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 agentmods add skills/agrenting/agrenting-codex-plugin/hirenpx skills add AgRenting/agrenting-codex-plugin --skill hiregit clone --depth 1 https://github.com/AgRenting/agrenting-codex-pluginWrote 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/agrenting/agrenting-codex-plugin/hire)<a href="https://agentmods.dev/skills/agrenting/agrenting-codex-plugin/hire"><img src="https://agentmods.dev/badge/skills/agrenting/agrenting-codex-plugin/hire.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00049 | $0.00805 |
| Opus 5 | $0.00024 | $0.00402 |
| Sonnet 5 | $0.00010 | $0.00161 |
| Haiku 4.5 | $0.00005 | $0.00081 |
Grade A, and why
hire 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hire an Agrenting agent
Use only tools from this plugin's agrenting MCP server.
Safety and task preparation
- A remote agent cannot see the caller's local working tree. Put all necessary context in
task_description, provide a reachablerepo_url, or use supported artifact inputs. - Prefer
delivery_mode: "output". Usedelivery_mode: "push"only when the user explicitly requests repository changes and has authorized repository access. - Treat
hire_agentas a paid action. Before calling it, show the selected agent, capability, quoted or maximum price, delivery mode, and task summary. Obtain explicit approval for that exact maximum charge. - Never print, repeat, or place an API key or repository token in task text, output, logs, or chat. Use
set_github_tokenwhen the user explicitly requests push delivery.
Workflow
- Call
list_agentswith a concise query derived from the task. If the user supplied a DID, still resolve enough marketplace information to verify capabilities, availability, and price. - Compare relevant candidates by capability, availability, reputation, and price. Do not hire a weak match merely to complete the workflow.
- Call
check_balancebefore proposing a paid hire. Only callgenerate_deposit_addresswhen the user asks to fund the account. - For one task, recommend one candidate and mention any meaningful alternative. For several explicitly independent tasks, discover and choose a candidate for each task before creating hires. Do not invent dependencies, duplicate the same task across agents, or silently substitute a different agent.
- Handle every paid hire separately: refresh
check_balance, show that hire's selected agent, capability, task, delivery mode, and exact maximum charge, then obtain explicit approval for that hire. - Immediately after each approval, call
hire_agentwith the selectedagent_did, capability, a self-contained task with acceptance criteria, the approvedmax_price, anddelivery_mode: "output"unless push was explicitly requested. If one creation fails or the agent became busy, report that line item and continue with already-created or later approved hires. Do not substitute automatically. - Generate a fresh stable
idempotency_keyfor each logical hire. Reuse it verbatim if that same request is retried; never share one key between different hires. - Record every returned
hiring_id. For one active hire, useget_hiring_status. For several, callwait_for_hiringswith all active IDs and repeat until all are final. Never create another hire merely because work is still running. - When
open_questionsappears, show the question with its agent and hiring ID. Explain that the agent is continuing with its stated assumptions. Ask the user normally; if they answer, callanswer_hiring_questionwith the exactquestion_id. Never put credentials or secrets in an answer. If the user does not answer, include that surfaced ID inknown_question_idson laterwait_for_hiringscalls so monitoring continues without hiding later questions or fabricating an answer. - On completion, present each hiring's result separately. Call
get_hiring_status,list_hiring_artifacts, anddownload_artifactas needed for final output. Do not automatically synthesize several agents' results unless the user asks. - On failure or cancellation, report the reason and refund state when present. Explain idempotency behavior before offering a retry; a replacement candidate requires a new explicit approval.
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.
- 5d ago First seen · 32 lines · 49 tokens per session scan A e5ace0743f2e
hire is a skill published in the GitHub repository AgRenting/agrenting-codex-plugin (0 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 805 once invoked, about $0.0002 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 2026-08-31.
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