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/agentconfig/agentconfig.org/advisornpx skills add agentconfig/agentconfig.org --skill advisorgit clone --depth 1 https://github.com/agentconfig/agentconfig.orgWhat 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 | $0.00045 | $0.02942 |
| Opus 5 | $0.00023 | $0.01471 |
| Sonnet 5 | $0.00009 | $0.00588 |
| Haiku 4.5 | $0.00005 | $0.00294 |
Grade A, and why
advisor 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 2d 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 — 327 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interactive Workflow Advisor
Help users discover and prioritize the right AI primitives from agentconfig.org for their specific workflow, team, and skill level.
Your Role
You are an expert consultant on AI coding assistant configuration. Your job is to:
- Understand the user's current workflow and pain points
- Recommend the most impactful AI primitives from agentconfig.org
- Explain why each primitive solves their specific needs
- Provide implementation guidance matched to their skill level
- Warn about common pitfalls for their setup
Step 1: Load the Primitive Reference
Before asking any questions, fetch the complete primitive documentation:
Read: https://agentconfig.org/llms-full.txt
This file contains all 13 AI primitives organized into eight layers:
- Instructions: Persistent Instructions, User Scope Instructions, Directory / Path Scope Instructions
- Procedures: Skills / Workflows, Slash Commands
- Tools & Context: Tool Integrations (MCP)
- Delegation: Agent Mode, Custom Agents
- Control & Approval: Permissions & Guardrails, Lifecycle Hooks, Runtime Sandbox
- Memory & State: (reserved; no primitive is modeled here yet)
- Distribution: Configuration Distribution
- Verification & Observability: Verification / Evals
It also documents the nine-entry scope model (managed/org, user, repository, local repository, directory/path, agent, session, turn, tool invocation) — scopes describe where a primitive applies and are not primitives themselves.
Step 2: Understand the User's Context
Ask 2-3 clarifying questions to understand their workflow:
Essential Questions
-
What's your primary pain point with AI coding assistants right now?
- Examples: "Inconsistent code style", "Too many manual steps", "Need to enforce safety rules", "Want better debugging help"
-
What's your setup?
- Role: solo developer, team lead, platform team, etc.
- Team size: solo, small team (2-10), large org (10+)
- Primary tool: GitHub Copilot, Claude Code, Cursor, OpenAI Codex, or a combination
- Skill level: beginner (new to AI tools), intermediate (use daily), advanced (configured custom workflows)
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.
- 2d ago First seen · 327 lines · 45 tokens per session scan A d90432f05b0e
advisor is a skill published in the GitHub repository agentconfig/agentconfig.org (8 stars, last pushed 5d ago), licensed ISC. It adds 45 tokens to every session and 2,942 once invoked, about $0.0002 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.
Other skills, from other repositories
clinical-reports
Write comprehensive clinical reports including case reports (CARE guidelines), diagnostic reports (radiology/pathology/lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP, H&P, discharge summaries). Full support with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation…
clinical-decision-support
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading…
immunology-assays
Computational analysis of immunology experimental data. ATAC-seq differential accessibility, immune cell tracking from microscopy, ELISA data processing with 4-parameter logistic fitting, immunohistochemistry quantification, antibody titer analysis, and cell cycle phase duration estimation. For flow cytometry use…
molecular-cloning
Molecular cloning simulation and design. PCR amplicon prediction, restriction enzyme digestion, Golden Gate and Gibson assembly simulation, primer design, CRISPR sgRNA design, and plasmid annotation. For protein-level sequence analysis use biopython or esm; for database lookups use gene-database or ensembl-database.
bioimage-analysis
Microscopy image analysis for cell biology. Cell segmentation (Cellpose, watershed), object tracking (trackpy), morphology quantification, colony counting, colocalization analysis, and cytoskeleton characterization. For pathology WSI use pathml; for flow cytometry use flow-cytometry-analysis.
flow-cytometry-analysis
Complete flow cytometry analysis pipeline. FCS file handling, compensation, manual/automated gating, immunophenotyping, CFSE proliferation analysis, cell cycle analysis (Dean-Jett-Fox), and apoptosis assays. Extends flowio with analytical workflows. For raw FCS parsing only use flowio.