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 raja21068/AutoResearch --skill figure-descriptiongit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/raja21068/autoresearch/figure-description)<a href="https://agentmods.dev/skills/raja21068/autoresearch/figure-description"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/figure-description/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/raja21068/autoresearch/figure-description"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/figure-description.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00049 | $0.01239 |
| Opus 5 | $0.00024 | $0.00620 |
| Sonnet 5 | $0.00010 | $0.00248 |
| Haiku 4.5 | $0.00005 | $0.00124 |
Grade A, and why
figure-description 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 6d 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.
This is a copy
100% identical to figure-description — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Figure Description for Patents
Process patent figures and generate drawing descriptions based on: $ARGUMENTS
Unlike /paper-figure which generates data plots, this skill processes user-provided technical diagrams and assigns reference numerals.
Constants
FIGURE_DIR = "patent/figures/"— Output directory for figure descriptionsREFERENCE_NUMERAL_PREFIX = 100— Starting numeral for first figure's componentsNUMERAL_SERIES = 100— Each figure uses a separate 100-series (Fig 1: 100-199, Fig 2: 200-299, etc.)
Inputs
- User-provided figures (PNG, JPG, SVG, PDF) — search for them in the project directory
patent/INVENTION_DISCLOSURE.md— for understanding what components to identifypatent/CLAIMS.md— for mapping numerals to claim elements
Workflow
Step 1: Discover Figures
- Search the project directory for figure files:
- Check
patent/figures/,figures/, root directory - Look for PNG, JPG, SVG, PDF files
- Check INVENTION_BRIEF.md or INVENTION_DISCLOSURE.md for figure references
- Check
- List all discovered figures with their paths
- If figures are missing that claims require, note them as
[MISSING: description needed]
Step 2: Analyze Each Figure
For each figure found:
- Read the image using the Read tool (supports image files)
- Identify components: What labeled or visually distinct components are shown?
- Identify connections: How do components relate to each other?
- Identify flow: If it's a flowchart or sequence, what is the step order?
Step 3: Assign Reference Numerals
For each figure, assign numerals using the series convention:
| Figure | Numeral Range |
|---|---|
| FIG. 1 | 100-199 |
| FIG. 2 | 200-299 |
| FIG. 3 | 300-399 |
For each identified component:
- Assign the next available numeral in the series
- Cross-reference to the claim elements it supports
- Note if a component appears in multiple figures (use same numeral across figures)
Step 4: Generate Drawing Descriptions
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.
- 6d ago First seen · 139 lines · 49 tokens per session scan A a083520af2ac
figure-description is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 1,239 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to figure-description, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…