ValueCell is a community-driven platform where multiple AI agents support financial activities such as stock research, market analysis, portfolio tracking, and trading. It is for people and developers working with equity and cryptocurrency markets, with sensitive information kept locally on the user's device.
Borrowing it
Nothing to install: this file belongs to ValueCell-ai/valuecell. 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/ValueCell-ai/valuecell/main/AGENTS.mdgit clone --depth 1 https://github.com/ValueCell-ai/valuecellWrote 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/valuecell-ai/valuecell/agents-md)<a href="https://agentmods.dev/instructions/valuecell-ai/valuecell/agents-md"><img src="https://agentmods.dev/badge/instructions/valuecell-ai/valuecell/agents-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/valuecell-ai/valuecell/agents-md"><img src="https://agentmods.dev/badge/instructions/valuecell-ai/valuecell/agents-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 | $0.01224 | $0.01224 |
| Opus 5.5 | $0.00490 | $0.00490 |
| Sonnet 5.5 | $0.00245 | $0.00245 |
| Haiku 4.5 | $0.00122 | $0.00122 |
Grade A, and why
valuecell AGENTS.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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guidelines
Python Programming
Python Environment
- Package manager: uv
- Virtual environment:
./python/.venv - Testing command:
uv run pytest
Imports
- Avoid inline imports unless required to break a circular dependency.
- If you import more than three names from a single module, prefer qualified imports:
- Prefer:
import pathlib; pathlib.Path, pathlib.PurePath - Avoid:
from pathlib import Path, PurePath, PurePosixPath, ...
- Prefer:
- Postpone changes to
__init__and__all__until APIs stabilize. - Use TYPE_CHECKING for imports only needed for type hints.
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from mypkg.schemas import AgentConfig
Runtime Checks
- Avoid excessive use of
getattr,hasattr, and runtime type checks. - If an object is a pydantic
BaseModel, prefer using its validated attributes and type annotations instead of probing attributes at runtime. - Rely on pydantic validation, model validators, and type hints; prefer
TypedDictorProtocolfor structural typing when appropriate. - When runtime checks are necessary, make them explicit, minimal, and well-documented so the reason for the guard is clear.
Async-First Design
- Prefer asynchronous APIs for I/O-bound work.
- Use asyncio or anyio; for HTTP, prefer httpx (async client).
- Ensure clear async boundaries: public APIs and I/O paths should be async.
- Provide minimal sync adapters only when needed, and document them.
import asyncio
from loguru import logger
import httpx
async def fetch_agent_state(url: str, timeout_s: float) -> dict:
"""Fetch agent state from a remote endpoint."""
async with httpx.AsyncClient(timeout=timeout_s) as client:
resp = await client.get(url)
resp.raise_for_status()
data = resp.json()
logger.info("Fetched state from {url}", url=url)
return data
def fetch_agent_state_sync(url: str, timeout_s: float) -> dict:
"""Synchronous adapter. Prefer the async variant."""
return asyncio.run(fetch_agent_state(url, timeout_s))
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 · 160 lines · 1,224 tokens per session scan A 33b2f7087c55
valuecell AGENTS.md is an instructions file published in the GitHub repository ValueCell-ai/valuecell (11,043 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 1,224 tokens to every session, about $0.0049 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 2026-08-30.
valuecell AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| valuecell AGENTS.mdValueCell-ai/valuecell | Instructions file | 11k | 8mo ago | Apache-2.0 | A | 1,224 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |