From 9a57aa2b1ff853405707048f296228a8c72d0095 Mon Sep 17 00:00:00 2001 From: Teknium <127238744+teknium1@users.noreply.github.com> Date: Mon, 20 Apr 2026 04:29:02 -0700 Subject: [PATCH] =?UTF-8?q?fix(docs):=20unbreak=20docs-site-checks=20?= =?UTF-8?q?=E2=80=94=20ascii-guard=20diagram=20+=20MDX=20`<1%`=20(#12984)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix(docs): unbreak ascii-guard lint on github-pr-review-agent diagram The intro diagram used 4 side-by-side boxes in one row. ascii-guard can't parse that layout — it reads the whole thing as one 80-wide outer box and flags the inner box borders at columns 17/39/60 as 'extra characters after right border'. Per the ascii-guard-lint-fixing skill, the only fix is to merge into a single outer box. Rewritten as one 69-char outer box with four labeled regions separated by arrows. Same semantic content, lint-clean. Was blocking docs-site-checks CI as 'action_required' across multiple PRs (see e.g. run 24661820677). * fix(docs): backtick-wrap `<1%` to avoid MDX JSX parse error Docusaurus MDX parses `<1%` as the start of a JSX tag, but `1` isn't a valid tag-name start so compilation fails with 'Unexpected character `1` (U+0031) before name'. Wrap in backticks so MDX treats it as literal code text. Found by running Build Docusaurus step on the PR that unblocked the ascii-guard step; full docs tree scanned for other `` patterns outside backticks/fences, only this one was unsafe. --- website/docs/guides/github-pr-review-agent.md | 15 +++++++++------ website/docs/reference/optional-skills-catalog.md | 2 +- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/website/docs/guides/github-pr-review-agent.md b/website/docs/guides/github-pr-review-agent.md index 530d8d6df..51b3c9799 100644 --- a/website/docs/guides/github-pr-review-agent.md +++ b/website/docs/guides/github-pr-review-agent.md @@ -13,12 +13,15 @@ description: "Build an automated AI code reviewer that monitors your repos, revi **What you'll build:** ``` -┌──────────────┐ ┌───────────────┐ ┌──────────────┐ ┌──────────────┐ -│ Cron Timer │────▶│ Hermes Agent │────▶│ GitHub API │────▶│ Review to │ -│ (every 2h) │ │ + gh CLI │ │ (PR diffs) │ │ Telegram/ │ -│ │ │ + skill │ │ │ │ Discord/ │ -│ │ │ + memory │ │ │ │ local file │ -└──────────────┘ └───────────────┘ └──────────────┘ └──────────────┘ +┌───────────────────────────────────────────────────────────────────┐ +│ │ +│ Cron Timer ──▶ Hermes Agent ──▶ GitHub API ──▶ Review │ +│ (every 2h) + gh CLI (PR diffs) delivery │ +│ + skill (Telegram, │ +│ + memory Discord, │ +│ local) │ +│ │ +└───────────────────────────────────────────────────────────────────┘ ``` This guide uses **cron jobs** to poll for PRs on a schedule — no server or public endpoint needed. Works behind NAT and firewalls. diff --git a/website/docs/reference/optional-skills-catalog.md b/website/docs/reference/optional-skills-catalog.md index f5dd2ac5b..9cb1f386b 100644 --- a/website/docs/reference/optional-skills-catalog.md +++ b/website/docs/reference/optional-skills-catalog.md @@ -110,7 +110,7 @@ The largest optional category — covers the full ML pipeline from data curation | **llava** | Large Language and Vision Assistant — visual instruction tuning and image-based conversations combining CLIP vision with LLaMA language models. | | **modal** | Serverless GPU cloud platform for running ML workloads. On-demand GPU access without infrastructure management, ML model deployment as APIs, or batch jobs with automatic scaling. | | **nemo-curator** | GPU-accelerated data curation for LLM training. Fuzzy deduplication (16x faster), quality filtering (30+ heuristics), semantic dedup, PII redaction. Scales with RAPIDS. | -| **peft-fine-tuning** | Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Train <1% of parameters with minimal accuracy loss for 7B–70B models on limited GPU memory. HuggingFace's official PEFT library. | +| **peft-fine-tuning** | Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Train `<1%` of parameters with minimal accuracy loss for 7B–70B models on limited GPU memory. HuggingFace's official PEFT library. | | **pinecone** | Managed vector database for production AI. Auto-scaling, hybrid search (dense + sparse), metadata filtering, and low latency (under 100ms p95). | | **pytorch-fsdp** | Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP — parameter sharding, mixed precision, CPU offloading, FSDP2. | | **pytorch-lightning** | High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. |