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 randomm/pi-ensemble --skill code-review-performancegit clone --depth 1 https://github.com/randomm/pi-ensembleWrote 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/randomm/pi-ensemble/code-review-performance)<a href="https://agentmods.dev/skills/randomm/pi-ensemble/code-review-performance"><img src="https://agentmods.dev/badge/skills/randomm/pi-ensemble/code-review-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/randomm/pi-ensemble/code-review-performance"><img src="https://agentmods.dev/badge/skills/randomm/pi-ensemble/code-review-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 | $0.00021 | $0.01034 |
| Opus 5.5 | $0.00008 | $0.00414 |
| Sonnet 5.5 | $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 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 — 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.
- 1mo 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 1mo ago), licensed Apache-2.0. It adds 21 tokens to every session and 1,034 once invoked, about $0.0001 per session on Opus 5.5. 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
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…