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.
git clone --depth 1 https://github.com/eliornl/rolemuleWrote 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/rules/eliornl/rolemule/hiring-outreach-feature)<a href="https://agentmods.dev/rules/eliornl/rolemule/hiring-outreach-feature"><img src="https://agentmods.dev/badge/rules/eliornl/rolemule/hiring-outreach-feature/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/rules/eliornl/rolemule/hiring-outreach-feature"><img src="https://agentmods.dev/badge/rules/eliornl/rolemule/hiring-outreach-feature.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00000 | $0.01853 |
| Opus 5.5 | $0.00000 | $0.00741 |
| Sonnet 5.5 | $0.00000 | $0.00371 |
| Haiku 4.5 | $0.00000 | $0.00185 |
Grade A, and why
hiring-outreach-feature 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 1mo 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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hiring Outreach Feature
Standalone on-demand feature — not in the LangGraph workflow. Finds hiring contacts via public web signals (company websites, news, team pages) and drafts personalized outreach messages for a completed job application. Copy-only — no send integration, no professional networking profile URLs or site names in output.
Product summary
- Tab: 10th application-detail tab Outreach (
data-tab="outreach",#pane-outreach) — after Mock Session - BYOK:
require_user_llm_context()→CFG_6001when user is not LLM-ready - Prerequisite:
workflow_session.job_analysismust exist (workflow completed at least Job Analyzer) - UX: Same family as Optimize CV / Mock Session — empty-state CTA, AI Setup warning, progress spinner, results with Copy buttons per draft (email subject+body, short message)
- No LinkedIn: Agent strips networking profile URLs; prompts forbid citing professional networking sites by name
Standalone Agent (NOT in LangGraph workflow)
HiringOutreachAgent in agents/hiring_outreach.py is invoked on-demand from api/hiring_outreach.py via BackgroundTasks.
agent = HiringOutreachAgent()
result = await agent.generate(
job_analysis=workflow_session.job_analysis or {},
company_research=workflow_session.company_research or {},
profile_matching=workflow_session.profile_matching or {},
user_profile=workflow_session.user_data or {},
user_api_key=user_api_key,
model=preferred_model,
llm_provider=llm_provider,
)
Grounding: settings.hiring_outreach_grounding_enabled (default True). Uses use_google_search_grounding=True on generate() when provider supports web search and company name is usable (_has_usable_company_name). Ollama → grounding off. On grounded-call failure, agent retries once without grounding (_generate_with_grounding_failover). Skips grounding when employer name is a placeholder (unnamed-posting mode).
Output: Up to 4 contacts (role_type: hiring_manager | recruiter | team_peer | generic; confidence: high | medium | low). Generic fallback drafts when no credible contacts found. No bracket placeholders; no invented personal emails.
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.
- 1mo ago First seen · 170 lines · 0 tokens per session scan A 6e4b8df55c5c
hiring-outreach-feature is a cursor rule published in the GitHub repository eliornl/rolemule (38 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,853 tokens. 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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