Borrowing it
Nothing to install: this file belongs to gaotiexinqu/OneResearchClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gaotiexinqu/OneResearchClaw/main/.cursor/skills/grounded-summary/SKILL.mdgit clone --depth 1 https://github.com/gaotiexinqu/OneResearchClawWrote 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/gaotiexinqu/oneresearchclaw/grounded-summary)<a href="https://agentmods.dev/skills/gaotiexinqu/oneresearchclaw/grounded-summary"><img src="https://agentmods.dev/badge/skills/gaotiexinqu/oneresearchclaw/grounded-summary/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/gaotiexinqu/oneresearchclaw/grounded-summary"><img src="https://agentmods.dev/badge/skills/gaotiexinqu/oneresearchclaw/grounded-summary.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 | $0.00042 | $0.04294 |
| Opus 5 | $0.00021 | $0.02147 |
| Sonnet 5 | $0.00008 | $0.00859 |
| Haiku 4.5 | $0.00004 | $0.00429 |
Grade A, and why
grounded-summary 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.
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 — 605 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grounded Summary
This skill produces a substantial, evidence-rich research report draft from:
- a grounded note, and
- a follow-up literature result.
It is not limited to meeting notes. It applies to any upstream grounded note produced by the grounding family, including:
- meeting-grounding
- document-grounding
- pptx-grounding
- table-grounding
- archive-grounding
The output of this skill is the main report draft for the current grounded item.
Write the result to:
data/report_inputs/<ground_id>/summary.md
This file is called summary.md for pipeline compatibility, but it should not behave like a short summary memo. It should behave like a full internal research report draft that preserves the important depth of both the grounded note and lit.md.
When to Use
Use this skill when:
- a grounded note already exists
- a literature result already exists
- you want to turn those materials into a substantial report draft
- you want the literature analysis to be integrated into the report body rather than reduced to a few bullets
Do not use this skill when:
- the grounded note has not been written yet
- the literature result has not been written yet
- you only want the raw literature result without report writing
- you only want final rendering/export to pdf/docx/slides/audio
Inputs
How to get ground_id
Read ground_id.txt from the grounding bundle to get the stable pipeline identifier:
data/grounded_notes/<ground_id>/ground_id.txt
Do NOT generate a new ground_id. All downstream directories reuse the same ground_id.
This skill assumes the following inputs already exist:
data/grounded_notes/<ground_id>/grounded.mddata/lit_results/<ground_id>/lit.md
Optional supporting input:
data/lit_inputs/<ground_id>/search_results.json
Pipeline Language Strategy
This skill is a mid-pipeline stage. Its output (summary.md) is always written in English only, regardless of the output_lang parameter.
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.
- 12d ago First seen · 605 lines · 42 tokens per session scan A 5094d6ed075f
grounded-summary is a skill published in the GitHub repository gaotiexinqu/OneResearchClaw (446 stars, last pushed 4mo ago), licensed MIT. It adds 42 tokens to every session and 4,294 once invoked, about $0.0002 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.
Other skills, from other repositories
quantum-qiskit
Reference qiskit 2.x patterns for variational quantum machine learning. Covers data-encoding feature maps, variational quantum classifier (VQC) training, variational quantum eigensolver (VQE) for chemistry, matrix-product-state circuits, and noise model integration. Use when writing Python code that imports qiskit…
flux-analyzer
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
fba-simulator
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
gsmm-validator
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
metabolic-study-planner
Plan publishable constraint-based metabolic modelling studies when the user has a broad biological or metabolic-engineering topic but no concrete dataset, organism, model, or hypothesis. Selects feasible BiGG/COBRA models, objectives, perturbations, analyses, metrics, figures, and risk controls before FBA code is…
gsmm-builder
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.