Awesome AI Apps is a collection of 132 projects, tutorials, and recipes for building applications powered by large language models. Developers use it to explore text and voice agents, retrieval-augmented generation, workflows, MCP tools, memory, and fine-tuning.
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 skills/arindam200/awesome-ai-apps/implement-universalnpx skills add Arindam200/awesome-ai-apps --skill implement-universalgit clone --depth 1 https://github.com/Arindam200/awesome-ai-appsWrote 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/arindam200/awesome-ai-apps/implement-universal)<a href="https://agentmods.dev/skills/arindam200/awesome-ai-apps/implement-universal"><img src="https://agentmods.dev/badge/skills/arindam200/awesome-ai-apps/implement-universal.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 | $0.00204 | $0.05514 |
| Opus 5 | $0.00102 | $0.02757 |
| Sonnet 5 | $0.00041 | $0.01103 |
| Haiku 4.5 | $0.00020 | $0.00551 |
Grade A, and why
implement-universal 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- implement-universal — 98% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Universal — Single-Context Workshop Implementation Loop
This is the harness-agnostic twin of /implement. It is functionally identical, but instead of dispatching subagents via Claude Code's Task tool, it instructs you to adopt two distinct roles in sequence:
- Software Engineer phase — read
agents/software-engineer.mdand execute it end-to-end. - Tester phase — read
agents/tester.mdand execute it as a fresh reviewer of the SWE phase's output.
Use this skill when running in any harness that does NOT have Claude Code's subagent dispatch (Cursor, Windsurf, generic MCP clients, plain Anthropic SDK loops, etc.). If you are running inside Claude Code, prefer /implement — the subagent isolation gives you a more independent Tester verdict.
new feature branch → SWE role implements (+ AC walk on glue tickets) → role switch → Tester role verifies (logic tickets only) → orchestrator moves file to tasks/done/ → orchestrator commits directly with `git commit -m` → report to human
After the report, the session ends. The human reviews the commit, talks the workshop audience through what happened, optionally amends or pushes, then re-invokes for the next ticket.
You are the orchestrator for steps 1–3, 6, and 7. For steps 4 and 5 you adopt the bundled roles. The orchestrator is a MANAGER — it does NOT write code, run make targets, or read changed files for review on its own behalf. That work happens inside the role phases.
Single-context limitation — read this first
Without subagent isolation, the SWE and Tester phases share one conversation history. That means:
- The Tester phase will see the SWE phase's reasoning. Bias toward confirming your own implementation is real. Counteract it: when you switch to the Tester role, treat the SWE hand-off message as if a stranger wrote it, and verify every AC against the file system / command output, not against your memory of what you implemented.
- Tool budget is shared. Format/lint/e2e runs from the SWE phase are visible; do not re-run them in the Tester phase (the Tester role explicitly trusts the SWE's e2e excerpt).
- Context window is finite. On a long ticket, the SWE phase may consume substantial context. If you notice context pressure, summarize the SWE work in a compact hand-off message rather than carrying every intermediate edit forward into the Tester phase.
What ships with it
2 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.
- 6d ago First seen · 320 lines · 204 tokens per session scan A 2d3f7d5ffaf7
implement-universal is a skill published in the GitHub repository Arindam200/awesome-ai-apps (13,677 stars, last pushed 6d ago), licensed MIT. It adds 204 tokens to every session and 5,514 once invoked, about $0.0010 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 2026-08-30.
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