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 lukasrepublic/agentic-foundry --skill data-tier-cutovergit clone --depth 1 https://github.com/lukasrepublic/agentic-foundryWrote 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/lukasrepublic/agentic-foundry/data-tier-cutover)<a href="https://agentmods.dev/skills/lukasrepublic/agentic-foundry/data-tier-cutover"><img src="https://agentmods.dev/badge/skills/lukasrepublic/agentic-foundry/data-tier-cutover/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/lukasrepublic/agentic-foundry/data-tier-cutover"><img src="https://agentmods.dev/badge/skills/lukasrepublic/agentic-foundry/data-tier-cutover.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 | $0.00233 | $0.01741 |
| Opus 5.5 | $0.00093 | $0.00696 |
| Sonnet 5.5 | $0.00047 | $0.00348 |
| Haiku 4.5 | $0.00023 | $0.00174 |
Grade A, and why
data-tier-cutover 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 1mo 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
data-tier-cutover — the stateful-tier decision model, inventory, gate, runbooks, and review hook
Migrating the stateful tier is the hardest step of a platform migration and is categorically
different from the stateless id-* flow. This skill encodes the decision model and the four
evidence-backed traps so they are driven, not re-derived. Read-only / never-apply: this skill
authors the plan artifact + runbook; every mutating step in the emitted runbook is operator-run
(the id-apply posture discipline). The merge floor — the adopter's branch protection + CI
checks (see docs/merge-floor.md) — stays the merge authority.
Stage A — the cutover-mechanism decision tree (fail-closed)
- Inputs (all three required):
downtime_tolerance(window_ok|near_zero),source_shared_with_prod(bool),source_is_replicable(bool). A missing input is a STOP — never assume tolerance. window_okAND not shared ⇒ snapshot-restore (the simple path): a snapshot restore PRESERVES the source DB users and passwords — the app keeps its credentials and only the host string changes.near_zeroOR shared-with-prod ⇒ CDC-replicate + fast-promote, and the plan artifact MUST carry the three required CDC caveat items: sequences are NOT replicated by logical CDC — resync them at promote; the cache cannot be slaved cross-environment — provision it FRESH and warm it; define a replication-lag SLA before promote (stale reads until promote).- Record the chosen mechanism + rationale into the per-app cutover plan artifact so the operator and the PR reviewers see WHY, not just WHAT.
Stage B — connection-pool inventory (from application source, never the deploy layer)
- Read the APPLICATION SOURCE for the data-access topology and record a matrix row per app:
{pool_shape: single | dual, ro_endpoint_key, rw_endpoint_key, sticky_read_your_writes: bool, orm/driver}. The deploy/gitops layer is an UNRELIABLE source for this — a dual read/write pool or a sticky read-your-writes default is only visible in code, and guessing from the deploy layer produced a wrong matrix on the evidence engagement. - The matrix drives feasibility: dual-pool apps can take a phased read-first cutover; a single-pool app cannot. Missing/ambiguous topology ⇒ default-deny — STOP and ask; never guess (a wrong guess silently reads stale data or connects to the wrong endpoint).
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.
- 1mo ago First seen · 94 lines · 233 tokens per session scan A 707d386074fc
data-tier-cutover is a skill published in the GitHub repository lukasrepublic/agentic-foundry (1 stars, last pushed 3d ago), licensed MIT. It adds 233 tokens to every session and 1,741 once invoked, about $0.0009 per session on Opus 5.5. 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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