Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/typedef-ai/fenic/agents-mdgit clone --depth 1 https://github.com/typedef-ai/fenicWrote 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/typedef-ai/fenic/agents-md)<a href="https://agentmods.dev/instructions/typedef-ai/fenic/agents-md"><img src="https://agentmods.dev/badge/instructions/typedef-ai/fenic/agents-md.svg" alt="Measured on agentmods" 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 | $0.00787 | $0.00787 |
| Opus 5 | $0.00394 | $0.00394 |
| Sonnet 5 | $0.00157 | $0.00157 |
| Haiku 4.5 | $0.00079 | $0.00079 |
Grade A, and why
fenic 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 5d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — writing fenic
fenic is a PySpark-style semantic DataFrame library (import fenic as fc).
You likely know its DataFrame surface; below are the mechanics that DON'T match
PySpark/pandas intuition. (Developing fenic itself? See CLAUDE.md.)
After writing or editing any fenic pipeline, run
fenic check <file>— a static lint (no execution) that resolves yourfc.*symbols against the installed fenic and flags namespace/import mistakes.
Must-knows
import fenic as fc; everything is flat onfc. There is nofenic.functions, nofenic.api.types, and no unifiedOpenAIModelConfig.- Function namespaces:
fc.text/fc.json/fc.markdown/fc.semantic/fc.embedding/fc.dt, andfc.arrfor array ops (⚠️fc.arrayis the array-literal constructor, not the ops namespace). explode/unnestare DataFrame methods —df.explode("c"),df.unnest("c")— neverfc.explode.- Language vs embedding models are separate classes (
fc.OpenAILanguageModelvsfc.OpenAIEmbeddingModel) in separate config keys (language_models/embedding_models);default_language_model/default_embedding_modelare required when more than one is registered. Anthropic uses splitinput_tpm/output_tpm, not a singletpm. - Semantic templates use Jinja2
{{ var }}+ matching column kwargs:fc.semantic.predicate("... {{ x }} ...", x=fc.col("x")).parse_pdfisfc.semantic.parse_pdf(undersemantic, notmarkdown). - Local extras for heavier operators:
fc.semantic.parse_pdfandsession.read.pdf_metadataneedfenic[pdf];df.semantic.with_cluster_labelsneedsfenic[cluster];df.semantic.sim_joinneedsfenic[sim-join].
Traps fenic check can't catch — get these right by hand
fc.json.jq(col, q)returns an array →.get_item(0)before a scalar.cast.- A semantic template with single braces
{x}is not interpolated (silent). fc.dt.datediff(end, start)returnsend - start(argument order matters).fc.dt.to_timestamp(col, fmt)takes Spark/Java patterns (yyyy-MM-dd HH:mm:ss), not Python%-tokens.
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.
- 5d ago First seen · 55 lines · 787 tokens per session scan A 65765876b735
fenic AGENTS.md is an instructions file published in the GitHub repository typedef-ai/fenic (670 stars, last pushed 3d ago), licensed Apache-2.0. It adds 787 tokens to every session, about $0.0039 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.
Other instructions, from other repositories
ai-dial-core CLAUDE.md
Claude Code instructions for epam/ai-dial-core, covering claude.md, build & run, set credentials via environment variables, build (skip tests) and run all tests.
ai AGENTS.md
AGENTS.md instructions for vercel/ai, covering agents.md, project overview, repository structure, key directories and core package dependencies.
blockrun-mcp AGENTS.md
AGENTS.md instructions for BlockRunAI/blockrun-mcp, covering blockrun mcp, commands, project structure, key dependencies and install in codex.
InvestSkill GEMINI.md
Gemini CLI instructions for yennanliu/InvestSkill, covering investskill — gemini cli setup & usage guide, installation & setup, quick start, navigate to the investskill directory and start gemini cli (loads gemini.md automatically).
technocore-chat AGENTS.md
AGENTS.md instructions for flop-labs/technocore-chat: CI runs exactly these — run them before pushing.
openrouter-mcp-multimodal AGENTS.md
AGENTS.md instructions for stabgan/openrouter-mcp-multimodal, covering agent instructions, before you ship, releasing (read this before publishing), short version and version files (must all match package.json).