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-summarizegit 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-summarize)<a href="https://agentmods.dev/skills/cloudwave818/ieee-skills/ieee-summarize"><img src="https://agentmods.dev/badge/skills/cloudwave818/ieee-skills/ieee-summarize/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-summarize"><img src="https://agentmods.dev/badge/skills/cloudwave818/ieee-skills/ieee-summarize.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 | $0.00104 | $0.00777 |
| Opus 5 | $0.00052 | $0.00388 |
| Sonnet 5 | $0.00021 | $0.00155 |
| Haiku 4.5 | $0.00010 | $0.00078 |
Grade A, and why
ieee-summarize 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IEEE Summarize Router
Use this skill before ieee-writing when the user's research materials are scattered, chronological, duplicated, or not yet in manuscript structure. The output is not final prose; it is an evidence-grounded research brief that later writing skills can use.
Do not turn messy notes into polished claims by guessing. Preserve uncertainty, source paths, missing evidence, and contradictions.
Routing Protocol
- Read
manifest.yaml. - Read every file listed under
always_load. - Detect the request axes:
material_scope: folder / pasted-notes / code-and-logs / literature-pack / mixed.output_target: research-brief / writing-input / experiment-summary / related-work-seed / claim-evidence-seed.cleanup_level: inventory / standard / deep.
- State the detected axes in one short line.
- If a folder path is provided, inventory first:
- Prefer
scripts/inventory_research_folder.py <folder>. - If the script is unsuitable, use
rg --files <folder>. - Do not read every large file blindly. Select representative and high-signal files by category.
- Prefer
- Map raw material into IEEE evidence slots:
- problem object and operating condition,
- engineering harm or practical motivation,
- method idea and implementation clues from notes/code,
- datasets, baselines, metrics, ablations, robustness, complexity, and reproducibility,
- related-work families and closest-prior-work candidates,
- contribution candidates and conclusion candidates.
- Preserve provenance. Every important extracted point should include a file path, heading, filename, or note source when available.
- Mark
solid,partial,unclear, ormissingfor each evidence block. - Hand off to the next skill explicitly:
ieee-writing,ieee-experiment,ieee-citation,ieee-reviewer, orieee-polishing.
Output Contract
Default output:
Detected axes: material_scope=..., output_target=..., cleanup_level=...
Material inventory
- Notes:
- Code:
- Experiments:
- Literature:
- Figures/tables:
- Unknown / skipped:
IEEE Research Brief
- Working title:
- Research object:
- Operating condition / constraints:
- Engineering harm:
- Core method:
- Implementation clues:
- Experiment evidence:
- Related-work seed:
- Contribution candidates:
- Conclusion candidates:
- Missing evidence:
- Contradictions / risks:
Next skill handoff
- Recommended next skill:
- Prompt-ready brief:
What ships with it
5 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.
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.
- 10d ago First seen · 81 lines · 104 tokens per session scan A 7f243f60ff65
ieee-summarize is a skill published in the GitHub repository CloudWave818/ieee-skills (275 stars, last pushed 28d ago), licensed MIT. It adds 104 tokens to every session and 777 once invoked, about $0.0005 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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