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/cratis/ai/review-performancenpx skills add Cratis/AI --skill review-performancegit clone --depth 1 https://github.com/Cratis/AIWhat 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 | $0.00596 |
| Opus 5 | $0.00023 | $0.00298 |
| Sonnet 5 | $0.00009 | $0.00119 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
review-performance 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-performance — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perform a focused performance review of changed code.
Chronicle / Event Sourcing
- Projections use AutoMap (on by default) — avoids manual mapping cost
- Projections do NOT join on the read model (forces full re-read)
- Reactors do NOT re-query the event log inside
On()— use event data directly - No eager loading of entire event sequences without paging/filtering
- New projections can replay all historical events without crashing
- Events are small — no large blobs or base64-encoded content embedded
MongoDB / Read Models
- Queries filter on indexed fields — no unintentional full-collection scans
- Paged queries use
.Skip()+.Take()— never load all rows - No N+1 pattern — single query returns all needed data
- Read-model records do not embed large nested collections that are never fully iterated
ASP.NET Core / Commands & Queries
- Query endpoints do not hydrate the full collection when only a count is needed
- Command validators are synchronous and in-memory — no I/O in validation
- No
await Task.Run(() => syncWork)wrapping for naturally async work - Response payloads include only fields the client uses — no over-fetching
React / TypeScript
-
DataTableuseslazy+paginatorfor collections larger than ~20 rows - No inline object/array literals passed as props (causes identity change every render)
-
useEffectdependencies are correct — no missing deps, no over-broad deps - Large-collection components wrapped in
React.memoor use stable references - No
JSON.parse(JSON.stringify(x))deep cloning
General .NET
- No LINQ
.ToList()before.Where()— filter before materialising -
IEnumerable<T>not enumerated multiple times — materialise once if needed - Large object logging uses
{@obj}only atDebuglevel
Risk classification
- 🔴 High — measurable degradation at moderate load — must fix before merge
- 🟡 Medium — could degrade under load or at scale
- 🟢 Low — minor inefficiency or style issue
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 · 56 lines · 45 tokens per session scan A 09b8a20a88cc
review-performance is a skill published in the GitHub repository Cratis/AI (2 stars, last pushed 5d ago), licensed MIT. It adds 45 tokens to every session and 596 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
cratis-readmodel
Step-by-step guidance for creating a Cratis Chronicle read model from scratch — defining events, choosing between projection and reducer, [ReadModel] record with static query methods, and the generated TypeScript proxy in React. Use when creating a read model, working with [EventType], [ReadModel], IProjectionFor…
add-ef-migration
Use this skill when asked to add a database table, column, relationship, or other schema change via Entity Framework Core in a Cratis-based project.
add-projection
Use this skill when asked to add a Chronicle projection to a Cratis-based project. Favor model-bound projections by default, and only fall back to declarative/fluent IProjectionFor projections when model-bound attributes cannot express the behavior cleanly. Enforces the AutoMap-first rule and Chronicle-specific join…
event-type-migrations
Evolve a Cratis Chronicle event schema without breaking replay — add a new generation and an EventTypeMigration so old stored events upcast into the new shape. Use when an event needs a new required property, a renamed property, or a structural change after events of the prior shape already exist.
inspect-running-chronicle
Inspect or operate a running Chronicle server with the cratis CLI - list failed partitions, read why an observer is stuck, replay a partition, browse events, event types, read models, projections and jobs. Use when a question is about the state of a live store rather than the source code, when a projection will not…
multi-tenancy
Isolate tenants in a Cratis application with Chronicle namespaces — Arc maps the current tenant to a namespace (TenantNamespaceResolver), and each namespace has its own events, projections, reducers, and read models. Use when one deployment must serve multiple tenants with data isolation. Keep it product-neutral — not…