القائمة
Efficiently fetch HuggingFace model data (README, config.json, and optional predecessor config) using parallel curl. Use at the START of any model research task. Invoke with a HF model ID like Qwen/Qwen3.5-397B-A17B, optionally followed by a predecessor ID.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification via an 8-phase pipeline (Scope, Plan, Retrieve, Triangulate, Synthesize, Critique, Refine, Package). Use when you need comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.
Safely find and fetch arXiv papers using the structured arXiv API and the /html/ endpoint. Use instead of guessing arXiv URLs (which often 404). Invoke with an arXiv ID, an arXiv URL, or a search query like "GLM-4.5 technical report".
Research a newly released Hugging Face model and produce a comprehensive deep-research report (model card + technical reports + novelty analysis vs predecessor + glossary + benchmark analysis), exported as markdown + PDF + metadata.json, optionally emailed. Invoke with a HuggingFace model ID like moonshotai/Kimi-K2-Thinking. Use when the user gives a HF model identifier or asks to deeply research a model release.
استنادا إلى تصنيف SOC المهني
| name | fetch-model |
| description | Efficiently fetch HuggingFace model data (README, config.json, and optional predecessor config) using parallel curl. Use at the START of any model research task. Invoke with a HF model ID like Qwen/Qwen3.5-397B-A17B, optionally followed by a predecessor ID. |
Fetch all raw model data efficiently using parallel curl. This is a pi-native
helper (pi has no WebFetch tool, so raw files are fetched with curl — which is
also faster and more reliable for HuggingFace/GitHub raw endpoints).
The model ID is provided by the user (e.g. Qwen/Qwen3.5-397B-A17B).
Optionally add a predecessor model ID after a space
(e.g. Qwen/Qwen3.5-397B-A17B Qwen/Qwen3-235B-A22B).
Parse the FIRST token as the model ID and the SECOND token (if present) as the predecessor.
ORG/MODEL../{ORG}/{MODEL}/.curl to fetch all files in ONE Bash call:MODEL_ID="<first token>"
PREDECESSOR="<second token, if present>"
ORG=$(echo "$MODEL_ID" | cut -d'/' -f1)
MODEL=$(echo "$MODEL_ID" | cut -d'/' -f2)
mkdir -p "./${ORG}/${MODEL}"
curl -sL --max-time 60 -A "Mozilla/5.0" "https://huggingface.co/${MODEL_ID}/raw/main/README.md" > "./${ORG}/${MODEL}/readme_raw.md" &
curl -sL --max-time 60 -A "Mozilla/5.0" "https://huggingface.co/${MODEL_ID}/raw/main/config.json" > "./${ORG}/${MODEL}/config.json" &
if [ -n "$PREDECESSOR" ]; then
curl -sL --max-time 60 -A "Mozilla/5.0" "https://huggingface.co/${PREDECESSOR}/raw/main/config.json" > "./${ORG}/${MODEL}/predecessor_config.json" &
fi
wait
echo "Fetched: readme_raw.md, config.json${PREDECESSOR:+, predecessor_config.json}"
read config.json to identify:
architectures / model_type)num_experts, n_routed_experts, num_experts_per_tok)max_position_embeddings), rope_thetadiff the two configs to identify what changed:diff "./${ORG}/${MODEL}/predecessor_config.json" "./${ORG}/${MODEL}/config.json" || true
readme_raw.md for arXiv links (pattern: arxiv.org/abs/XXXX.XXXXX or
arxiv.org/html/XXXX.XXXXX) and any blog/GitHub links. List the arXiv IDs
found — do NOT fetch them here (use the fetch-arxiv skill for that).Report:
./{org}/{model}/