Claude Code Skills Marketplace is a collection and marketplace of skills, plugins, agents, and instructions that extend Claude Code with specialized development workflows. It is for developers who want to install existing workflows or create, validate, and package their own Claude Code skills.
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 daymade/claude-code-skills --skill llm-eval-harnessgit clone --depth 1 https://github.com/daymade/claude-code-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/daymade/claude-code-skills/llm-eval-harness)<a href="https://agentmods.dev/skills/daymade/claude-code-skills/llm-eval-harness"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/llm-eval-harness/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/daymade/claude-code-skills/llm-eval-harness"><img src="https://agentmods.dev/badge/skills/daymade/claude-code-skills/llm-eval-harness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 79 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 83 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Rogue Agent · line 292 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00096 | $0.04537 |
| Opus 5.5 | $0.00038 | $0.01815 |
| Sonnet 5.5 | $0.00019 | $0.00907 |
| Haiku 4.5 | $0.00010 | $0.00454 |
Grade A, and why
llm-eval-harness 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 24d 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.
curl loop, when onboarding a provider, or debugging "system prompt 不生效" / a tok/s claim — How it starts
The opening of the file, as written. The whole thing — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Eval Harness
Overview
Give this skill an endpoint (base_url + model + an API key in an env var) and it
measures whether the endpoint actually works and whether the model is fast, stable,
protocol-correct, and good enough — instead of trusting the vendor's headline numbers.
Six dimensions, usually scattered across ad-hoc scripts that get rewritten (with the
same bugs) every time:
| Dimension | Script | Answers |
|---|---|---|
| Availability | scripts/availability_probe.py |
which model IDs work here, with 3-state error classification |
| Request fidelity | scripts/fidelity_probe.py |
do system prompt / tools / history actually REACH the model? |
| Speed | scripts/speed_probe.py |
TTFT + sustained decode tok/s, thinking-aware |
| Concurrency / stability | scripts/concurrency_probe.py |
success rate, p50/p90 latency, where it breaks |
| Protocol compliance | scripts/protocol_probe.py |
does the Anthropic thinking block actually fire when requested, AND does the endpoint accept one already sitting in history on a later turn (N≥10, both are separate checks — see Dimension 3)? |
| Quality / use-case regression | scripts/usecase_runner.py + blind judges |
does it pass your accumulated cases? |
Which dimensions to run
Route from what the user is actually doing:
- "接入新模型/新网关先测一下" / onboarding a provider → Availability → Fidelity, then speed/concurrency if adoption looks likely
- "这个 ID 能不能用 / 写个支持列表" → Availability (and read disciplines §9 before writing any "unavailable" verdict)
- "模型不听 system prompt / agent 行为怪但没报错" → Fidelity (system canary)
- "快不快 / tok/s 是真的吗" → Speed; "扛不扛得住" → Concurrency
- "这个兼容端点是真兼容吗" → Protocol + Fidelity's tools/auth checks
- "这个模型/渠道用着用着就 400 了 / session 断了 / continue 不下去了" (extended-thinking client,
multi-turn session) → Protocol
--check history-replayspecifically — this symptom is the generation check passing while history-replay silently doesn't; don't stop at generation - "换模型质量会不会掉" → Quality regression
- "要给厂商报 bug" → run the relevant probe with
--output, then follow references/vendor_evidence_protocol.md - "部署闸门 / 常驻监控 / 告警老误报" → references/production_testing_patterns.md
What ships with it
11 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.
- assets/example_usecases.json 1.8 KB
- references/evaluation_disciplines.md 25 KB
- references/production_testing_patterns.md 9.5 KB
- references/quality_blind_judge.md 4.1 KB
- references/vendor_evidence_protocol.md 7.9 KB
- scripts/availability_probe.py 15 KB runs code
- scripts/concurrency_probe.py 8.5 KB runs code
- scripts/fidelity_probe.py 17 KB runs code
- scripts/protocol_probe.py 20 KB runs code
- scripts/speed_probe.py 14 KB runs code
- scripts/usecase_runner.py 5.8 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.
- 24d ago Changed · -8 lines · -167 tokens per session 23d825018153
- 1mo ago Changed · -1 lines · +2 tokens per session e03488e53b6c
- 1mo ago First seen · 328 lines · 261 tokens per session scan A 9d5d8e75f10d
llm-eval-harness is a skill published in the GitHub repository daymade/claude-code-skills (1,473 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 4,537 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-03.
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