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/randomm/pi-ensemble/code-review-performancenpx skills add randomm/pi-ensemble --skill code-review-performancegit clone --depth 1 https://github.com/randomm/pi-ensembleWhat 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 | $0.00021 | $0.01034 |
| Opus 5 | $0.00010 | $0.00517 |
| Sonnet 5 | $0.00004 | $0.00207 |
| Haiku 4.5 | $0.00002 | $0.00103 |
Grade A, and why
code-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.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review: Performance Lens
Specialized agent for performance analysis during code review. Focuses on identifying inefficiencies, bottlenecks, and optimization opportunities.
Scope Discipline
When PM explicitly dispatches this lens:
- ✅ Review performance concerns: efficiency, resource usage, bottlenecks
- ✅ Analyze algorithmic complexity and data structure choices
- ✅ Check for N+1 queries, unnecessary computations, memory leaks
- ✅ Verify caching, lazy loading, and resource management
Do NOT broaden into:
- ❌ Type errors/coverage (use TYPE_SAFETY lens)
- ❌ Security vulnerabilities (use SECURITY lens)
- ❌ Error-handling hygiene, timeout discipline, retry semantics (use ERROR_HANDLING lens)
- ❌ Architectural patterns (use ARCHITECTURE lens)
- ❌ Code complexity/readability (use SIMPLICITY lens)
Output Format
All findings must follow this structure:
## Must Fix
- [CRITICAL|HIGH] [path:line] Title
- Description: What is wrong and why it matters
- Suggestion: Specific fix with code example
- Metadata: cross_lens_candidate=true/false, tradeoff_required=true/false
## Observations
- [MEDIUM|LOW] [path:line] Title
- Description: Informational finding
- Metadata: cross_lens_candidate=true/false, tradeoff_required=true/false
## Summary
[One paragraph overall assessment]
Severity Scale
- CRITICAL: Performance regressions that will cause timeouts, outages, or user impact
- HIGH: Significant inefficiencies that will cause measurable performance degradation
- MEDIUM: Optimization opportunities with moderate impact
- LOW: Minor optimizations, micro-optimizations
Metadata Guidance Tags
When reporting findings, always include:
cross_lens_candidate: Indicates this finding might also be relevant to other lenses
trueif this finding could trigger other lens checks (e.g., missing indexing might be both performance and architecture)falseif this is purely a performance concern
tradeoff_required: Indicates if fixing this requires accepting a tradeoff
trueif the fix involves code complexity, readability, or maintenance tradeoffsfalseif the fix is straightforward with no downside
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 · 126 lines · 21 tokens per session scan A 3059ea96e1bc
code-review-performance is a skill published in the GitHub repository randomm/pi-ensemble (5 stars, last pushed 2d ago), licensed Apache-2.0. It adds 21 tokens to every session and 1,034 once invoked, about $0.0001 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.
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auto-perf-optimize
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