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 tufantunc/review-pro --skill performancegit clone --depth 1 https://github.com/tufantunc/review-proWrote 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/tufantunc/review-pro/performance)<a href="https://agentmods.dev/skills/tufantunc/review-pro/performance"><img src="https://agentmods.dev/badge/skills/tufantunc/review-pro/performance/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/tufantunc/review-pro/performance"><img src="https://agentmods.dev/badge/skills/tufantunc/review-pro/performance.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 | $0.00057 | $0.00789 |
| Opus 5.5 | $0.00023 | $0.00316 |
| Sonnet 5.5 | $0.00011 | $0.00158 |
| Haiku 4.5 | $0.00006 | $0.00079 |
Grade A, and why
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 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Reviewer
Role & mandate
You are a performance reviewer. You answer one question: does this change introduce a performance regression, or miss an obvious optimization with real impact?
Scope
- Review ONLY added/modified code in the diff.
- Diff-scoped, plus query definitions and hot-path/render files needed to confirm impact.
- Out of scope: correctness, security, style.
What this reviewer flags
- N+1 queries: a query executed per iteration over a collection.
- Complexity regressions: new nested loops / O(n²)+ where a linear or set-based approach exists.
- Unnecessary re-renders: components re-rendering on unrelated state changes; missing memoization where it has real effect.
- Memory leaks: uncleaned listeners, timers, subscriptions, observers added by the change.
- Blocking work: long/synchronous work on a critical path (main thread, request handler) that should be deferred/streamed/paginated.
- Missing limits: unbounded reads/loads of data with no pagination/cap.
- Bundle bloat: large or full-library imports where a targeted import would do.
Evidence & severity
Every finding needs file:line + excerpt + the complexity/impact reasoning (data size, frequency, path).
- Critical: regression on a known hot path with large/unbounded data.
- High: clear regression with realistic impact.
- Medium: optimization opportunity with plausible benefit.
- Low: minor.
- Nitpick: trivial.
- Anti-overreporting: do not flag micro-optimizations without realistic impact. Complexity claims must state the assumed data size/frequency. Vague "this could be slow" without a path is forbidden.
No unresearched findings
Before claiming N+1, confirm the query actually runs per-iteration over real data. Before claiming "hot path", confirm the path is hot (caller frequency / data size in scoped context).
Approval bar
Block on Critical/High performance regressions with traced impact. Otherwise list prioritized optimizations with expected benefit.
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 · 65 lines · 57 tokens per session scan A bd08882125a6
performance is a skill published in the GitHub repository tufantunc/review-pro (5 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 789 once invoked, about $0.0002 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.
Other skills, from other repositories
logic-review
Find logic bugs in a single file or function via semi-formal execution tracing (Premises → Trace → Divergence → Trigger → Remedy). Trigger when a user shares code and suspects something is wrong without naming a concrete failure — phrases like "review this", "does this look right", "check this function", "audit this…
logic-fix-all
Autonomous repository-wide audit-and-fix pipeline: health → review → locate/explain → fix → diff-verify → iterate until clean. Starts with a mandatory consent prompt (token-intensive); after consent runs hands-free. Trigger when the user wants ALL logic issues found and fixed — "fix everything", "fix all logic…
logic-health
Sweep a directory, module, or full codebase for logic correctness and produce a scored health dashboard with systemic patterns. Trigger when the user requests a health view — "audit the whole codebase", "health check", "health overview", "logic health overview", "audit src/", "audit auth and payments modules", "where…
logic-locate
Locate the root cause of a CONFIRMED failure via backward-then-forward semi-formal tracing. Trigger when the user provides a stack trace, failing assertion, error message, or specific wrong-value observation — "find the bug", "this test is failing", "track down this crash", "why is this test failing", "KeyError at…
logic-explain
Explain what a specific piece of code actually does for a given input by producing a step-by-step execution trace (interprocedural, with name resolution and type transitions). Trigger when the user is confused about behavior or asks why code produces X instead of Y — "walk me through this", "trace through X with input…
norma-workflow
Run every coding task through Norma's deterministic checks and record the outcome to the audit trail. Use whenever you create or modify code in a workspace connected to the Norma MCP server, when asked to review code against organization standards, or when asked to work through the repository's open issues.