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 agentmods add skills/noesisvision/nasde-toolkit/nasde-benchmark-runnernpx skills add NoesisVision/nasde-toolkit --skill nasde-benchmark-runnergit clone --depth 1 https://github.com/NoesisVision/nasde-toolkitWrote 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/noesisvision/nasde-toolkit/nasde-benchmark-runner)<a href="https://agentmods.dev/skills/noesisvision/nasde-toolkit/nasde-benchmark-runner"><img src="https://agentmods.dev/badge/skills/noesisvision/nasde-toolkit/nasde-benchmark-runner.svg" alt="Measured on agentmods" 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 | $0.00139 | $0.07789 |
| Opus 5 | $0.00069 | $0.03895 |
| Sonnet 5 | $0.00028 | $0.01558 |
| Haiku 4.5 | $0.00014 | $0.00779 |
Grade A, and why
nasde-benchmark-runner 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 6d 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.
Use Python `urllib.request` (never curl — it drops the `Comet-Workspace` header): How it starts
The opening of the file, as written. The whole thing — 521 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NASDE Benchmark Runner
Run coding agent benchmarks with nasde and verify results. The two-stage pipeline: Harbor runs agents in Docker containers (functional test → reward 0/1), then an LLM-as-a-Judge scores architecture quality across multiple dimensions.
Authentication setup
Before running any benchmark, set up authentication tokens for the agents you plan to run. Both OS and auth method matter — pick the right command per row.
Step 1 — Ask the user which auth they prefer
Always ask the user before running, never assume. Two questions:
- Which agents will you run? (Claude / Codex / Gemini, any combination)
- For each agent, OAuth (subscription) or API key (per-token billing)? Default recommendation: OAuth where available — no per-token cost, no env vars to manage.
Then detect their OS and pick the matching script row from the table below. On Windows, also ask whether they're in PowerShell or WSL (cmd.exe is not directly supported — see "Windows: cmd.exe" below).
Where the auth scripts live
The OAuth scripts ship inside this skill. After nasde install-skills they are at:
- User scope (default):
~/.claude/skills/nasde-benchmark-runner/scripts/(macOS/Linux/WSL) or%USERPROFILE%\.claude\skills\nasde-benchmark-runner\scripts\(Windows PowerShell) - Project scope:
<project>/.claude/skills/nasde-benchmark-runner/scripts/(if installed withnasde install-skills --scope project) - Editable nasde checkout (devs only):
<repo>/scripts/— same files, mirrored from the skill bundle
Below, <SKILL_SCRIPTS> is shorthand for whichever absolute path applies. Resolve it once, then substitute it in every command. Verify the path with ls <SKILL_SCRIPTS> before telling the user to source anything — if the directory is missing, they need to run nasde install-skills first.
Step 2 — Run the right script per agent × OS
Priority order: Claude → Codex → Gemini. Claude is required even for non-Claude variants when [evaluation] backend = "claude" (default), because the assessment evaluator spawns claude CLI as a subprocess.
What ships with it
7 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.
- scripts/export_codex_oauth_token.ps1 2.2 KB runs code
- scripts/export_codex_oauth_token.sh 2.3 KB runs code
- scripts/export_gemini_oauth_token.ps1 2.0 KB runs code
- scripts/export_gemini_oauth_token.sh 2.1 KB runs code
- scripts/export_oauth_token.ps1 1.3 KB runs code
- scripts/export_oauth_token.sh 1.7 KB runs code
- scripts/pareto.py 21 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.
- 6d ago First seen · 521 lines · 139 tokens per session scan A d104b674e93f
nasde-benchmark-runner is a skill published in the GitHub repository NoesisVision/nasde-toolkit (12 stars, last pushed today), licensed MIT. It adds 139 tokens to every session and 7,789 once invoked, about $0.0007 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 2026-08-30.
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watch
Start EvalView watch mode to automatically re-run regression checks whenever project files change.
api-testing
REST/GraphQL API testing with automated validation — test endpoints, validate responses, check status codes, and ensure API contracts.
agent-eval-coverage
Use when the user wants to know whether their AI/agent repo has the evals and tests needed to trust changes — checking for golden/regression test sets, prompt regression tests, LLM-as-judge, behavioral & tool-use tests, hallucination/safety checks, CI gating, and metrics. Triggers on "do I have enough evals", "how do…
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