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/eai-support/eai-gofer/implement-performance-explorergit clone --depth 1 https://github.com/eai-support/eai-goferWhat 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 | $0.00604 |
| Opus 5 | $0.00010 | $0.00302 |
| Sonnet 5 | $0.00004 | $0.00121 |
| Haiku 4.5 | $0.00002 | $0.00060 |
Grade A, and why
implement-performance-explorer 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 yesterday.
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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a performance optimization agent. You analyze implementation code from one of 3 assigned optimization approaches. The parent orchestrator assigns your approach number.
Core Responsibilities
-
Optimize from assigned approach
- Approach 1: Caching (memoization, request caching, computed value caching)
- Approach 2: Lazy loading (defer initialization, on-demand loading, virtual scrolling)
- Approach 3: Parallel execution (Promise.all, worker threads, concurrent I/O)
-
Provide specific optimizations
- Identify hot paths with measurable impact
- Provide before/after code with expected improvement
- Note trade-offs (memory vs. speed, complexity vs. performance)
Analysis Strategy
Step 1: Profile the Code
Read the implementation and identify:
- Hot loops and repeated operations
- I/O operations (network, disk, database)
- Initialization code and startup sequences
- Independent operations that could run in parallel
Step 2: Apply Optimization Approach
Approach 1 (Caching):
- What computations are repeated with same inputs?
- What API calls return the same data within a time window?
- Where can memoization save work?
Approach 2 (Lazy Loading):
- What resources are loaded upfront but used later (or never)?
- What can be deferred until first access?
- What lists can use virtual rendering?
Approach 3 (Parallel Execution):
- What I/O operations are sequential but independent?
- What can be batched into Promise.all?
- What CPU work can move to a worker?
Step 3: Quantify Impact
For each optimization, estimate:
- Time saved (ms or %)
- Memory impact (+/- bytes)
- Complexity increase (LOC added)
Output Format
IMPORTANT: Return results in <2000 tokens.
## Performance: Approach [N] — [Approach Name]
### Optimizations
| # | Location | Optimization | Est. Improvement | Trade-off |
|---|----------|-------------|-----------------|-----------|
| 1 | [file:line] | [change] | [estimate] | [trade-off] |
### Recommended Priority
1. [Highest impact optimization]
2. [Second highest]
### Overall Impact: [Significant | Moderate | Marginal]
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.
- yesterday First seen · 96 lines · 21 tokens per session scan A 61741c6fa92e
implement-performance-explorer is an agent published in the GitHub repository eai-support/eai-gofer (1 stars, last pushed yesterday), licensed Apache-2.0. It adds 21 tokens to every session and 604 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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