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 CloudWave818/ieee-skills --skill ieee-experimentgit clone --depth 1 https://github.com/CloudWave818/ieee-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/cloudwave818/ieee-skills/ieee-experiment)<a href="https://agentmods.dev/skills/cloudwave818/ieee-skills/ieee-experiment"><img src="https://agentmods.dev/badge/skills/cloudwave818/ieee-skills/ieee-experiment/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/cloudwave818/ieee-skills/ieee-experiment"><img src="https://agentmods.dev/badge/skills/cloudwave818/ieee-skills/ieee-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00068 | $0.00443 |
| Opus 5.5 | $0.00027 | $0.00177 |
| Sonnet 5.5 | $0.00014 | $0.00089 |
| Haiku 4.5 | $0.00007 | $0.00044 |
Grade A, and why
ieee-experiment 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 2mo 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.
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.
What it actually says
IEEE Experiment Router
Use this skill to decide whether the experiments prove the paper's claims. The primary output is an evidence audit, not generic advice.
Do not design experiments from memory alone. Follow the routing protocol and load the selected fragments.
Routing Protocol
- Read
manifest.yaml. - Read every file listed under
always_load. - Detect the axes:
task_type: classification / detection / regression / control / signal-processing / communications / optimization / hardware-system / general.evidence_type: baseline / ablation / robustness / complexity / statistical / real-world / reproducibility.failure_mode: missing-traditional-baseline / unfair-comparison / weak-ablation / no-condition-test / overclaimed-results / insufficient-reproducibility.stage: planning / audit / result-writing / reviewer-response.
- State the detected axes in one short line.
- Load only the matching fragments.
- Build or update a claim-evidence matrix.
- Identify missing experiments by reviewer impact.
Output Contract
Default output:
Detected axes: task_type=..., evidence_type=..., failure_mode=..., stage=...
Claim-evidence matrix
Claim | Required evidence | Current evidence | Missing experiment | Review risk
Priority fixes
1. ...
For experiment planning, return an experiment plan with baselines, metrics, variables, controlled conditions, and expected claims.
For result writing, return IEEE-style result paragraphs and flag any claim that lacks evidence.
Red Lines
Do not invent numerical results, datasets, baseline performance, p-values, hardware metrics, or statistical significance.
Do not recommend unnecessary experiments that do not support a stated claim.
Do not treat "more experiments" as automatically better. Prioritize experiments that close the reviewer's proof gap.
What ships with it
32 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.
- agents/openai.yaml 226 B
- manifest.yaml 3.3 KB
- static/core/claim-evidence-matrix.md 599 B
- static/core/output-format.md 465 B
- static/core/stance.md 391 B
- static/core/workflow.md 632 B
- static/fragments/evidence_type/ablation.md 266 B
- static/fragments/evidence_type/baseline.md 375 B
- static/fragments/evidence_type/complexity.md 368 B
- static/fragments/evidence_type/real-world.md 368 B
- static/fragments/evidence_type/reproducibility.md 372 B
- static/fragments/evidence_type/robustness.md 326 B
- static/fragments/evidence_type/statistical.md 349 B
- static/fragments/failure_mode/insufficient-reproducibility.md 279 B
- static/fragments/failure_mode/missing-traditional-baseline.md 300 B
- static/fragments/failure_mode/no-condition-test.md 377 B
- static/fragments/failure_mode/overclaimed-results.md 340 B
- static/fragments/failure_mode/unfair-comparison.md 320 B
- static/fragments/failure_mode/weak-ablation.md 275 B
- static/fragments/stage/audit.md 149 B
- static/fragments/stage/planning.md 213 B
- static/fragments/stage/result-writing.md 210 B
- static/fragments/stage/reviewer-response.md 292 B
- static/fragments/task_type/classification.md 466 B
- static/fragments/task_type/communications.md 409 B
- static/fragments/task_type/control.md 369 B
- static/fragments/task_type/detection.md 401 B
- static/fragments/task_type/general.md 257 B
- static/fragments/task_type/hardware-system.md 382 B
- static/fragments/task_type/optimization.md 348 B
- static/fragments/task_type/regression.md 335 B
- static/fragments/task_type/signal-processing.md 373 B
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.
- 2mo ago First seen · 51 lines · 68 tokens per session scan A 21048e83b92d
ieee-experiment is a skill published in the GitHub repository CloudWave818/ieee-skills (429 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 443 once invoked, about $0.0003 per session on Opus 5.5. 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.
ieee-experiment compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ieee-experimentCloudWave818/ieee-skills | Skill | 429 | 2mo ago | MIT | A | 68 | Codex |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |