Borrowing it
Nothing to install: this file belongs to sandeep-alluru/agentcrdt. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/sandeep-alluru/agentcrdt/main/CLAUDE.mdgit clone --depth 1 https://github.com/sandeep-alluru/agentcrdtWrote 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/sandeep-alluru/agentcrdt/claude-md)<a href="https://agentmods.dev/instructions/sandeep-alluru/agentcrdt/claude-md"><img src="https://agentmods.dev/badge/instructions/sandeep-alluru/agentcrdt/claude-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 | $0.00231 | $0.00231 |
| Opus 5 | $0.00115 | $0.00115 |
| Sonnet 5 | $0.00046 | $0.00046 |
| Haiku 4.5 | $0.00023 | $0.00023 |
Grade A, and why
agentcrdt 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 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.
What it actually says
agentcrdt — Session Anchor
Research spec: ../tech-research/14-Gaming/semantic-causal-crdt-for-agent-mutable-world-state/README.md
One-liner: CRDT where concurrent agent-world conflicts become observable events, not silent overwrites
Phase: backlog
Stack: Python, automerge-py (or py-crdt), NetworkX
Key decisions
Next step
Read the research spec, then design the semantic domain lattice and causal entailment DAG schema.
MVP definition
pip install agentcrdtworks- CRDT over semantic domain lattice (not just a standard LWW or OR-Set)
- Causal entailment DAG tracking logical dependencies between facts
- Conflict resolution emits observable world events (not silent merges)
- API:
agentcrdt.merge(a, b),agentcrdt.observe_conflicts(),agentcrdt.subscribe(callback) - Demo: two concurrent agents write contradictory world facts → conflict surfaces as an event
- README with distributed systems context and example
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 · 22 lines · 231 tokens per session scan A f7e65188f62d
agentcrdt CLAUDE.md is an instructions file published in the GitHub repository sandeep-alluru/agentcrdt (0 stars, last pushed 19d ago), licensed MIT. It adds 231 tokens to every session, about $0.0012 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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