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 Pattyboi101/oats-autonomous-agents --skill agent-experience-auditgit clone --depth 1 https://github.com/Pattyboi101/oats-autonomous-agentsWrote 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/pattyboi101/oats-autonomous-agents/agent-experience-audit)<a href="https://agentmods.dev/skills/pattyboi101/oats-autonomous-agents/agent-experience-audit"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/agent-experience-audit/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/pattyboi101/oats-autonomous-agents/agent-experience-audit"><img src="https://agentmods.dev/badge/skills/pattyboi101/oats-autonomous-agents/agent-experience-audit.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 | $0.00035 | $0.00797 |
| Opus 5 | $0.00017 | $0.00398 |
| Sonnet 5 | $0.00007 | $0.00159 |
| Haiku 4.5 | $0.00003 | $0.00080 |
Grade A, and why
agent-experience-audit scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import urllib.request, json How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Experience Audit — MCP Dept
You audit what AI agents see when they use Your Project's MCP server. Your goal is to ensure every interaction is obviously better than a raw web search.
Before Starting
- Is production up? Check /health endpoint
- Is SSH available? Try a quick fly ssh command
- What's the context? (New registry listing, post-outreach, routine check)
How This Skill Works
Mode 1: Search Quality Audit
Test the top 5 search queries (auth, analytics, payments, email, database). For each: is the top result relevant? Does it have install_command? Is the tagline useful?
Mode 2: Tool Detail Audit
Pick specific tools (e.g. the 13 we emailed) and check their full detail response. Are descriptions accurate? Are migration signals present? Are compatible tools shown?
Mode 3: First-Install Experience
Simulate a developer who just installed from Smithery/Glama. What's their first query? What do they see? Would they keep using us?
Audit Checklist
Per Search Query
| Check | Pass | Fail |
|---|---|---|
| Top result is relevant to query? | Auth → auth tool | Auth → Airflow |
| Top result has install_command? | npm install X |
Empty |
| Top result has useful tagline? | Describes what it does | Truncated or generic |
| Category is correct? | Analytics → Analytics | Analytics → AI & Automation |
| Migration signal present? | "14 repos migrated from X" | Empty (acceptable if no data) |
Per Tool Detail
| Check | Pass | Fail |
|---|---|---|
| Description > tagline? | Full paragraph | Same as tagline |
| Install command correct? | Matches actual package | Wrong package entirely |
| Migration data present? | Gaining/losing signals | Empty (check sdk_packages mapping) |
| Verified combos present? | "Works with X in N repos" | Empty |
| Trust tier shown? | "New/Tested/Verified" | Missing |
Query via Production
# Search test
~/.fly/bin/fly ssh console -a your-project -C 'python3 -c "
import urllib.request, json
r = urllib.request.urlopen(\"http://localhost:8080/api/tools/search?q={query}&limit=5\")
data = json.loads(r.read())
for t in data.get(\"tools\", []):
print(t.get(\"slug\") + \": \" + str(t.get(\"migration_signal\", \"no signal\")))
"'
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.
- 11d ago First seen · 77 lines · 35 tokens per session scan A 7ae47aa35f75
agent-experience-audit is a skill published in the GitHub repository Pattyboi101/oats-autonomous-agents (6 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 797 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…