Matplotlib
zLanqing/codex-claude-academic-skills
Low-level plotting library for full customization. An agent skill from zLanqing/codex-claude-academic-skills.
Run and monitor PapersFlow DeepScan jobs. An agent skill from hashgraph-online/awesome-codex-plugins.
Install Deepscan Monitor
$ npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins deepscan-monitor --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor .claude/skills/deepscan-monitor && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "deepscan-monitor" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor into .claude/skills/deepscan-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepscan-monitor", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitorType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins deepscan-monitor --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor .agents/skills/deepscan-monitor && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "deepscan-monitor" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor into .agents/skills/deepscan-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepscan-monitor", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins deepscan-monitor --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor .cursor/skills/deepscan-monitor && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "deepscan-monitor" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor into .cursor/skills/deepscan-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepscan-monitor", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/hashgraph-online/awesome-codex-plugins.git --path plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins deepscan-monitor --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor .gemini/skills/deepscan-monitor && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "deepscan-monitor" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor into .gemini/skills/deepscan-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepscan-monitor", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install hashgraph-online/awesome-codex-plugins deepscan-monitorInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor .github/skills/deepscan-monitor && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "deepscan-monitor" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor into .github/skills/deepscan-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepscan-monitor", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install hashgraph-online/awesome-codex-plugins deepscan-monitor --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/hashgraph-online/awesome-codex-plugins.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor .opencode/skills/deepscan-monitor && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "deepscan-monitor" agent skill from https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor into .opencode/skills/deepscan-monitor/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "deepscan-monitor", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
deepscan-monitor- GitHub stars
- 1.3k
- Token cost
- ~601 tokens
- SKILL.md length
- 311 words
- Files
- 1
- Skills in repo
- 716
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Run and monitor PapersFlow DeepScan jobs. An agent skill from hashgraph-online/awesome-codex-plugins.
- Works in 7 steps: Use run_deepscan to start the job. → Immediately tell the user that the run… → Poll with get_deepscan_live_snapshot for… → …
- The user wants long-running research progress
- SKILL.md covers Workflow, Important Behavior, Progress Update Style and Plotting Guidance, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Deepscan Monitor is an agent skill from hashgraph-online/awesome-codex-plugins. Run and monitor PapersFlow DeepScan jobs. Use when the user wants long-running research progress, intermediate findings, final reports, or plotting from a completed run.
Its SKILL.md is about 600 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Data & Analytics, covering Data visualization. The repository describes itself as: A curated list of awesome OpenAI Codex / ChatGPT plugins, skills, and resources. The 1 Codex Marketplace. See live plugins at: https://hol.org/plugins/best-codex-plugins. The licence is Apache-2.0.
When your agent uses it
- The user wants long-running research progress
- Intermediate findings
- Plotting from a completed run
Example prompts
- “/deepscan-monitor”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- Use run_deepscan to start the job.
- Immediately tell the user that the run is asynchronous.
- Poll with get_deepscan_live_snapshot for the best live view of
- Fall back to get_deepscan_status if the user only wants lightweight progress checks.
- Once finalReportAvailable is true or the run is completed, call get_deepscan_report.
- Use summarize_evidence when the user wants a cross-report summary from stored DeepScan history.
- Use run_python_plot only after you have stable report data worth plotting.
What it can do on your machine
Read from SKILL.md and the folder at commit 3e1456a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Deepscan Monitor loads about 601 tokens when it runs. Until then it costs about 47 tokens; SKILL.md has 311 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from hashgraph-online/awesome-codex-plugins at commit 3e1456a, republished under its Apache-2.0 licence (© hashgraph-online). 311 words, ~601 tokens.
Download SKILL.mdSave it as
.claude/skills/deepscan-monitor/SKILL.md (or your agent's skills folder).- name
- deepscan-monitor
- description
- Run and monitor PapersFlow DeepScan jobs. Use when the user wants long-running research progress, intermediate findings, final reports, or plotting from a completed run.
DeepScan Monitor
Use this skill when the user wants Claude to manage a longer-running PapersFlow research workflow instead of a single search call.
Workflow
- Use
run_deepscanto start the job. - Immediately tell the user that the run is asynchronous.
- Poll with
get_deepscan_live_snapshotfor the best live view of:- progress
- status message
- checkpoint state
- top papers
- partial summary
- key findings
- Fall back to
get_deepscan_statusif the user only wants lightweight progress checks. - Once
finalReportAvailableis true or the run is completed, callget_deepscan_report. - Use
summarize_evidencewhen the user wants a cross-report summary from stored DeepScan history. - Use
run_python_plotonly after you have stable report data worth plotting.
Important Behavior
- Do not imply the MCP server will push completion notifications into Claude automatically.
- Poll deliberately and explain that the run is being checked.
- Prefer
get_deepscan_live_snapshotoverget_deepscan_statuswhen the user wants richer live information. - If a report is not ready yet, say that clearly and keep the next action obvious.
Progress Update Style
When a run is still active, summarize:
- current status
- progress percentage
- current stage or status message
- any checkpoint question
- notable live papers
- key findings if available
Keep updates brief unless the user asks for more detail.
Plotting Guidance
Use run_python_plot only for meaningful visualizations after you have stable report outputs, for example:
- papers by year
- citation distribution
- venue distribution
- grouped comparison across a small number of finished runs
Do not generate plots for sparse or obviously low-quality data without saying so.
Examples
- User asks: "Run a DeepScan on evaluation benchmarks for agentic retrieval systems and keep me posted."
- User asks: "Check how my DeepScan is progressing and tell me the key findings so far."
- User asks: "The run is finished, summarize the final report and plot papers by year."
- User asks: "Summarize the evidence from my recent DeepScan reports on protein structure prediction."
© hashgraph-online, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor of hashgraph-online/awesome-codex-plugins.
Open the folder on GitHubat commit 3e1456a
Compare with similar skills
Deepscan Monitor next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Deepscan Monitor this skillhashgraph-online/awesome-codex-plugins | 1.3k | — | ~601 | Automated safety check: Pass | Apache-2.0 | |
| MatplotlibzLanqing/codex-claude-academic-skills | 4.7k | 17 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Chart Visualizationbytedance/deer-flow | 84k | 1 repos | ~840 | Automated safety check: Pass | MIT | |
| Scientific Visualizationmims-harvard/OptimusKG | 147 | 19 repos | ~6.3k | Automated safety check: Pass | MIT | |
| SeabornzLanqing/codex-claude-academic-skills | 4.7k | 15 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Plot From DataTrae1ounG/paper-plot-skills | 872 | 1 repos | ~583 | Automated safety check: Pass | None |
Similar skills
Chart Visualization
bytedance/deer-flow
Picks a suitable chart type from 26 options for your data, maps the data to that chart's parameters and generates a chart image through a JavaScript script.
Scientific Visualization
mims-harvard/OptimusKG
Create publication figures with matplotlib/seaborn/plotly. An agent skill from mims-harvard/OptimusKG.
Seaborn
zLanqing/codex-claude-academic-skills
Statistical visualization with pandas integration. An agent skill from zLanqing/codex-claude-academic-skills.
Plot From Data
Trae1ounG/paper-plot-skills
Generate publication-quality matplotlib figures by selecting a pre-built paper style and substituting user data.
Scientific Figure Making
ChenLiu-1996/figures4papers
Covers publication-ready matplotlib figures for academic papers, slides, and reports—bars, trends, scatter, heatmaps, and multi-panel layouts—with this…
More from hashgraph-online/awesome-codex-plugins
All 715 skills in this repoAnime Reaction Gif
hashgraph-online/awesome-codex-plugins
Create original anime-style reaction stickers as looping GIFs and MP4 previews, using generated character pose sheets and timed key poses.
Calibredb
hashgraph-online/awesome-codex-plugins
Manage and query Calibre libraries with the calibredb CLI (local paths or Calibre Content server URLs).
Rust API Test Harness
hashgraph-online/awesome-codex-plugins
A skill your agent uses when adding, changing, testing, or debugging Rust HTTP APIs and services, especially when Codex needs black-box integration tests, random-port app startup, real database test…
Art
hashgraph-online/awesome-codex-plugins
Make a studio's game look like something at build time — a cover from a real frame of the game (free), painted covers, backdrops, textures and character plates from image models through the…
Calle
hashgraph-online/awesome-codex-plugins
Use CALL-E from Codex through the calle CLI. An agent skill from hashgraph-online/awesome-codex-plugins.
Game Balance Economy
hashgraph-online/awesome-codex-plugins
Balance game difficulty, resources, rewards, probability, progression, economies, and dominant strategies.
Related
Categories
Questions about Deepscan Monitor
What does Deepscan Monitor do?
Run and monitor PapersFlow DeepScan jobs. An agent skill from hashgraph-online/awesome-codex-plugins. Deepscan Monitor is an agent skill from hashgraph-online/awesome-codex-plugins. Run and monitor PapersFlow DeepScan jobs.
When should I use Deepscan Monitor?
Deepscan Monitor fits situations like: the user wants long-running research progress; intermediate findings; plotting from a completed run.
How do I install Deepscan Monitor in Claude Code?
Run `npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a claude-code`. Or copy the skill folder (plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor in hashgraph-online/awesome-codex-plugins) into .claude/skills/deepscan-monitor in your project. Claude Code loads it when a task matches its description.
How do I install Deepscan Monitor in Codex?
Run `npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a codex`. Or copy the skill folder (plugins/papersflow-ai/papersflow-codex-plugin/skills/deepscan-monitor in hashgraph-online/awesome-codex-plugins) into .agents/skills/deepscan-monitor in your project. Codex loads it when a task matches its description.
Can I use Deepscan Monitor in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add hashgraph-online/awesome-codex-plugins --skill deepscan-monitor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepscan-monitor, .gemini/skills/deepscan-monitor, .github/skills/deepscan-monitor and .opencode/skills/deepscan-monitor in your project.
What does Deepscan Monitor need to run?
SKILL.md names no scripts, command-line tools or credentials: Deepscan Monitor is instructions for the agent only.
Does Deepscan Monitor access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Deepscan Monitor safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Deepscan Monitor use?
Deepscan Monitor is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Deepscan Monitor use?
About 601 tokens (SKILL.md is roughly 2.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Deepscan Monitor?
Skills that share tags, products or a category with Deepscan Monitor: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Chart Visualization (bytedance/deer-flow, 84k stars), Scientific Visualization (mims-harvard/OptimusKG, 147 stars) and Seaborn (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Deepscan Monitor?
hashgraph-online (a GitHub organization) maintains it in hashgraph-online/awesome-codex-plugins, which has 1,267 GitHub stars. The repository holds 716 skills in this directory. The repository was last updated on October 10, 2026.
Source: hashgraph-online/awesome-codex-plugins on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.