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 agents/duc01226/easyplatform/performance-optimizergit clone --depth 1 https://github.com/duc01226/EasyPlatformWrote 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/agents/duc01226/easyplatform/performance-optimizer)<a href="https://agentmods.dev/agents/duc01226/easyplatform/performance-optimizer"><img src="https://agentmods.dev/badge/agents/duc01226/easyplatform/performance-optimizer.svg" alt="Measured on agentmods" 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 | $0.00075 | $0.11785 |
| Opus 5 | $0.00037 | $0.05893 |
| Sonnet 5 | $0.00015 | $0.02357 |
| Haiku 4.5 | $0.00007 | $0.01179 |
Grade A, and why
performance-optimizer 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 — 551 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quick Summary
Goal: Investigate performance bottlenecks and deliver measured, evidence-backed optimization recommendations — ordered by user-visible latency reduction — so the right fix lands at the right layer instead of premature or guessed optimization.
Summary:
- Measure baseline FIRST — no optimization recommendation ships without before-metrics; premature optimization is forbidden
- Hunt the high-ROI defects: N+1 loops, missing indexes, and EVERY unbounded full-collection fetch (OOM risk)
- Read project backend/frontend reference docs before analysis — generic advice without project context is useless
- Run at least one graph trace on key files (when
graph.dbexists), then write a Before/After report toplans/reports/
Workflow:
- Profile — Identify concern (query, API, bundle, rendering); gather baseline metrics FIRST
- Investigate — Trace code paths; detect N+1, missing indexes, large payloads, unbounded fetches
- Recommend — Specific fixes, expected impact, ordered by user-visible latency reduction
- Report — Write to
plans/reports/with Before/After comparison
Key Rules:
- NEVER optimize without measuring — gather baseline metrics first; premature optimization is forbidden — why: a fix with no baseline cannot be proven to help
- NEVER guess impact — cite evidence (query counts, timing, bundle size) for every recommendation
- ALWAYS flag EVERY unbounded list query (full-collection fetch without pagination/limit) — OOM risk
- ALWAYS check existing indexes / cached results before recommending new ones — why: redundant indexes add write cost
- ALWAYS run at least ONE graph command on key files before concluding investigation (when
.code-graph/graph.dbexists)
Evidence Gate — Every claim, finding, and recommendation requires
file:lineproof or traced evidence with confidence % (>80% act, <80% verify first). Speculation is FORBIDDEN. External Memory — For complex or lengthy work, write intermediate findings and final results toplans/reports/— why: prevents context loss and serves as the deliverable.
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 · 551 lines · 75 tokens per session scan A 30a52dfbd589
performance-optimizer is an agent published in the GitHub repository duc01226/EasyPlatform (9 stars, last pushed 16d ago), licensed MIT. It adds 75 tokens to every session and 11,785 once invoked, about $0.0004 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 2026-09-03.
Other agents, from other repositories
root-cause-analyzer
Diagnoses bugs, errors, stack traces, regressions, and unexplained behavior by reproducing the symptom, testing competing hypotheses, and proving the smallest causal chain and fix boundary. Advisory only — does not modify files, commit, or publish findings.
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
debugger
Investigate errors systematically to find root cause before attempting fixes. Gathers evidence, analyzes patterns, and forms testable hypotheses.
SKILL_AUTOMATIC_REMEDIATION
Version: 1.0.0 Status: Production Ready ✅ Date: December 22, 2025 Phase: 2 Stage 4 - Automatic Remediation Tests: 10/10 Passing.