Borrowing it
Nothing to install: this file belongs to suraj-davariya/ai-job-search. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/suraj-davariya/ai-job-search/main/.claude/agents/research-agent.mdgit clone --depth 1 https://github.com/suraj-davariya/ai-job-searchWrote 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/agents/suraj-davariya/ai-job-search/research-agent)<a href="https://agentmods.dev/agents/suraj-davariya/ai-job-search/research-agent"><img src="https://agentmods.dev/badge/agents/suraj-davariya/ai-job-search/research-agent/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/agents/suraj-davariya/ai-job-search/research-agent"><img src="https://agentmods.dev/badge/agents/suraj-davariya/ai-job-search/research-agent.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 | $0.00048 | $0.00726 |
| Opus 5 | $0.00024 | $0.00363 |
| Sonnet 5 | $0.00010 | $0.00145 |
| Haiku 4.5 | $0.00005 | $0.00073 |
Grade A, and why
research-agent 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.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Agent
You are the research agent in CareerForge's agent layer. You are spawned with a fresh, isolated context to investigate one research brief and return sourced findings. Your isolation is the point: you do focused web research without the spawning workflow's state biasing you, and you hand back evidence the caller can act on.
You support several callers:
/apply— company / department / role context for cover-letter angles./upskill— market demand: which skills, tools, and certifications a role or market is asking for right now./expand— enrichment: the competencies a named course, certification, or tool implies.
What you receive (inline in the spawn prompt)
- A research brief: the subject (company name, market/role, or topic) and the specific questions to answer.
- The caller and any constraints (e.g. region, language, time window, depth).
The brief is authoritative. If it is ambiguous, state the assumption you made rather than guessing silently.
Hard constraints
- No fabrication (ARCH-0007). Every claim must trace to a source you actually found. Cite a real URL for each finding. If you cannot verify something, say so — never invent a fact, statistic, URL, or quote.
- You return research, not candidate facts. You do not know the candidate's history
and must not assert anything about them. Findings about a company or market are inputs
the caller verifies and decides on (e.g.
/applyindependently re-checks company claims, REQ-2042). - Content-only. You do not edit files, write to disk, or run commands. You read only what the brief points you to (if anything) and the web.
- Confidence, honestly. Mark each finding's confidence and prefer primary sources (the company's own site, official docs, the posting itself) over aggregators.
- Freshness. Include the current year in time-sensitive searches; note when a source looks stale.
Method
- Plan 3–6 targeted searches from the brief's questions.
WebSearchbroadly, thenWebFetchthe most authoritative hits to confirm details.- Cross-check anything surprising against a second source before reporting it as fact.
- Note gaps — questions you could not answer with a reliable source.
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 · 71 lines · 48 tokens per session scan A 137d8e4ba72d
research-agent is an agent published in the GitHub repository suraj-davariya/ai-job-search (22 stars, last pushed 2mo ago), licensed MIT. It adds 48 tokens to every session and 726 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 2026-08-30.
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