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CLI Tool for AI Agent Management, Skills, Agent, MCP, API Registry, Benchmarks and Hints in AI Agent Marketplace\n","maintainers":[{"name":"deepnlp","email":"deepnlp.contact@gmail.com"}],"readme":"\n### agtm: CLI Tool for AI Agent Management, Skills, Agent Registry, Benchmarks and Hints in AI Agent Marketplace\n\n[GitHub](https://github.com/aiagenta2z/agtm)|[AI Agent Marketplace CLI Doc](https://www.deepnlp.org/doc/ai_agent_marketplace)|[DeepNLP AI Agent Marketplace](https://www.deepnlp.org/store/ai-agent) | [OneKey Gateway](https://deepnlp.org/doc/onekey_gateway) | [Agent MCP OneKey Router Ranking](https://www.deepnlp.org/agent/rankings) | [NodeJS agtm](https://www.npmjs.com/package/@aiagenta2z/agtm)\n\n`agtm` (AI Agent Manager CLI) unifies skill management, agent registration, marketplace search, and provider CLI execution. Install skills from GitHub, log and rate skill runs, upload agent metadata to registries, query the public marketplace, and run agent toolchains with fuzzy hints.\n\n## Features\n\n**agtm skills**: Manage Skills, Add Skills, List Skills, Log Skills Performance, Skills performance Evaluator, compare to realworld benchmarks   \n**agtm upload**: AI Agent Registry, register local agent meta information of json or yaml format(agent.json/agent.yaml) or sync your github source meta including README.md     \n**agtm search**: Search the open source AI Agent Marketplace, including github community, huggingface community, product hunt community, deepnlp ai agent marketplace index, etc     \n**agtm run**: Run agent clis, don't need to remember, with the powerful hints and completion ability, just type a few characters and \"--hint\" will help you complete the command line.   \n\n\n### Live Update\n**20260407**: Agtm Hints now works with any npm-registered package, automatically converting CLI help output into structured, highlighted hints—e.g., for example @larksuite/cli, you \ncan quickly find \"message\" related clis using \"agtm run @larksuite/cli message\" and it will guide you to the clis, such as \"lark-cli im messages-reply\"\n\n\nThe agent cli run hint from \"play\" to \"playwright\" complete cli.\n\n<img src=\"docs/static/snapshot_playwright_google.png\" width=\"400px\" />\n\nFurthermore, `agtm` provides memory to track skill outputs and enables performance rating against industry job level benchmarks. This allows you to score each skill execution and assign a professional tier to your AI Agent's capabilities—for example, evaluating its performance as equivalent to that of an L3 or L5 software engineer, marketing professional, etc.\n\n```shell\nskill_id             run_times  score  level\n-------------------  ---------  -----  -----\ncode_success_skills  5          0.9     L3(100%)    \ncode_fail_skills  4          0.9     L3(100%)    \n```\n\n## Quickstart\n\n###  Installation\n\n**Node**\n``` NodeJS\nnpm install -g @aiagenta2z/agtm\n```\n\n### Run a playwright Webpage cli\nSetup hint and skills benchmark \n```shell\nnpx agtm setup --levels  ## Needed before `agtm rate`, to sync the benchmarks json to local folder \nnpx agtm setup --hint    ## Needed before `agtm run` \n```\n\n```shell\nnpm install -g @playwright/cli@latest\nagtm run play\n### hint to \"playwright-cli goto\" command line\n```\n\nResult Hint\n```shell\nSelected Skill/Cli is microsoft/playwright-cli\n\nEnter command to run (leave empty to list cli hints): play\n\nComplete the Cli with your arguments or leave blank and press Enter\n\nFinal command line [playwright-cli goto <url>]:\nplaywright-cli goto https://www.google.com\nagtm run microsoft/playwright-cli playwright-cli goto https://www.google.com\n\n### Browser `default` opened with pid 4603.\n- default:\n  - browser-type: chrome\n  - user-data-dir: <in-memory>\n  - headed: false\n---\n\n### Ran Playwright code\nawait page.goto('https://www.google.com');\n\n\n### Page\n- Page URL: https://www.google.com/\n- Page Title: Google\n### Snapshot\n- [Snapshot](.playwright-cli/page-2026-03-22T03-08-05-614Z.yml)\n```\n\n\n## Agtm CLI Options\n\n| CLI         | Command and Options                       | Document                       |\n|-------------|-------------------------------------------|--------------------------------|\n| agtm skills | add, list, build, log, rate               | [Doc](./docs/skills/README.md)  |\n| agtm search | --q query                                 | [Doc](./docs/registry/README.md) |\n| agtm upload | --github  --config to local agent meta    | [Doc](./docs/registry/README.md) |\n| agtm run    | --hint agent-cli hint and auto completion | [Doc](./docs/run/README.md)     |\n\n## `skills`  \n\n### skills add\n\nDownload and add skills to your agent directory.\n\n#### Usage\n```\nnpx agtm skills add <unique_id>\nnpx agtm skills add <github_url>\nnpx agtm skills add <github_url> -a <agent_id>\nnpx agtm skills add <owner_id/repo_id> -s <skill_id>\n```\n\n#### Example\n```\nnpx agtm skills add anthropics/skills -a claude-code  ## install skills only to claude-codex\nnpx agtm skills add msitarzewski/agency-agents\nnpx agtm skills add aiagenta2z/onekey-gateway\nnpx agtm skills add msitarzewski/agency-agents -s academic-anthropologist -a codex\nnpx agtm skills add anthropics/skills -s skill-creator -a claude-code --global\n```\n\n### skills list\n\n#### Usage\n```\nnpx agtm skills list\nnpx agtm skills list --agent <agent_id> --global\n```\nLists installed skills with `agent`, `skill_id`, `description`, install `path`, average `score`, and aggregated `level` (if ratings exist).\n\n\n\n#### Example\n```\nnpx agtm skills list\nnpx agtm skills list --agent codex\nnpx agtm skills list --agent claude-code --global\n```\n\n\n### skills build\nConvert registered Agentic API to skills easily. Before running the build command, make sure \nthe unique_id agent and API meta infos are registered in AI Agent Marketplace using agtm upload.\n\n#### Usage Example\n```\nagtm skills build <unique_id>\n\n# build skills for food calories app\nagtm skills build fdcnal/usda-fooddata-central-agent\n# or \nagtm skills build aiagenta2z/financeagent\n\n```\n\n### skills log\n\n#### Usage\n```\nnpx agtm skills log <skill_id> --data '<json_payload>'\n```\n\n- Persists a run record at `.agtm/skills/log/<uuid>.json` (or the `--logDir` you supply).\n- `<json_payload>` must contain at least `input` and `output`; optional fields (meta, rating, level) are accepted.\n\n#### Example\n```\nnpx agtm skills log <skill_id> --data '{\"input\":\"write a website for store\",\"output\":\"success\"}'\nnpx agtm skills log code_success_skills --data '{\"input\":\"generate sql\",\"output\":\"ok\",\"meta\":{\"agent\":\"claude-code\"}}'\n```\n\n### skills rate\n\n#### Setup\n\nTo use the rate command, have to setup the benchmark levels configuration. save to `./agtm/levels/*.json` files\n```shell\nnpx agtm setup --levels\n```\n\n#### Usage\n```\nnpx agtm skills rate prepare --skill_id <skill_id> --prompt \"<eval_prompt>\" --benchmark <path/benchmark.json>\nnpx agtm skills rate apply   --skill_id <skill_id> --result '<result_json>'\nnpx agtm skills rate show    --skill_id <skill_id>\n```\n- `prepare` exports logs plus the top benchmark slices (e.g., Google SWE L3–L7) for an external evaluator.\n- `apply` writes evaluator outputs (`rating`, `level`) back to each log.\n- `show` summarizes run counts, average score, and level distribution.\n\n#### Example\n```\nnpx agtm skills rate prepare --skill_id code_success_skills --prompt \"Evaluate the results\" --benchmark path/customized_agent_benchmark.json\nnpx agtm skills rate apply --skill_id code_success_skills --result '{\"results\":[{\"log_id\":\"3679a3fe-4d97-4eb1-83bc-f83d711be195\",\"rating\":0.90,\"level\":\"L4\"}]}'\nnpx agtm skills rate show --skill_id code_success_skills\n```\nSample output:\n```\nskill_id             run_times  score  level\n-------------------  ---------  -----  -----\ncode_success_skills  3          0.88   L4(100%)\n```\nBenchmarks live in `data/config/levels/*.json` and follow this structure:\n```\n{\n  \"software-engineering\": {\n    \"Google\": [\n      { \"level\": \"L3\", \"title\": \"Software Engineer II\", \"description\": \"Entry-level engineer. Delivers well-scoped tasks with guidance.\", \"signals\": [\"task execution\",\"learning velocity\",\"code quality basics\"] },\n      { \"level\": \"L4\", \"title\": \"Software Engineer III\", \"description\": \"Independent contributor. Owns small features end-to-end.\", \"signals\": [\"ownership\",\"code quality\",\"debugging ability\"] }\n    ]\n  }\n}\n```\nThe evaluator compares each `<input,output>` log to the benchmark definitions and assigns a rating. You can customize levels such as `poor`, `fair`, `good`, `excellent` in your benchmark file.\n\nPartial List of Skills and Job Level Description \n\n| Job Category      | Company | Levels Description (e.g. Google L3, Meta E4)                                              |\n|-------------------|---------|-------------------------------------------------------------------------------------------|\n| Marketing         | Apple   | [marketing_sales_apple_levels.json](data/config/levels/marketing_sales_apple_levels.json) |\n| Software Engineer | Google  | [software_engineer_google_levels.json](data/config/levels/software_engineer_google_levels.json)                                                                                         |\n| Software Engineer | Meta    | [software_engineer_meta_levels.json](data/config/levels/software_engineer_meta_levels.json)                                            |\n\n\nwrite your `customized_agent_benchmark.json` following the formats\n\n```\n{\n  \"domain\": {\n    \"my_benchmark\": [\n      { \"level\": \"poor\", \"description\": \"the skill failed or the output is meaningless\"},\n      { \"level\": \"fair\", \"description\": \"the skills produces fair results, complete the task\"},\n      { \"level\": \"good\", \"description\": \"the skills output a report, an image is good in real life standards..\"},\n    ]\n  }\n}\n```\n\n\n## `Run`\n\nExecution of Agent Cli with hints and auto completion.\n\nThe `run` command executes agent workflows with interactive hints and fuzzy CLI completion. Typing a few characters surfaces suggested commands so you can finish the full invocation quickly (for example, typing `play` will suggest the Playwright runner).\n\nLet's say you want to run an agent command of Playwright to go to a URL and fetch a webpage. You don't need to remember the full command—type `play`, pick the provider, then pick the CLI action.\n\n\n### Usage\n\nRemember to setup hint before running the agent-cli\n\n```shell\nagtm setup --hint\n```\n\n```\nagtm run <provider_unique_id> <agent_cli>\n```\n\n### Cli Examples\n\n```shell\nagtm run play\nSkill ID suggestions:\n  1. microsoft/playwright-cli\n  2. googleworkspace/cli\nSkill ID suggestions: 1\nCommand hints:\n  1. playwright-cli goto <url> # navigate to a url\n  2. playwright-cli open [url] # open browser, optionally navigate to url\n\nSelect command (number or input custom): 1\nFinal command [playwright-cli goto <url>]: playwright-cli goto https://www.github.com\nagtm run microsoft/playwright-cli playwright-cli goto https://www.github.com\n```\n\nSupport CLI List, Please welcome to contrib\n\n| unique_id | agent cli |\n| --- | --- |\n| microsoft/playwright-cli | playwright-cli open [url], playwright-cli goto <url>\n| googleworkspace/cli | gws drive files list --params\n| aiagenta2z/onekey-gateway | onekey agent <unique_id> <api_id> <data_json|@file>, onekey mcp <server_name> [--name config_name], onekey llm --provider <provider> --model <model> --messages <json|@file> [--temperature <num>] [--response-format <format>] [--options <json|@file>], onekey llm --payload <json|@file>\n| openai/codex-cli | codex, codex exec \"[instruction]\"\n| anthropic/claude-code | claude \"[prompt]\", claude --dangerously-skip-permissions\n| paul-gauthier/aider | aider --model [model_name], /test [command]\n| openinterpreter/open-interpreter | interpreter, interpreter --os\n| google-gemini/gemini-cli | gemini, gemini -p \"[prompt] @[file/dir]\", gemini --yolo, /memory add \"[fact]\", /mcp list, /restore\n\n#### npmjs registered packages\n\nAgtm Hints now works with any npm-registered package, automatically converting CLI help output into structured, highlighted hints—e.g., for example @larksuite/cli.\n\n```shell\n## hints on all the commands of the package\nagtm run @larksuite/cli\n\n## Search all the message related clis \nagtm run @larksuite/cli message  \n```\n\nResult\n```shell\nnpx agtm run @larksuite/cli message \n\nINFO: Updating CLI Hints for Package @larksuite/cli...\n\nCommand hints:\n  1. lark-cli im messages-resources-download Download images/files from a message; user/bot; downloads image/file resources by message-id and file-key to a safe relative output path\n  2. lark-cli mail user_mailbox.message.attachments user_mailbox.message.attachments operations\n  3. lark-cli im messages-search # Search messages across chats (supports keyword, sender, time range filters) with user identity; user-only; filters by chat/sender/attachment/time, enriches results via mget and chats batch_query\n  4. lark-cli im messages-reply # Reply to a message (supports thread replies) with bot identity; bot-only; supports text/markdown/post/media replies, reply-in-thread, idempotency key\n  5. lark-cli im messages-mget # Batch get messages by IDs; user/bot; fetches up to 50 om_ message IDs, formats sender names, expands thread replies\n```\n\n## AI Agent Registry\n### `search`\n`agtm search` helps to search AI Agent MCP and skills marketplace by id or query keywords.\n\nExample Usage\n```shell\nagtm search --q 'coding agent'\nagtm search --id 'google-maps/google-maps'\n```\n\n### `upload`\n`agtm update` helps to update your local agent.json or agent.yaml meta information to DeepNLP AI Agent Marketplace Index.\nExample Usage\n```shell\nagtm upload --github https://github.com/AI-Hub-Admin/My-First-AI-Coding-Agent\nagtm upload --config ./agent.json\nagtm upload --config ./agent.yaml\n```\n\n`schema.json` should have two keys defined the required fields and optional fields you want to submit from the agent.json file. \n\nRemember to keep the `access_key` in safe place, the post request will post the `access_key` as well as schema to the endpoint. \n\nPlease visit the command line github package [agtm](https://github.com/aiagenta2z/agtm) and [DOC](https://www.deepnlp.org/doc/ai_agent_marketplace) detailed usage.\n\n\nUse the test account and access \n\n```\nexport AI_AGENT_MARKETPLACE_ACCESS_KEY=\"TEST_KEY_AI_AGENT_REGISTRY\"\n\nagtm upload --config ./agent.json --endpoint https://www.deepnlp.org/api/ai_agent_marketplace/registry --schema ./schema.json\n\nagtm upload --config ./agent.json --endpoint https://www.aiagenta2z.com/api/ai_agent_marketplace/registry --schema ./schema.json\n```\n\n\n\n### Skills Agtm-Cli\n\nWe provide Skills repo to use in various agents to evaluate skills and run agent hints.\nThe skills can be found in ./skills/ folder\n\n| skill | description |\n| ---- | ---- |\n| agent-cli-hint-completion | This skill uses `agtm run --mode agent` to help hint agents clis usage |\n| agent-skills-evaluator | This skill use `agtm skills log` and `agtm skills rate` to track other skills performance from LLM-based evaluator, match to professional Job Level Benchmarks, such as Google L3 level software engineers/ Apple M3 level marketing specialist performance. |\n\n```shell\nnpx agtm skills add aiagenta2z/agtm ## install all the skill evaluation and skill cli-hints\nnpx agtm skills add aiagenta2z/agtm -s agent-skills-evaluator\n```\n\n```shell\nnpx skills add aiagenta2z/agtm ## install all the skill evaluation and skill cli-hints\nnpx skills add aiagenta2z/agtm -s agent-skills-evaluator\n```\n\n### Contributing\n\n#### Agent CLI List\nYou are welcome to contrib your cli list to the agent cli hints json file to folder [hints](data/config/hints) or table README.md\n\n#### Skill Run Benchmark \n\nYou are welcome to contrib your own customized benchmarks of skills and unique levels system in folder [levels](data/config/levels). \n\n","readmeFilename":"README.md"}