Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hcompai/hai-agents-python --skill hai-agentsgit clone --depth 1 https://github.com/hcompai/hai-agents-pythonWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hcompai/hai-agents-python/hai-agents)<a href="https://agentmods.dev/skills/hcompai/hai-agents-python/hai-agents"><img src="https://agentmods.dev/badge/skills/hcompai/hai-agents-python/hai-agents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hcompai/hai-agents-python/hai-agents"><img src="https://agentmods.dev/badge/skills/hcompai/hai-agents-python/hai-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00102 | $0.00959 |
| Opus 5.5 | $0.00041 | $0.00384 |
| Sonnet 5.5 | $0.00020 | $0.00192 |
| Haiku 4.5 | $0.00010 | $0.00096 |
Grade A, and why
hai-agents scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 18d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
User: *"is the H Company Agent API quickstart still showing the curl example"* How it starts
The opening of the file, as written. The whole thing — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
hai-agents
The hai-agents MCP server runs H Company's autonomous agents on H Company's infrastructure and streams back a single answer. You hand an agent a self-contained task; it opens its own browse-and-act loop in a remote browser and works until it has an answer, hits its step or time budget, or is cancelled. Nothing runs on the user's machine.
The agent is blind to this conversation. It sees only the task string, so fold in the context it needs: which site or source, what "done" looks like, the shape of the answer you want back. Keep the user's own action verbs and any literal text (search queries, message bodies) verbatim, since the agent grounds well on natural imperatives. Paraphrase by adding context, not by rewording.
Use it for open-ended work on the live web: finding and extracting information across pages, filling in and submitting forms, multi-step navigation behind logins the agent can perform, and research that needs a real browser. It is the wrong tool when the answer is already in your knowledge or a single fetch, when the job is local file, shell, or code work, or when the user wants something written or explained in the conversation rather than done on the web.
Tools
list_agents(page?, size?): agents the caller can run (their org's plus the publich/ones). Pick theagentname from here.run_agent(task, agent, max_steps?, max_time_s?, idempotency_key?): start a run. Returns either the final answer or a session handle{ session_id, status, answer, done }.wait_for_session(session_id, wait=True): long-poll a running session for its answer;wait=Falsereturns the current snapshot without blocking.send_message(session_id, message): steer a running session with a follow-up.cancel_session(session_id): stop a run you no longer need.share_session(session_id): get a public read-only URL for the run.list_files(session_id, path="~/Downloads", ...),read_file(session_id, path),write_file(session_id, path, content_base64): files on the agent's browser machine, e.g. its downloads. These take the browser session id issued by env-manager, not the idrun_agentreturns.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 18d ago Changed · +1 lines 1794faf58f65
- 1mo ago Changed d23506a13612
- 1mo ago First seen · 46 lines · 102 tokens per session scan A 3391d279a9aa
hai-agents is a skill published in the GitHub repository hcompai/hai-agents-python (44 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 959 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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