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/implementnpx skills add Arindam200/awesome-ai-apps --skill implementgit 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)<a href="https://agentmods.dev/skills/arindam200/awesome-ai-apps/implement"><img src="https://agentmods.dev/badge/skills/arindam200/awesome-ai-apps/implement.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 | $0.00197 | $0.05757 |
| Opus 5 | $0.00098 | $0.02878 |
| Sonnet 5 | $0.00039 | $0.01151 |
| Haiku 4.5 | $0.00020 | $0.00576 |
Grade A, and why
implement 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 5d 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 — 95% identical, 10 lines differ
How it starts
The opening of the file, as written. The whole thing — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implement Mode — Workshop Single-Ticket Implementation Loop
A workshop-specialized adaptation of squid's /day skill. Drives one pre-groomed ticket from implement_yourself/tasks/NNN-slug.groomed.md through:
new feature branch → SWE implements (+ AC walk on glue tickets) → verifier (Tester on logic tickets, orchestrator spot-check on glue tickets) → 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 types /implement next (or /implement NNN) to pick up the following ticket.
You are the orchestrator — a MANAGER, not an implementer. You do NOT write code, run make targets, or read changed files for review yourself. You launch agents, enforce the Tester gate, and finalize the ticket (branch + done-move + commit).
E2E smoke tests
The Makefile exposes three end-to-end targets that double as smoke tests. Most tickets name one of them in their Acceptance Criteria as the verification target:
make test-research-workflow— exercises the Deep Research MCP server end-to-end on the dataset seed. Default smoke test for any research-side ticket (#001–#010, #013).make test-writing-workflow— exercises the LinkedIn Writer MCP server end-to-end on the dataset guideline + prebuilt research. Default smoke test for any writing-side ticket (#011, #014–#019).make test-end-to-end— runs research + writing back-to-back on a dataset sample. Use for cross-cutting tickets (#020 Okahu/Monocle tracing, #024 README, anything that integrates both servers).
When a ticket does not explicitly name a target, infer the right one from the affected server. Bootstrap tickets (make run-research-server / make run-writing-server) are the exception — those boot-and-kill checks are not smoke tests.
Critical rules
- Never rubber-stamp the Tester's (or SWE's, on glue tickets) report. When the verifier says PASS, re-read each Acceptance Criterion in the ticket and confirm the report's evidence is real (file path, command output excerpt, Python expression result). REJECT and re-launch if not.
/implementis single-shot per ticket. After step 7, end the session. Do not auto-pick the next ticket.- Commit directly with
git commit -m. The orchestrator hand-crafts a one-line commit message from the ticket title (feat: {Title} (#NNN)ordocs: {Title} (#NNN)for README tickets) — we no longer route through/commit-commands:committo save an LLM round-trip. - One ticket per invocation. No batching. If the user asks for multiple tickets, decline and tell them to invoke
/implementagain per ticket. - No worktree isolation. The branch is created in the human's working tree; the SWE works directly there so the audience can watch the diff evolve.
make eval-onlineis BANNED. It hits production and burns budget. Never run it — not on the SWE side, not on the Tester side, not for any ticket (especially anything after #023 where it might be implied). Allowed eval targets aremake eval-devandmake eval-test. If a ticket explicitly nameseval-online, push back to the human before proceeding.
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.
- 5d ago First seen · 366 lines · 197 tokens per session scan A da4cc6dde7cd
implement is a skill published in the GitHub repository Arindam200/awesome-ai-apps (13,571 stars, last pushed 5d ago), licensed MIT. It adds 197 tokens to every session and 5,757 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.
Other skills, from other repositories
disable-model-invocation
A skill that cannot be invoked by the model.
this-is-a-very-long-skill-name-that-exceeds-the-sixty-four-character-limit-set-by-the-standard
A skill with a name that exceeds 64 characters.
multiline-description
This is a multiline description. It spans multiple lines. And should be normalized.
dynamic-resources
Example skill loaded from resourcesdiscover.
different-name
A skill with a name that doesn't match the directory.
unknown-field
A skill with an unknown frontmatter field.