Borrowing it
Nothing to install: this file belongs to qte77/analyze-stock-kpi. 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/qte77/analyze-stock-kpi/main/AGENTS.mdgit clone --depth 1 https://github.com/qte77/analyze-stock-kpiWrote 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/qte77/analyze-stock-kpi/agents-md)<a href="https://agentmods.dev/instructions/qte77/analyze-stock-kpi/agents-md"><img src="https://agentmods.dev/badge/instructions/qte77/analyze-stock-kpi/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/qte77/analyze-stock-kpi/agents-md"><img src="https://agentmods.dev/badge/instructions/qte77/analyze-stock-kpi/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 | $0.00845 | $0.00845 |
| Opus 5 | $0.00423 | $0.00423 |
| Sonnet 5 | $0.00169 | $0.00169 |
| Haiku 4.5 | $0.00085 | $0.00085 |
Grade A, and why
analyze-stock-kpi 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 8d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Instructions for analyze-stock-kpi
Behavioural rules for AI agents working on this library-based stock KPI CLI. Shared dev workflow lives in CONTRIBUTING.md (test conventions, commit + PR conventions, branch protection, GHA workflow rules, changelog fragments, release flow); this document carries only what's agent-specific. For project overview see README.md; for module map + data flow see docs/architecture.md.
Core Rules
- Follow KISS, DRY, YAGNI, AHA — simplest solution that works, no speculative features, no premature abstractions
- Never assume missing context — ask if uncertain about requirements
- Never hallucinate libraries — only use packages verified in
pyproject.toml - Always confirm file paths exist before referencing in code or tests
- Never delete existing code unless explicitly instructed
- Touch only task-related code — bug fixes don't need surrounding cleanup
- Strict pydantic — every structured payload is a
BaseModel; CLI / env viaBaseSettings(cli_parse_args=True). NoTypedDict, nodataclass.
Decision Framework
Priority order: User instructions → AGENTS.md → CONTRIBUTING.md → README.md → existing code patterns
Information sources:
- Requirements: task description (primary)
- Run / lint / test commands:
make help - Project version:
src/__version__.py - Library API shapes (yfinance, pydantic, etc.):
context7MCP, not training data
Anti-scope-creep: Implement only what is explicitly requested. Prefer landing small working slices over comprehensive rewrites within a single PR.
Quality Thresholds
Before starting any task, gut-check four dimensions on a 1 / 0 / -1 scale —
the assessment exists only to pick the next action, so it has three states, not ten.
- Context — requirements, codebase patterns, and target API understood
- Clarity — implementation path and expected outcome are clear
- Alignment — follows project patterns + KISS / DRY / YAGNI / AHA
- Success — confident the task can be completed correctly
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.
- 8d ago First seen · 84 lines · 845 tokens per session scan A 099193c916ae
analyze-stock-kpi AGENTS.md is an instructions file published in the GitHub repository qte77/analyze-stock-kpi (2 stars, last pushed 2d ago), licensed Apache-2.0. It adds 845 tokens to every session, about $0.0042 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 2026-08-31.
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