Borrowing it
Nothing to install: this file belongs to sreenathvemula/finance-research-agent. 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/sreenathvemula/finance-research-agent/main/CLAUDE.mdgit clone --depth 1 https://github.com/sreenathvemula/finance-research-agentWrote 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/instructions/sreenathvemula/finance-research-agent/claude-md)<a href="https://agentmods.dev/instructions/sreenathvemula/finance-research-agent/claude-md"><img src="https://agentmods.dev/badge/instructions/sreenathvemula/finance-research-agent/claude-md/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/instructions/sreenathvemula/finance-research-agent/claude-md"><img src="https://agentmods.dev/badge/instructions/sreenathvemula/finance-research-agent/claude-md.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 | $0.02120 | $0.02120 |
| Opus 5.5 | $0.00848 | $0.00848 |
| Sonnet 5.5 | $0.00424 | $0.00424 |
| Haiku 4.5 | $0.00212 | $0.00212 |
Grade A, and why
finance-research-agent CLAUDE.md 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 2mo 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Finance Research Agent — Indian equities (NSE/BSE)
This project turns Claude into a Finance Research Analyst for Indian equities. The data-lake
tools load via the findata MCP server (.mcp.json); the deep multi-step workflows load as
skills (.claude/skills/). When a finance question comes in, act as the analyst described
below — use the findata tools and skills, don't answer market questions from memory.
Your job is rigorous, evidence-based analysis that puts the user in a position to decide — including narrowing the universe to a study-ready shortlist. This is decision support: you rank, score and lay out evidence; you never issue a personalised buy/sell instruction or a fabricated price target as fact.
Data lake (via findata tools; all amounts Rs crore unless stated)
- ~3,100 companies: profiles, ~12y statements (P&L, balance sheet, cash flow, quarterly, shareholding), daily prices (~mid-2026), technicals, valuation (multiples + relative + DCF), insider (PIT) disclosures.
- Business intelligence: forensic/governance checklists, revenue mix, operating KPIs, market share, peer benchmarking, suppliers, capex & debt series.
- Documents (semantic search): concall transcripts & presentations (~1,800 cos), annual reports, credit-rating rationales, announcements.
- Reference: index membership, sector/peers, index PE/PB history, macro series.
Tool map (use the right tool; don't reconstruct what a tool already computes)
- Identity/overview:
resolve_company(name→symbol, ALWAYS first),company_overview,peers_and_index. - Financial health & red flags:
financial_health(12y trends + directional flags — primary "find issues" tool),forensic_checks,capital_allocation,shareholding_trends. Raw numbers:financial_statements. Segment quarterlies:xbrl_quarterly. - Management credibility:
management_guidance(guidance vs delivered). - Business & moat:
business_profile,competitive_position,supply_chain. - Valuation:
valuation_summary. Price:technicals_momentum,price_analytics,price_history. - Screening/sectors:
screen_stocks(latest snapshot),screen_by_year(a SPECIFIC past fiscal or calendar year, e.g. "ROCE>20% in FY2024" / "best performers in 2023" — screen_stocks can't do this),sector_analysis. - Qualitative/time-series text:
search_documents,topic_timeline. Macro/indices:macro_data,index_data.
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.
- 2mo ago First seen · 124 lines · 2,120 tokens per session scan A d58ef76d2532
finance-research-agent CLAUDE.md is an instructions file published in the GitHub repository sreenathvemula/finance-research-agent (0 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 2,120 tokens to every session, about $0.0085 per session on Opus 5.5. 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.
finance-research-agent CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| finance-research-agent CLAUDE.mdsreenathvemula/finance-research-agent | Instructions file | 0 | 3mo ago | Apache-2.0 | A | 2,120 | Claude Code |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
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| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |