.NEW_AGENT,
};

const DEFAULT_FORMATS: RenderingItem[] = [
  // ... existing formats
  { value: 'new-agent', name: 'New Agent', checked: false },
];
Step 18: Update frontend RunDistributionBody

Add the new agent label to apps/frontend/src/domain/deployments/components/RunDistribution/RunDistributionBody.tsx in the renderModeLabels record.

typescript
const labels: Record<RenderMode, string> = {
  // ... existing labels
  [RenderMode.NEW_AGENT]: 'New Agent',
};
Step 19: Update frontend DeploymentsHistory

Add the new agent label to apps/frontend/src/domain/deployments/components/DeploymentsHistory/DeploymentsHistory.tsx in the formatNames record.

typescript
const formatNames: Record<RenderMode, string> = {
  // ... existing labels
  [RenderMode.NEW_AGENT]: 'New Agent',
};
Step 20: Update documentation files

Add the new agent to the documentation:

  1. apps/doc/docs/manage-ai-agents.mdx - Add a row to the agent support table showing file locations and supported features
  2. apps/doc/docs/artifact-rendering.mdx - Add the agent to the standards, commands, and/or skills tables as applicable
markdown
| **New Agent** | AGENTS.md + `.new-agent/` directories | Yes |
Step 21: Create unit tests

Create comprehensive unit tests in packages/coding-agent/src/infra/repositories/{agentName}/{AgentName}Deployer.spec.ts. Follow Packmind test standards: single expectation per test, assertive titles (no "should"), and nested describe blocks for workflows. Test:

  • Standards rendering (with and without scope, empty lists)
  • Commands deployment as individual files (if supported)
  • Skills deployment as multi-file directories (if supported)
  • Artifact removal scenarios
  • Agent cleanup operations
typescript
describe('NewAgentDeployer', () => {
  describe('deployRecipes', () => {
    describe('when deploying recipes', () => {
      it('creates one file update', async () => {
        // Single expectation test
      });
    });
  });
});
Step 22: Create integration tests

Create integration tests in packages/integration-tests/src/coding-agents-deployments/{agent-name}-deployment.spec.ts following the pattern of existing integration tests (e.g., cursor-deployment.spec.ts). Test the full deployment workflow through DeployerService, including file creation, frontmatter validation, and content verification. If the agent shares a file with another agent, test the dual-active supersedence scenario. Follow test standards with single expectations and nested describe blocks.

typescript
describe('New Agent Deployment Integration', () => {
  describe('when deploying standards', () => {
    it('creates the expected file', async () => {
      // Test through deployerService
    });
  });
});

© PackmindHub, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in packages/.agents/skills/adding-ai-agent-rendering-system of PackmindHub/packmind.

Open the folder on GitHubat commit 8a10541

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Questions about Adding AI Agent Rendering System

What does Adding AI Agent Rendering System do?

Implement a new Packmind AI agent rendering/deployer pipeline (single-file or multi-file) with type and registry wiring, frontend UI/docs updates, and thorough unit/integration tests to reliably…. Adding AI Agent Rendering System is an agent skill from PackmindHub/packmind. Implement a new Packmind AI agent rendering/deployer pipeline (single-file or multi-file) with type and registry wiring, frontend UI/docs updates, and thorough unit/integration tests to reliably support additional coding assistants and distribution formats when introducing a new agent integration or render mode.

When should I use Adding AI Agent Rendering System?

Adding AI Agent Rendering System fits situations like: tasks that involve Frontend development; tasks that involve Integration testing.

How do I install Adding AI Agent Rendering System in Claude Code?

Run `npx skills add PackmindHub/packmind --skill adding-ai-agent-rendering-system -a claude-code`. Or copy the skill folder (packages/.agents/skills/adding-ai-agent-rendering-system in PackmindHub/packmind) into .claude/skills/adding-ai-agent-rendering-system in your project. Claude Code loads it when a task matches its description.

How do I install Adding AI Agent Rendering System in Codex?

Run `npx skills add PackmindHub/packmind --skill adding-ai-agent-rendering-system -a codex`. Or copy the skill folder (packages/.agents/skills/adding-ai-agent-rendering-system in PackmindHub/packmind) into .agents/skills/adding-ai-agent-rendering-system in your project. Codex loads it when a task matches its description.

Can I use Adding AI Agent Rendering System in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add PackmindHub/packmind --skill adding-ai-agent-rendering-system -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/adding-ai-agent-rendering-system, .gemini/skills/adding-ai-agent-rendering-system, .github/skills/adding-ai-agent-rendering-system and .opencode/skills/adding-ai-agent-rendering-system in your project.

What does Adding AI Agent Rendering System need to run?

SKILL.md names no scripts, command-line tools or credentials: Adding AI Agent Rendering System is instructions for the agent only.

Does Adding AI Agent Rendering System access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Adding AI Agent Rendering System safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Adding AI Agent Rendering System use?

Adding AI Agent Rendering System is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Adding AI Agent Rendering System use?

About 4.5k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Adding AI Agent Rendering System?

Skills that share tags, products or a category with Adding AI Agent Rendering System: OpenHarness End-to-End Evals (HKUDS/OpenHarness, 16k stars), Doc Screenshots (WordPress/wordpress-playground, 2k stars), Drive MiMo Code (XiaomiMiMo/MiMo-Code, 14k stars) and Plugin Testing (polyipseity/obsidian-terminal, 948 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Adding AI Agent Rendering System?

PackmindHub (a GitHub organization) maintains it in PackmindHub/packmind, which has 317 GitHub stars. The repository holds 35 skills in this directory. The repository was last updated on October 8, 2026.

Source: PackmindHub/packmind on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.