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 skills/context4ai/agent-graph/draftnpx skills add context4ai/agent-graph --skill draftgit clone --depth 1 https://github.com/context4ai/agent-graphWhat 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 | $0.00017 | $0.00076 |
| Opus 5 | $0.00009 | $0.00038 |
| Sonnet 5 | $0.00003 | $0.00015 |
| Haiku 4.5 | $0.00002 | $0.00008 |
Grade A, and why
draft-artifact 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 yesterday.
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.
What it actually says
Draft artifact
Read the required route resources marked read-required, produce the artifact, and report an explicit outcome.
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.
- yesterday First seen · 13 lines · 17 tokens per session scan A 9d8cf58ee842
draft-artifact is a skill published in the GitHub repository context4ai/agent-graph (12 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 76 once invoked, about $0.0001 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 skills, from other repositories
output-dev-evaluator-function
Create evaluator functions in evaluators.ts for Output SDK workflows. Use when implementing quality assessment, validation logic, or content evaluation.
output-dev-prompt-file
Create .prompt files for LLM operations in Output SDK workflows. Use when designing prompts, configuring LLM providers, or using Liquid.js templating.
output-dev-eval-testing
Create offline evaluation tests for Output SDK workflows using @outputai/evals. Use when implementing test evaluators with verify(), creating dataset YAML files, building eval workflows, or running workflow tests via CLI.
output-dev-http-client-create
Create shared HTTP clients in src/shared/clients/ for Output SDK workflows. Use when integrating external APIs, creating service wrappers, or standardizing HTTP operations.
output-dev-step-function
Create step functions in steps.ts for Output SDK workflows. Use when implementing I/O operations, error handling, HTTP requests, or LLM calls.
output-meta-project-context
Comprehensive guide to Output.ai Framework for building durable, LLM-powered workflows orchestrated by Temporal. Covers project structure, workflow patterns, steps, LLM integration, HTTP clients, CLI commands, and the full inventory of available agents and skills.