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 neverinfamous/memory-journal-mcp --skill adversarial-performancegit clone --depth 1 https://github.com/neverinfamous/memory-journal-mcpWrote 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/neverinfamous/memory-journal-mcp/adversarial-performance)<a href="https://agentmods.dev/skills/neverinfamous/memory-journal-mcp/adversarial-performance"><img src="https://agentmods.dev/badge/skills/neverinfamous/memory-journal-mcp/adversarial-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/neverinfamous/memory-journal-mcp/adversarial-performance"><img src="https://agentmods.dev/badge/skills/neverinfamous/memory-journal-mcp/adversarial-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 | $0.00086 | $0.01547 |
| Opus 5.5 | $0.00034 | $0.00619 |
| Sonnet 5.5 | $0.00017 | $0.00309 |
| Haiku 4.5 | $0.00009 | $0.00155 |
Grade A, and why
adversarial-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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Performance
A multi-pass performance auditing system that produces high-confidence optimization assessments by introducing structured adversarial critique stages. Audits pass through an iterative pipeline of profiling, stress-testing critique, optimization planning, and optional external validation — producing output optimized for measurable impact, effort efficiency, and regression safety.
When to Load
Load this skill when any of these apply:
- Running a performance audit against an entire repository
- Profiling build times, runtime hot paths, or bundle size
- The user asks for an adversarial performance review or stress-test analysis
- The user says "perf audit", "performance review", "find bottlenecks", "adversarial performance", "optimize this repo", "make this faster", "why is this slow", or "speed up my code"
- Preparing a performance baseline report before a major release
- You want to reduce blind spots in your own performance assessment
Auto-Detection
Before starting, auto-detect the project profile by scanning the repository:
| Signal | Project Profile | Extra Categories |
|---|---|---|
MCP SDK imports, tool handlers, tools/list |
mcp-server |
Token & Context Efficiency (Category 7) — full depth |
Express/Hono/Fastify, HTTP handlers, listen() |
web-app |
Runtime Performance (Category 4) — extra API latency focus |
bin field, CLI arg parsing |
cli-tool |
Startup Cost analysis in Category 4 |
| Vitest/Jest/Playwright config | tested |
Test Suite Performance (Category 5) — full depth |
| Dockerfile present | containerized |
Build Performance (Category 1) — Docker layer analysis |
| Database imports (better-sqlite3, pg, mysql2) | data-layer |
Database & I/O (Category 6) — full depth |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 140 lines · 86 tokens per session scan A 73db7e19987d
adversarial-performance is a skill published in the GitHub repository neverinfamous/memory-journal-mcp (20 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 1,547 once invoked, about $0.0003 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 2026-08-30.
Other skills, from other repositories
perf-profiling
Systematic performance profiling and optimization across frontend (Core Web Vitals, code splitting, lazy loading), backend (N+1 queries, async), and database (EXPLAIN ANALYZE, indexing) layers. Use when the user reports slow code, latency, memory leaks, needs to benchmark, or wants to speed up an application. Measure…
deep-debugging
Systematic debugging protocol for bugs that resist quick fixes. Use bisection, hypothesis trees, and scientific method when a bug isn't obvious from the stack trace. Goes beyond bugfix-quick for production-grade root cause analysis.
failure-classifier
Classifies execution failures into F1-F5 (transient/local-logic/local-design/story-level/architectural). Outputs JSON with class, confidence, evidence, and recommended action. Deterministic rule-based classifier; no LLM call.
self-healing-orchestrator
Proposes patches for F2 (local-logic) and F3 (local-design) failures. NEVER applies without user approval. Confidence ≥0.7 to propose; below that, escalates raw findings. Counts toward replanbudget. Per-task: max 1; per-session: max 5.
bugfix-quick
Fast bug fixes with root cause investigation + TDD. Enforces 'no fix without root cause' discipline and verification protocol. Without this skill, fixes are applied at symptoms instead of sources, and bugs return.
problem-solving
5 techniques for different problem types. Use when stuck or facing complex challenges.