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 skills add AnthonyAlcaraz/agentic-graph-rag-skills --skill memory-consistency-model-selectorgit clone --depth 1 https://github.com/AnthonyAlcaraz/agentic-graph-rag-skillsWrote 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/skills/anthonyalcaraz/agentic-graph-rag-skills/memory-consistency-model-selector)<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/memory-consistency-model-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/memory-consistency-model-selector/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anthonyalcaraz/agentic-graph-rag-skills/memory-consistency-model-selector"><img src="https://agentmods.dev/badge/skills/anthonyalcaraz/agentic-graph-rag-skills/memory-consistency-model-selector.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00194 | $0.02329 |
| Opus 5 | $0.00097 | $0.01164 |
| Sonnet 5 | $0.00039 | $0.00466 |
| Haiku 4.5 | $0.00019 | $0.00233 |
Grade A, and why
memory-consistency-model-selector 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 12d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Consistency Model Selector
Overview
When Agent A writes a fact to shared memory, when does Agent B see it? Ch4 names this the memory consistency problem, and getting it wrong produces agents that contradict each other, overwrite each other's conclusions, or act on stale information. This is CAP — the classic consistency/availability trade-off — applied per coordination operation to shared agent memory, not decided once globally. Consistency is chosen per operation because most workflows mix safety-critical decision points with tolerant background work.
Four consistency models, each with a characteristic profile:
- strong (linearizable): every agent sees the latest write before any proceeds. A synchronization barrier after every write — expensive, but required when agents act on shared authoritative state (locks, budgets, inventory) or make safety-critical irreversible decisions. The chapter's example: a drug-interaction finding every agent must see before recommending treatment.
- causal: causally-related writes are ordered; unrelated updates may lag. If A's conclusion depends on B's finding, any reader of A also sees B. The practical default for collaborating agents.
- read_your_writes: an agent always sees its own writes; other agents' writes may arrive later. Session memory for a single agent's continuity.
- eventual: cheapest; all agents converge eventually, but not when. Fine when no single fact is safety-critical and a final synthesis reconciles disagreement (background enrichment, literature accumulation).
The selector scores each model across the operation's requirement axes and
recommends one, surfacing escalate_to_strong when the default is not strong
but the operation touches authoritative state — the chapter's rule: default
to causal, escalate the irreversible decision points to strong.
The cache-divergence helper makes the failure concrete. Ch4's cache-sharing
section warns that without a protocol, Agent B acts on a cached read that a
newer committed write already superseded. detect_cache_divergence flags
exactly that: any cached snapshot older than the latest committed write on the
same key.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 164 lines · 194 tokens per session scan A 2c2afafc6325
memory-consistency-model-selector is a skill published in the GitHub repository AnthonyAlcaraz/agentic-graph-rag-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 194 tokens to every session and 2,329 once invoked, about $0.0010 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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