vllm-ascend-workspace: Skill for Codex

.agents/skills/curate-workspace-knowledge/SKILL.md

curate-workspace-knowledge is a skill for Codex from maoxx241/vllm-ascend-workspace. It costs 126 tokens per session (621 once invoked), scanned A, original, MIT.

A review workflow for managing verified vLLM Ascend workspace knowledge, including proposed entries that may be promoted, merged, rejected, or retired. vLLM Ascend is a system for running language models on Ascend hardware.

In plain words
What is it for?
Reviewing knowledge candidates, checking evidence, promoting or merging valid entries, rejecting unsupported ones, retiring stale entries, and running validation.
Why use it?
It keeps one trusted knowledge store and prevents duplicate, unsupported, temporary, or outdated entries from becoming official project guidance.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents).

This is maoxx241/vllm-ascend-workspace's own configuration. It tells Codex how to work on vllm-ascend-workspace itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything vllm-ascend-workspace configures →

Reuse

Borrowing it

Nothing to install: this file belongs to maoxx241/vllm-ascend-workspace. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/maoxx241/vllm-ascend-workspace/main/.agents/skills/curate-workspace-knowledge/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/maoxx241/vllm-ascend-workspace

Made for: Codex.

Wrote 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.

agentmods badge for curate-workspace-knowledge

README.md
[![agentmods](https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/curate-workspace-knowledge/github.svg)](https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/curate-workspace-knowledge)
Your own site
<a href="https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/curate-workspace-knowledge"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/curate-workspace-knowledge/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.

agentmods 80×15 button for curate-workspace-knowledge

Your own site · 80×15
<a href="https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/curate-workspace-knowledge"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/curate-workspace-knowledge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 126 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 621 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00126 $0.00621
Opus 5 $0.00063 $0.00311
Sonnet 5 $0.00025 $0.00124
Haiku 4.5 $0.00013 $0.00062

Measured 12d ago against content hash 065b3431811d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

curate-workspace-knowledge 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.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/knowledge_curate.py, tests/test_knowledge_curate.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/curate-workspace-knowledge/SKILL.md · 57 lines

How it starts

The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Curate Workspace Knowledge

Keep .agents/knowledge/ as the only formal project knowledge source. Treat .vaws-local/knowledge/candidates/ as an untracked review queue, never as a second authoritative store.

Workflow

  1. Run scripts/knowledge_curate.py list.
  2. Inspect one candidate and its possible formal matches.
  3. Check that the root cause is confirmed, the original symptom was rerun, and at least one stable test, commit, issue, or PR evidence item exists.
  4. Choose exactly one disposition:
    • promote a novel candidate;
    • merge it into an existing entry with the same cause and scope;
    • reject an unsupported, transient, secret-bearing, or duplicate candidate;
    • deprecate a stale formal entry.
  5. Run .agents/scripts/knowledge_validate.py and the owning Skill's tests.
  6. Commit the formal knowledge change together with any regression protection.

Entry point

scripts/knowledge_curate.py provides:

  • list: return compact candidate summaries;
  • inspect: return one full candidate plus possible formal matches;
  • promote: create one experimental or active formal entry;
  • merge: merge evidence and occurrences into an existing formal entry;
  • reject: archive a candidate locally without changing formal knowledge;
  • deprecate: retain a formal entry while marking it obsolete.

Read only the reference needed for the active operation:

Rules

  • Never parse or persist a full transcript.
  • Never promote inconclusive verification.
  • Never promote knowledge supported only by untracked or unstable evidence.
  • Require a regression test or two verified occurrences before active.
  • Prefer merge over a new entry when cause and applicability match.
  • Use --force-new only after reviewing an identical fingerprint with a different confirmed cause.
  • Keep deterministic behavior in the owning Skill's scripts and tests; store only the cross-session explanation, scope, fingerprints, and evidence here.
  • Do not copy upstream model-adapter lessons or profiler-local counterexamples into workspace knowledge unless the new record adds workspace-specific scope and references the upstream source.

Read the full file on GitHub · 57 lines

Files

What ships with it

6 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.

Changes

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.

  1. 12d ago First seen · 57 lines · 126 tokens per session scan A 065b3431811d

Subscribe to this mod's changes

curate-workspace-knowledge is a skill published in the GitHub repository maoxx241/vllm-ascend-workspace (36 stars, last pushed 7d ago), licensed MIT. It adds 126 tokens to every session and 621 once invoked, about $0.0006 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.

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