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 Inference1/learn-agent-skills --skill ascend-inference-repos-copilotgit clone --depth 1 https://github.com/Inference1/learn-agent-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/inference1/learn-agent-skills/ascend-inference-repos-copilot)<a href="https://agentmods.dev/skills/inference1/learn-agent-skills/ascend-inference-repos-copilot"><img src="https://agentmods.dev/badge/skills/inference1/learn-agent-skills/ascend-inference-repos-copilot/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/inference1/learn-agent-skills/ascend-inference-repos-copilot"><img src="https://agentmods.dev/badge/skills/inference1/learn-agent-skills/ascend-inference-repos-copilot.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00353 | $0.05325 |
| Opus 5 | $0.00177 | $0.02662 |
| Sonnet 5 | $0.00071 | $0.01065 |
| Haiku 4.5 | $0.00035 | $0.00532 |
Grade A, and why
ascend-inference-repos-copilot scanned grade A with 1 finding 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 11d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **GitCode Issues** (for `verylucky01/*` repos — replace `verylucky01` with `Ascend` in the API path): `curl --location 'https://api.gitcode.com/api/v5/search/issues?q=<keywords>&repo=<Ascend/repo>' --header 'Authorizat How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Specialized Intelligent Q&A for Ascend Inference Open-Source Code Repositories
Answer technical questions about Ascend inference repositories with evidence-based, causally-grounded responses. DeepWiki is the primary knowledge source; embedded knowledge below is orientation context to help formulate better queries.
Quick Diagnostic (for error/troubleshooting questions)
Before diving into deep research, complete this checklist — it resolves ~60% of issues and takes 2 minutes. Do not skip steps in favor of going straight to GitHub Issues.
- Version alignment — vllm-ascend requires strict 1:1 match with upstream vLLM (e.g., v0.18.0 ↔ v0.18.0). Each release also pins exact CANN and torch_npu versions. Misalignment is the #1 root cause.
- Isolate graph mode — add
--enforce-eagerto rule out ACLGraph/torchair as the cause. - Check the error code — ACL error codes point directly to the failure layer:
507015(stream sync),561000(MoE dispatch),503900(KV transfer). - Hardware platform — 310P (Atlas 300I Duo) has a significantly smaller operator library than 910B. Features that work on 910B may simply not exist on 310P.
- Feature combination — MTP + FULL_DECODE_ONLY, NZ layout + RL training, and W8A8 + MTP are known incompatible combinations.
- max_model_len — silently too-large values cause OOM, garbled output, and shape errors. Always verify or suggest reducing it.
- Enable synchronous error reporting — async execution hides the real error. Set these before reproducing:
export ASCEND_LAUNCH_BLOCKING=1 # synchronous NPU execution — accurate stack traces export TASK_QUEUE_ENABLE=0 # disables async task queue export ASCEND_GLOBAL_LOG_LEVEL=0 # verbose CANN logs
Ask for python collect_env.py output (from the vllm-ascend repo root) when you need version details. Key fields to check: vllm_ascend, vllm, torch_npu, CANN, driver.
Research strategy: Complete the Quick Diagnostic checklist first, then use GitHub Issues search for version-specific bugs or recent fixes. Deep research should supplement standard checks, not replace them.
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.
- 11d ago First seen · 228 lines · 353 tokens per session scan A 9b384e6f1bc5
ascend-inference-repos-copilot is a skill published in the GitHub repository Inference1/learn-agent-skills (11 stars, last pushed 3mo ago), licensed MIT. It adds 353 tokens to every session and 5,325 once invoked, about $0.0018 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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