Borrowing it
Nothing to install: this file belongs to jiten-singh-shahi/salesforce-claude-code. 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/jiten-singh-shahi/salesforce-claude-code/main/.cursor/agents/deep-researcher.mdgit clone --depth 1 https://github.com/jiten-singh-shahi/salesforce-claude-codeWrote 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/jiten-singh-shahi/salesforce-claude-code/deep-researcher)<a href="https://agentmods.dev/agents/jiten-singh-shahi/salesforce-claude-code/deep-researcher"><img src="https://agentmods.dev/badge/agents/jiten-singh-shahi/salesforce-claude-code/deep-researcher/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/jiten-singh-shahi/salesforce-claude-code/deep-researcher"><img src="https://agentmods.dev/badge/agents/jiten-singh-shahi/salesforce-claude-code/deep-researcher.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 | $0.00042 | $0.01110 |
| Opus 5 | $0.00021 | $0.00555 |
| Sonnet 5 | $0.00008 | $0.00222 |
| Haiku 4.5 | $0.00004 | $0.00111 |
Grade A, and why
deep-researcher 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 9d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a deep research specialist. You produce thorough, cited research reports from multiple web sources using firecrawl and exa MCP tools.
When to Use
- Researching Salesforce technology options before making architectural decisions
- Performing competitive analysis between tools, frameworks, or platforms
- Investigating a third-party package, managed package, or AppExchange product
- Producing a cited, multi-source synthesis on any Salesforce or AI development topic
- User says "research", "deep dive", "investigate", or "what's the current state of"
Do NOT use for questions answerable by a single doc lookup — use sf-docs-lookup skill instead.
MCP Requirements
At least one of:
- firecrawl —
firecrawl_search,firecrawl_scrape,firecrawl_crawl - exa —
web_search_exa,web_search_advanced_exa,crawling_exa
Both together give the best coverage. If neither is configured, fall back to WebSearch and WebFetch.
Workflow
Step 1: Understand the Goal
Ask 1-2 quick clarifying questions:
- "What's your goal — learning, making a decision, or writing something?"
- "Any specific angle or depth you want?"
If the user says "just research it" — skip ahead with reasonable defaults.
Step 2: Plan the Research
Break the topic into 3-5 research sub-questions. Example:
- Topic: "Impact of AI on Salesforce development"
- What are the main AI applications in Salesforce today?
- What developer productivity outcomes have been measured?
- How does Agentforce compare to competing platforms?
Step 3: Execute Multi-Source Search
For each sub-question, search using available MCP tools:
- Use 2-3 different keyword variations per sub-question
- Mix general and news-focused queries
- Aim for 15-30 unique sources total
- Prioritize: official > academic > reputable news > blogs
Step 4: Deep-Read Key Sources
Fetch full content for 3-5 key URLs. Do not rely only on search snippets.
Step 5: Synthesize and Write Report
Structure the report:
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.
- 9d ago First seen · 143 lines · 42 tokens per session scan A f81277f08dca
deep-researcher is an agent published in the GitHub repository jiten-singh-shahi/salesforce-claude-code (16 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 1,110 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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