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.
curl -O https://raw.githubusercontent.com/jpicklyk/task-orchestrator/main/.claude/skills/perf-review/SKILL.mdgit clone --depth 1 https://github.com/jpicklyk/task-orchestratorWrote 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/jpicklyk/task-orchestrator/perf-review)<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>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.00658 |
| Opus 5 | $0.00020 | $0.00329 |
| Sonnet 5 | $0.00008 | $0.00132 |
| Haiku 4.5 | $0.00004 | $0.00066 |
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.
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.,
countChildrenByRoleper 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(*)withGROUP BYvs 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
childCountsto every child in overview) - Repeated serialization — same object serialized multiple times in one request?
- String parsing —
PropertiesHelper.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?
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.
- 8d ago First seen · 49 lines · 40 tokens per session scan A 1fac7a3067e1
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.
Other skills, from other repositories
code-review
Structured code reviews with severity-ranked findings and deep multi-agent mode. Use when performing a code review, auditing code quality, or critiquing PRs, MRs, or diffs. For the full multi-agent workflow, use the ia-review command (/ia-review in Claude Code).
receiving-code-review
Process code review feedback critically: check correctness before acting, push back on incorrect suggestions, no performative agreement. Use when responding to PR/MR review comments or implementing reviewer suggestions received from others.
security-privacy-threat-modeling
Use this capability for threat modeling, secure code review, OWASP risk review, API abuse, input/output validation, injection, XSS, CSRF, SSRF, deserialization, security headers, encryption, secrets exposure, audit logging, privacy-by-design, or security acceptance gates.
code-review-graph
An MCP server that parses a codebase into an AST graph (Tree-sitter into SQLite) so an assistant can fetch only the files in a change's blast radius instead of reading everything. It cuts token use on large repos and supports dead-code detection, refactor previews, and architecture maps. Best on big or multi-repo…
receiving-code-review
Guides an engineer or agent through ingesting code-review feedback and revising well — triaging comments into must-fix, nit, and question, replying to each without defensiveness, grouping related changes, pushing back with evidence when warranted, and re-requesting review. Use when a pull request has review comments…
simplify-code
Cuts away accidental complexity from working code by deleting dead branches, collapsing needless layers of indirection, un-nesting control flow, and reducing parameter lists, all while keeping observable behavior identical. Applies when a file feels over-built, more abstract than its single use justifies, or harder to…