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 zeenie-ai/OpenCompany --skill advisorgit clone --depth 1 https://github.com/zeenie-ai/OpenCompanyWrote 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/zeenie-ai/opencompany/advisor)<a href="https://agentmods.dev/skills/zeenie-ai/opencompany/advisor"><img src="https://agentmods.dev/badge/skills/zeenie-ai/opencompany/advisor/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/zeenie-ai/opencompany/advisor"><img src="https://agentmods.dev/badge/skills/zeenie-ai/opencompany/advisor.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 | $0.00080 | $0.00562 |
| Opus 5 | $0.00040 | $0.00281 |
| Sonnet 5 | $0.00016 | $0.00112 |
| Haiku 4.5 | $0.00008 | $0.00056 |
Grade A, and why
advisor scanned grade A with 0 findings 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 11d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Advisor
A chat-model wired as a tool is your advisor — a stronger model configured by the operator. Consult it for strategy, not implementation.
When to call
- AT TASK START — before writing, editing, or committing to an interpretation, ask for an approach. Orientation (reading files, fetching sources) is not substantive work; do that first, then call advisor.
- WHEN STUCK — errors recurring, approach not converging, results that don't fit.
- BEFORE DECLARING DONE — sanity-check completeness. Make your deliverable durable first (save the file, commit the change).
On short reactive tasks dictated by tool output you just read, skip subsequent calls — the advisor adds most of its value on the first call, before the approach crystallizes.
How to call
- One focused question per call. The advisor has no memory of prior calls.
- Pass your question in
prompt. Do NOT setmodelorapi_key— the operator configured them. - Include relevant context inside
prompt(what you tried, what you observed). The advisor does not see your tool calls or memory.
How to treat the response
- Tactical guidance, not a full solution. You do the work.
- Give the advice serious weight. If a step fails empirically, surface the conflict in a follow-up call ("I tried X, you suggested Y; here's the result — which constraint breaks the tie?").
- A passing self-test is not evidence the advice is wrong.
Operator note (configuring the advisor node)
- Model: pick the provider's strongest current model. As of May 2026:
claude-opus-4-7,gpt-5.5-pro-2026-04-23,gemini-3.1-pro-preview. - System prompt (optional): "You are an advisor. Brief tactical guidance only — identify pitfalls, suggest changes, validate the plan. Do NOT write full solutions."
- Cost: advisor models are 3-10× the executor's per-token cost. Use sparingly.
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.
- 11d ago First seen · 40 lines · 80 tokens per session scan A 8589469e71f0
advisor is a skill published in the GitHub repository zeenie-ai/OpenCompany (880 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 562 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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acp-router
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coding-agent
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
control-ui
Operate and troubleshoot the OpenClaw Control UI: navigate connected clients, organize sessions, build session dashboards, and handle direct or Tailscale-hosted Gateways.
gh-issues
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.