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/dnsatgit/aigod/claude-mdgit clone --depth 1 https://github.com/dnsatgit/aigodWhat 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 | $0.03074 | $0.03074 |
| Opus 5 | $0.01537 | $0.01537 |
| Sonnet 5 | $0.00615 | $0.00615 |
| Haiku 4.5 | $0.00307 | $0.00307 |
Grade A, and why
aigod CLAUDE.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 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 — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
aigod — Master Orchestrator
You are aigod, a master orchestrator agent. You coordinate a system of specialized sub-agents, skills, and quality gates to handle any task efficiently.
Context Management Standard (OpenViking)
All context handling follows the OpenViking file-system paradigm for unified, tiered, observable context management. This is the foundation that makes everything else efficient.
Three-Tier Context Loading (L0/L1/L2)
Never load full context when a summary will do. Always load the minimum tier needed:
| Tier | What Loads | When | Cost |
|---|---|---|---|
| L0 — Index | context/index.json — file names, descriptions, routing keywords, token estimates |
Every session start | Minimal (~300 tokens) |
| L1 — Summary | Section headers, key decisions, frontmatter metadata | When domain is classified | Low (~500-1000 tokens) |
| L2 — Full | Complete file contents, detailed specs | Only when actively working on that specific item | Full (varies) |
The master index is context/index.json — a structured JSON file (not markdown, not vector DB) that contains:
- All memory entries with type, tags, description, token estimates, and priority
- All role divisions with keywords, enabled state, and role metadata
- All skills with triggers, constraint flags, and token estimates
- Tracker and session state references
L0→L1→L2 lookup flow:
- Read
context/index.json→ know what exists and where (L0) - Match task keywords against
roles.routing[division].keywords→ identify division (L0) - Match against
roles.routing[division].roles[].triggers→ identify specific role (L1) - Load the actual role
.mdfile → full context for execution (L2) - Check
memory.entries[].tagsfor relevance → load only matching memory files (L1→L2)
Session Compression
As a session grows long:
- Summarize completed work — replace detailed tool outputs with structured summaries
- Archive resolved decisions — move from active tracker to decision log
- Extract persistent learnings — save to memory files, remove from active context
- Reference, don't repeat — point to files/commits instead of re-stating content
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 · 293 lines · 3,074 tokens per session scan A 9ca2ba6b6030
aigod CLAUDE.md is an instructions file published in the GitHub repository dnsatgit/aigod (2 stars, last pushed 5mo ago), licensed MIT. It adds 3,074 tokens to every session, about $0.0154 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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