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 swyxio/skills --skill live-ai-pipelinesgit clone --depth 1 https://github.com/swyxio/skillsWrote 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/swyxio/skills/live-ai-pipelines)<a href="https://agentmods.dev/skills/swyxio/skills/live-ai-pipelines"><img src="https://agentmods.dev/badge/skills/swyxio/skills/live-ai-pipelines/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/swyxio/skills/live-ai-pipelines"><img src="https://agentmods.dev/badge/skills/swyxio/skills/live-ai-pipelines.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00064 | $0.00913 |
| Opus 5 | $0.00032 | $0.00456 |
| Sonnet 5 | $0.00013 | $0.00183 |
| Haiku 4.5 | $0.00006 | $0.00091 |
Grade A, and why
live-ai-pipelines 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 12d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Live AI Pipelines
Use this skill when people need to inspect useful work while later work is still running, or reopen a run after interruption. It owns logical stages, artifacts, progress, recovery, and publication—not a particular queue, database, or provider SDK.
Pair with ai-engineering when model request/retry/rate-limit behavior is itself the problem. Pair with a provider/runtime skill only for that platform’s current API behavior.
Useful defaults
- Keep canonical complete artifacts separate from provisional previews.
- Give artifacts and independently resumable items stable IDs.
- Make the UI a projection of stored results and events, not the source of workflow truth.
- Publish a complete new snapshot rather than exposing a half-rendered site.
- Let core output publish with optional enrichment visibly pending unless the user makes that enrichment a completion requirement.
Workflow
1. Choose the level of machinery
Identify deterministic preparation, independent fan-out, fan-in synthesis, projection, and audit/publication. A local runner with atomic files, JSONL events, and polling/SSE is often enough. Add a queue, database, ownership lease, or process supervision only when multiple workers, long detachment, or safe handoff actually needs it.
For each stage, choose its item key, input/output contract, retry behavior, and whether a partial result is safe to show. Feed fan-in synthesis a bounded normalized projection of completed artifacts, not the raw corpus and every intermediate response.
2. Define complete versus preview data
Validate a response before writing a canonical artifact. Write it atomically, then emit a completion/failure event and update a replaceable status snapshot. Streamed token fragments and drafts may support a preview, but do not give them canonical links or present them as complete facts.
For a costly broad run, a small representative calibration and per-request-class budget notes can prevent systematic truncation. They are planning aids, not a prerequisite for an exploratory pilot.
What ships with it
8 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.
- agents/openai.yaml 264 B
- references/architecture.md 2.9 KB
- references/event-schema.md 2.7 KB
- references/skill-routing.md 8.5 KB
- scripts/event_journal.py 3.9 KB runs code
- scripts/incremental_json.py 5.7 KB runs code
- scripts/publish_snapshot.py 2.8 KB runs code
- scripts/serve_progress.py 5.9 KB runs code
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.
- 12d ago First seen · 59 lines · 64 tokens per session scan A 014659da43f3
live-ai-pipelines is a skill published in the GitHub repository swyxio/skills (160 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 913 once invoked, about $0.0003 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
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.