task-orchestrator: Skill for Claude Code

.claude/skills/perf-review/SKILL.md

perf-review is a skill for Claude Code from jpicklyk/task-orchestrator. It costs 40 tokens per session (658 once invoked), scanned A, original, MIT.

A review process for estimating how a code change may affect speed and resource use in a Kotlin server with a SQLite database.

In plain words
What is it for?
Use it to inspect request paths, loops, startup work, background operations, query counts, filtering, indexes, transaction duration, and measurement plans.
Why use it?
It helps identify slower request handling, excessive database queries, full-table scans, missing indexes, and other performance risks before release.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

This is jpicklyk/task-orchestrator's own configuration. It tells Claude Code how to work on task-orchestrator itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything task-orchestrator configures →

Reuse

Borrowing it

Nothing to install: this file belongs to jpicklyk/task-orchestrator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/jpicklyk/task-orchestrator/main/.claude/skills/perf-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/jpicklyk/task-orchestrator

Made for: Claude Code.

Wrote 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.

agentmods badge for perf-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/jpicklyk/task-orchestrator/perf-review.svg)](https://agentmods.dev/skills/jpicklyk/task-orchestrator/perf-review)
Your own site
<a href="https://agentmods.dev/skills/jpicklyk/task-orchestrator/perf-review"><img src="https://agentmods.dev/badge/skills/jpicklyk/task-orchestrator/perf-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 658 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.00658
Opus 5 $0.00020 $0.00329
Sonnet 5 $0.00008 $0.00132
Haiku 4.5 $0.00004 $0.00066

Measured 8d ago against content hash 1fac7a3067e1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

perf-review 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 8d 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.

.claude/skills/perf-review/SKILL.md · 49 lines

How it starts

The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Performance Review Framework

Evaluate performance impact of changes. This project is a Kotlin MCP server with SQLite via Exposed ORM, handling tool calls synchronously per request.

Step 1: Hot Path Analysis

Identify which hot paths the change touches:

  • Per-request paths — MCP tool execution (every tool call hits this). New work here adds latency to every request.
  • Per-item loops — operations that iterate over items (search, overview, stalled-item detection). N+1 patterns here scale poorly.
  • Startup path — server initialization, database schema creation, config loading. Affects container startup time.
  • Background operations — cascade detection, dependency resolution. Runs inline, not async.

Step 2: Database Query Patterns

  • N+1 queries — does the change add a query inside a loop? (e.g., countChildrenByRole per child in overview). Count total queries for a typical operation.
  • Full table scans — any selectAll() without filters on large tables?
  • Missing indexes — new filter conditions that would benefit from an index?
  • Transaction scope — are transactions held open longer than necessary?
  • Aggregate vs fetch-all — using SELECT COUNT(*) with GROUP BY vs fetching all rows and counting in memory?

Step 3: JSON/Serialization Cost

  • Large response payloads — does the change add fields that significantly increase response size? (e.g., adding childCounts to every child in overview)
  • Repeated serialization — same object serialized multiple times in one request?
  • String parsingPropertiesHelper.extractTraits() parses JSON on every call. Acceptable for small objects, flag if called in tight loops.

Step 4: Complexity Analysis

  • What is N? — identify the scaling variable (number of items, children, notes, dependencies)
  • Current complexity — O(1), O(N), O(N*M)? Where does the change sit?
  • Realistic scale — what's the expected N in practice? (Most projects: <100 items, <30 children per root)
  • Worst case — what happens at 1000+ items? Does it degrade gracefully or hit a wall?

Read the full file on GitHub · 49 lines

Changes

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.

  1. 8d ago First seen · 49 lines · 40 tokens per session scan A 1fac7a3067e1

Subscribe to this mod's changes

perf-review is a skill published in the GitHub repository jpicklyk/task-orchestrator (206 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 658 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 2026-08-30.

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