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/rootly-ai-labs/rootly-graphify-importer/agents-mdgit clone --depth 1 https://github.com/Rootly-AI-Labs/rootly-graphify-importerWrote 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/rootly-ai-labs/rootly-graphify-importer/agents-md)<a href="https://agentmods.dev/instructions/rootly-ai-labs/rootly-graphify-importer/agents-md"><img src="https://agentmods.dev/badge/instructions/rootly-ai-labs/rootly-graphify-importer/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 5.1 | $0.04539 | $0.04539 |
| Opus 5 | $0.02269 | $0.02269 |
| Sonnet 5 | $0.00908 | $0.00908 |
| Haiku 4.5 | $0.00454 | $0.00454 |
Grade A, and why
rootly-graphify-importer 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 — 474 lines — stays where its author put it; the contents beside it link to each section on GitHub.
rootly-graphify
This project turns Rootly incident data into a knowledge graph. The Rootly corpus lives in graphify-rootly-data/ and the graph output in graphify-out/.
Always-on rules:
- Before answering incident or architecture questions, read
graphify-out/GRAPH_REPORT.mdfor god nodes and community structure - If
graphify-out/wiki/index.mdexists, navigate it instead of reading raw files - After modifying code files, run:
python -c "from graphify.watch import _rebuild_code; from pathlib import Path; _rebuild_code(Path('.'))"to keep the graph current
graphify pipeline — full instructions
When the user asks you to run graphify on a path (e.g. "run graphify on graphify-rootly-data"), follow these steps in order.
Usage patterns
graphify <path> # full pipeline
graphify <path> --mode deep # richer INFERRED edges
graphify <path> --update # re-extract only new/changed files
graphify query "<question>" # BFS traversal of graph.json
graphify path "NodeA" "NodeB" # shortest path between two nodes
graphify explain "NodeName" # explain a single node
Step 1 — Ensure graphify is installed
python -c "import graphify" 2>/dev/null || pip install graphifyy -q
Step 2 — Detect files
python -c "
import json
from graphify.detect import detect
from pathlib import Path
result = detect(Path('INPUT_PATH'))
Path('.graphify_detect.json').write_text(json.dumps(result, indent=2), encoding='utf-8')
print(result['total_files'], 'files,', result['total_words'], 'words')
for k, v in result.get('files', {}).items():
if v: print(f' {k}: {len(v)} files')
"
Replace INPUT_PATH with the actual path. Present a clean summary:
Corpus: X files · ~Y words
docs: N files
code: N files
- If
total_filesis 0: stop with "No supported files found." - If
total_words> 2,000,000 ORtotal_files> 500: warn and ask which subfolder to run on. - Otherwise: proceed directly to Step 3.
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 · 474 lines · 4,539 tokens per session scan A b3dd0e4080ee
rootly-graphify-importer AGENTS.md is an instructions file published in the GitHub repository Rootly-AI-Labs/rootly-graphify-importer (45 stars, last pushed 4mo ago), licensed MIT. It adds 4,539 tokens to every session, about $0.0227 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
GPT-RAG release.instructions.md
Instructions for Azure/GPT-RAG, a project described as: Enterprise-grade accelerator for agentic RAG on Azure. Built on Microsoft Foundry with Foundry IQ as the default retrieval backend, Microsoft Agent Framework orchestration, Zero-Trust architecture and IaC.
apex-accelerator vendor-prompting.instructions.md
Vendor prompting best-practice rules for Anthropic Claude and OpenAI GPT-5.6-Terra agents and prompts. Each rule cites a rule ID in the vendor-prompting skill rules.json registry. Validator: npm run lint:vendor-prompting.
rag-code-mcp copilot-instructions.md
Instructions for doITmagic/rag-code-mcp, covering copilot instructions - ragcode mcp, ⚖️ the golden rule, project overview, architecture & patterns and developer workflows.
ken CLAUDE.md
Claude Code instructions for townsendmerino/ken, covering claude.md, what this is, repository ownership (read this first), commands and embedding parity & golden fixtures (now in aikit).
ZipAI CLAUDE.md
Claude Code instructions for nickdesi/ZipAI, covering claude.md — zipai: ultra-dense token optimizer, rules, 1. zero filler, 2. ambiguity and 3. prompt caching.
gpu-ai-skills CLAUDE.md
Claude Code instructions for intel/gpu-ai-skills, covering claude.md, what this repository is, repository structure, commands and validation (required before any skill change).