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EvoScientist/EvoSkills
🧬 Extend EvoScientist with Installable Skill & Knowledge Packs
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Individual skills in this repository
- academic-slides
skills/academic-slides - evo-memory
skills/evo-memory - evomath-tao
skills/evomath-tao - experiment-craft
skills/experiment-craft - experiment-iterative-coder
skills/experiment-iterative-coder - experiment-pipeline
skills/experiment-pipeline - nano-banana
skills/nano-banana - paper-figures
skills/paper-figures - paper-graph
skills/paper-graph - paper-navigator
skills/paper-navigator - paper-planning
skills/paper-planning - paper-rebuttal
skills/paper-rebuttal - paper-review
skills/paper-review - paper-writing
skills/paper-writing - research-ideation
skills/research-ideation - research-survey
skills/research-survey
How it works
academic-slides
When your agent uses it
Use this skill for creating or refining an academic slide deck and the talk built around it: structuring a conference talk, thesis defense, lab meeting, or paper-to-slides deck; deciding the narrative arc and slide breakdown; improving slide design and visual hierarchy; planning rehearsal, timing, Q&A, and backup slides; or generating the .pptx. Reach for it when the user is shaping the presentation itself. Do not use for writing the paper, producing standalone speaker notes/scripts/transcripts, making posters, creating isolated figures/charts outside a slide deck, or building non-academic presentations.
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
1,551 tokensWhen the skill is activated
Reference documents
10,542 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Before You Start: Three Questions
- Core Workflow
- Artifact Sources from Other Skills
- Counterintuitive Presentation Rules
- Reference Navigation
Files
SKILL.mdcore7,151 bytesassets/talk-outline-template.mddoc2,203 bytesreferences/delivery-and-qa.mddoc6,664 bytesreferences/slide-creation.mddoc19,369 bytesreferences/slide-design.mddoc5,701 bytesreferences/talk-structure.mddoc5,520 bytesrequirements.txtdoc24 bytes
Read SKILL.md
# Academic Slides
A structured approach to creating academic presentation slides and preparing research talks. Covers narrative structure, slide design, visual hierarchy, delivery technique, and Q&A preparation.
## When to Use This Skill
- User wants to create presentation slides for a research talk
- User asks about structuring an academic presentation
- User needs to prepare for a conference talk, thesis defense, or lab meeting
- User wants to design a slide deck from a paper or research project
- User mentions "slides", "presentation", "talk", "defense", "poster talk"
---
View the complete source on GitHubevo-memory
When your agent uses it
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after research-ideation), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing research-ideation cycles or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use research-ideation).
What it loads into context
Frontmatter
232 tokensAlways available to the agent
SKILL.md
3,903 tokensWhen the skill is activated
Reference documents
12,449 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- The Learning Layer
- Two Memory Stores
- Three Evolution Mechanisms
- Reading Memory at Cycle Start
- Memory Maintenance
- Counterintuitive Memory Rules
- Memory Integration Points
- Reference Navigation
Files
SKILL.mdcore19,134 bytesassets/evolution-report-template.mddoc1,169 bytesassets/experiment-memory-template.mddoc1,179 bytesassets/ideation-memory-template.mddoc2,061 bytesreferences/ese-protocol.mddoc9,613 bytesreferences/ide-protocol.mddoc7,313 bytesreferences/ive-protocol.mddoc11,610 bytesreferences/memory-schema.mddoc13,480 bytesreferences/paper-prompts.mddoc10,328 bytes
Read SKILL.md
# Evo-Memory
A persistent learning layer that accumulates research knowledge across ideation and experimentation cycles. Maintains two memory stores and implements three evolution mechanisms that feed learned patterns back into future research.
## When to Use This Skill
- User has completed an `research-ideation` and needs to update Ideation Memory
- User has completed (or failed) an `experiment-pipeline` and needs to update memory
- User is starting a new research cycle and wants to load prior knowledge
- User asks about research memory, learned patterns, or cross-cycle knowledge
- User mentions "evo-memory", "update memory", "what worked before", "research history", "evolution"
## The Learning Layer
View the complete source on GitHubevomath-tao
When your agent uses it
Use this skill whenever the user submits a non-trivial mathematical claim that needs a rigorous proof or audit. Trigger on IMO/Putnam/USAMO/Olympiad-style problems, ML/AI theoretical statements, research conjectures, suspected-false claims, multi-step proofs the user already failed on, proof drafts with possible hidden assumptions, or any request containing 'prove rigorously', 'verify this', 'is this true', 'find the gap', 'audit my proof', 'find a counterexample', or 'use EvoMath' that targets a mathematical claim. Activate also when the problem requires more than three reasoning steps. Do NOT use for single-step calculations, definition lookups, textbook exercises with a known recipe, code analysis tasks, literature survey questions, pure symbolic manipulation, or non-mathematical applications of those trigger phrases (e.g., 'is it true that GPT-4 can solve math?', 'verify this LaTeX syntax'); hand those back instead.
What it loads into context
Frontmatter
256 tokensAlways available to the agent
SKILL.md
3,514 tokensWhen the skill is activated
Reference documents
30,497 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Methodology Anchor — Terence Tao's Research-Math Practice
- Operating Rules
- Fast Exit
- Execution Protocol (TodoWrite + Validation)
- Workflow
- Status Labels
- Final Answer Shape
- Optional Helpers
Scripts executed separately
scripts/evomath_workspace.py
Files
SKILL.mdcore15,126 bytesSKILL.mdcore15,126 bytesSKILL.mdcore15,126 bytesSKILL.mdcore15,126 bytesreferences/angles-by-type.mddoc9,447 bytesreferences/claim-memory.mddoc27,268 bytesreferences/confidence-rules.mddoc6,188 bytesreferences/grading-taxonomy.mddoc15,358 bytesreferences/handoff-template.mddoc9,848 bytesreferences/intake-checklist.mddoc8,216 bytesreferences/model-tier.mddoc3,716 bytesreferences/output-formats.mddoc9,876 bytesreferences/output-schema.mddoc14,810 bytesreferences/phase-4-audit.mddoc11,876 bytesreferences/test-prompts.mddoc7,676 bytesscripts/evomath_workspace.pyruns14,934 bytes
Read SKILL.md
# EvoMath (Tao-style)
EvoMath is a lightweight proof workflow for contest-style mathematical
reasoning. Its job is to produce a rigorous proof, a verified counterexample, a
useful partial result, or a clear handoff. Keep the process small; do not run a
heavy audit pipeline by default.
## Methodology Anchor — Terence Tao's Research-Math Practice
This skill operationalizes the way Terence Tao approaches research mathematics:
1. **Compute small cases first** (Kepler before Newton) — build intuition from data before reaching for theory.
2. **Try the standard toolbox broadly before going deep** — most hard problems crack to a standard technique; the few that don't only reveal which after several have failed.
3. **Hold rigor and intuition together** (post-rigorous mathematics) — trust intuition, but verify every step. "It feels right" is a hypothesis, not a proof.View the complete source on GitHubexperiment-craft
When your agent uses it
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
What it loads into context
Frontmatter
221 tokensAlways available to the agent
SKILL.md
1,586 tokensWhen the skill is activated
Reference documents
1,525 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- The Debugging Mindset
- 5-Step Diagnostic Flow
- Counterintuitive Experiment Rules
- Experiment Logging
- Return to experiment-pipeline
- Handoff to Paper Writing
- Reference Navigation
Files
SKILL.mdcore8,038 bytesassets/experiment-log-template.mddoc788 bytesreferences/debugging-methodology.mddoc6,691 bytes
Read SKILL.md
# Experiment Craft
A systematic approach to running, debugging, and iterating on research experiments. The critical skill is not running more experiments — it's understanding WHY experiments fail.
## When to Use This Skill
- User's experiment is not working or producing unexpected results
- User needs help diagnosing why a method fails on certain data
- User wants to organize their experiment process with structured logging
- User asks about debugging research code or iterating on approaches
- User mentions "experiment debugging", "why doesn't this work", "experiment log", "results are wrong"
> This skill is typically loaded from within `experiment-pipeline` when a stage attempt fails. After debugging, return to the pipeline's stage-gate structure to continue. Can also be used standalone for any experiment debugging.
View the complete source on GitHubexperiment-iterative-coder
When your agent uses it
Iterative code refinement through plan → code → evaluate → refine cycles. Runs lint checks (ruff), tests (pytest), and structured self-evaluation each cycle, then diagnoses failures and refines. Decomposes complex tasks into sequential phases, iterates up to 3 times per phase (10 total). Use when: the main agent delegates a code task with 'MODE: MORE_EFFORT', the user selects 'More Effort' code generation mode, or the task explicitly requests iterative refinement for higher code quality. Do NOT use for single-pass code generation (Lite mode), experiment pipeline orchestration (use experiment-pipeline), or diagnosing a specific experiment failure (use experiment-craft).
What it loads into context
Frontmatter
197 tokensAlways available to the agent
SKILL.md
2,360 tokensWhen the skill is activated
Reference documents
1,109 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- The Iteration Mindset
- Before Starting: Load Context
- Phase Decomposition
- The Iteration Loop
- Completion
- Counterintuitive Iteration Rules
- Skill Integration
- Reference Navigation
Files
SKILL.mdcore9,882 bytesSKILL.mdcore9,882 bytesSKILL.mdcore9,882 bytesassets/iteration-log-template.mddoc575 bytesreferences/evaluation-protocol.mddoc3,757 bytes
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# Iterative Coder
Iterative code refinement through structured plan → code → evaluate → refine cycles. Each cycle runs objective checks (lint, tests) and self-evaluation, then diagnoses failures and plans targeted improvements. Reaches production quality in 3-8 iterations.
## When to Use This Skill
- Main agent delegates a code task prefixed with "MODE: MORE_EFFORT"
- User selected "More Effort" mode for code generation
- Task requires high code quality with verified correctness
- Task involves complex implementation (5+ files, multiple modules)
- You want to iterate on code quality rather than submit first-pass code
- You mention "iterative refinement", "code quality loop", "plan-code-evaluate"
## The Iteration MindsetView the complete source on GitHubexperiment-pipeline
When your agent uses it
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
What it loads into context
Frontmatter
179 tokensAlways available to the agent
SKILL.md
3,718 tokensWhen the skill is activated
Reference documents
5,768 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- The Pipeline Mindset
- Before Starting: Load Prior Knowledge
- 4-Stage Pipeline Overview
- Stage 1: Initial Implementation
- Stage 2: Hyperparameter Tuning
- Stage 3: Proposed Method
- Stage 4: Ablation Study
- Integrating experiment-craft for Diagnosis
- Code Trajectory Logging
- Counterintuitive Pipeline Rules
- Handoff to Paper Writing
- Skill Integration
- Reference Navigation
Files
SKILL.mdcore17,800 bytesassets/pipeline-tracker-template.mddoc2,891 bytesassets/stage-log-template.mddoc1,794 bytesreferences/attempt-budget-guide.mddoc6,411 bytesreferences/code-trajectory-logging.mddoc6,074 bytesreferences/stage-protocols.mddoc7,945 bytes
Read SKILL.md
# Experiment Pipeline
A structured 4-stage framework for executing research experiments from initial implementation through ablation study, with attempt budgets and gate conditions that prevent wasted effort. This follows the Experiment Tree Search design from the EvoScientist paper, where the engineer agent iteratively generates executable code, runs experiments, and records structured execution results at each stage.
## When to Use This Skill
- User has a planned experiment and needs to organize the execution workflow
- User wants to systematically validate a novel method against baselines
- User asks about experiment stages, attempt budgets, or when to move on
- User needs to reproduce baseline results before testing their method
- User mentions "experiment pipeline", "baseline first", "ablation study", "stage budget", "experiment execution"
## The Pipeline Mindset
View the complete source on GitHubnano-banana
When your agent uses it
Generate professional presentation slides and high-quality illustrations using Gemini image generation API (Nano Banana 2), with interactive browser-based review and iterative editing. Full workflow: content planning conversation → slides_plan.json → batch image generation → review with feedback → targeted slide editing → PPTX packaging. Use when: user wants to create a presentation, make slides, generate a PPT/PPTX, prepare a talk deck, design visual slide content, or generate high-quality figures/illustrations for papers and documents. Do NOT use for: writing academic papers (use paper-writing) or planning academic conference talk narrative structure (use academic-slides).
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
3,826 tokensWhen the skill is activated
Reference documents
3,013 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Before You Start: Prerequisites
- Core Workflow
- Phase 1: Content Planning Conversation
- Phase 2: Generate slides_plan.json
- Phase 3: Select Style & Generate Slides
- Phase 4: Launch Review Server
- Phase 5: Apply Feedback Edits
- Phase 6: Package as PPTX
- Phase 7: Cleanup
- Counterintuitive Rules
- Scripts Reference
- Style Template Format
Scripts executed separately
scripts/atlas_image.pyscripts/edit_slide.pyscripts/generate_ppt.pyscripts/package_pptx.pyscripts/serve_viewer.py
Files
SKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesSKILL.mdcore16,007 bytesrequirements.txtdoc76 bytesscripts/atlas_image.pyruns8,973 bytesscripts/edit_slide.pyruns6,857 bytesscripts/generate_ppt.pyruns21,469 bytesscripts/package_pptx.pyruns3,137 bytesscripts/serve_viewer.pyruns4,977 bytesstyles/gradient-glass.mddoc4,599 bytesstyles/lineal-color.mddoc4,547 bytesstyles/vector-illustration.mddoc4,732 bytestemplates/viewer.htmlasset10,340 bytes
Read SKILL.md
# Nano Banana
Generate high-quality presentation slides as images using Gemini's image generation API, review them interactively in a browser, and iteratively edit based on feedback.
## When to Use This Skill
- User asks to create a presentation, slide deck, or PPT
- User wants to generate visual slides for a talk or lecture
- User has a document or outline and wants slides based on it
- User says "make me a PPT", "generate slides", "create a presentation"
- User wants to edit or refine existing generated slides
- User needs high-quality figures, diagrams, or illustrations for papers or documents
- User asks to generate research figures, architecture diagrams, or concept illustrations
View the complete source on GitHubpaper-figures
When your agent uses it
Use this skill to produce standalone, publication-ready PNG graphics and reproducible matplotlib scripts from tabular data (CSVs or DataFrames). It renders numerical data into formal scientific visualizations—including scatter, line, bar, pie, ring, bubble, tornado, KDE, violin, box, heatmap, histogram, and area charts, plus composite multi-panel figures that combine these types in a single image—for scholarly manuscripts. Only trigger when the final deliverable is an individual image file. Do not use for interactive dashboards or HTML-rendered outputs (Plotly, Streamlit, Quarto, Jupyter notebooks), nor when the request involves building a container document or presentation that includes charts (slide deck, conference poster). It is not for non-data conceptual illustrations like flowcharts, algorithm schematics, or process diagrams. This skill focuses on high-fidelity data rendering into final image files, not presentation design, document layout, or reverse-engineering code from existing screenshots.
What it loads into context
Frontmatter
248 tokensAlways available to the agent
SKILL.md
3,934 tokensWhen the skill is activated
Reference documents
10,187 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Inputs and Output
- Setup
- Core Workflow
- Core Design Rules
- Counterintuitive Notes
- Reference Navigation
Scripts executed separately
scripts/validate_figure.py
Files
SKILL.mdcore17,655 bytesSKILL.mdcore17,655 bytesSKILL.mdcore17,655 bytesreferences/chart-types.mddoc26,345 bytesreferences/publication-style.mddoc13,768 bytesrequirements.txtdoc30 bytesscripts/validate_figure.pyruns6,179 bytes
Read SKILL.md
# Paper Figures
A structured approach to producing publication-ready chart figures (PNG) from tabular data plus a natural-language description, using matplotlib.
## When to Use This Skill
- User provides a CSV / dataframe / inline data and asks for a chart
- User describes a target figure in words and wants it rendered
- User mentions "figure", "plot", "chart", "visualize", "render" for a paper or experiment
---
## Inputs and Output
View the complete source on GitHubpaper-graph
When your agent uses it
Map the genealogical lineage and historical progression of a research field. Visualize how earlier technical challenges led to later approaches and improvements, producing a Markdown report with embedded Mermaid diagrams. Trigger when the user asks for a topic's developmental trajectory, a model's family tree, significant predecessors or follow-ups to a seed paper, or how a research line matured over time. Do not trigger for inventories of datasets, benchmarks, libraries, or other artifacts; finding one latest paper; simple keyword search; one-paper summaries; or head-to-head comparisons. Use this skill for chronological synthesis across multiple works, not for bibliography cataloging or one-off retrieval.
What it loads into context
Frontmatter
181 tokensAlways available to the agent
SKILL.md
6,958 tokensWhen the skill is activated
Reference documents
3,261 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Inputs and Output
- Setup
- Runbook
- Verification
- Design notes (for editors of this skill, not the runtime agent)
Scripts executed separately
scripts/audit.pyscripts/cli.pyscripts/config.pyscripts/deepxiv_client.pyscripts/logger.pyscripts/mermaid.pyscripts/paper_md.pyscripts/pipeline.pyscripts/prompts.pyscripts/web_api.py
Files
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Read SKILL.md
# Paper Graph
Build a Markdown report with embedded Mermaid diagrams showing how research on a user-specified topic (or paper) evolved — clustered into challenges → solutions and traced as per-solution evolution paths.
The skill has no outbound LLM dependency. The host agent provides all LLM calls; the skill provides deterministic data fetchers (S2 / DeepXiv), prompt templates, markdown parsers, and Mermaid renderers. Run the runbook below step-by-step.
**Execution requirement.** When the caller already provides `parsed_query.json`, `seed.json` and `papers.json` (wherever they are, for example in an `input/` directory), copy them into the workdir under those names, treat steps 1–5 as complete and start at step 6. Still run every deterministic CLI step from classify through assemble; do not replace parsing, edge audit, rendering, or assembly with hand-authored Mermaid. Before finishing, verify that every parsed solution has a detail render and an audit verdict file, even when that file is an empty JSON list. Treat `assemble_report` as the only final-report writer: do not edit or append hand-authored edges afterward, even when a caller asks to display uncertain relationships. Preserve uncertainty in verdict JSON.
## When to Use This Skill
Trigger when the user asks something like:
- "Show me the history of <topic>" / "How did <topic> evolve?"
- "Where does <paper> stem from?" / "What did <paper> build on?"View the complete source on GitHubpaper-navigator
When your agent uses it
Find and read academic papers (S2 + arXiv). Disambiguate ambiguous queries, search by keyword + citation graph + recommendations + snippets, judge relevance against an authored rubric, and read with L1/L2/L3 strategy. Trigger phrases: find papers, search papers, related work, citation analysis, recent advances, read this paper, baseline with code. Do NOT use for: survey reports (research-survey), idea generation (research-ideation), Related Work sections (paper-writing).
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
6,745 tokensWhen the skill is activated
Reference documents
11,428 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Setup
- Five Red Lines (always)
- Router
- POINT branch (known paper)
- LIST / ITERATIVE branch — 6 steps
- Tool Cheat Sheet
- Rate limits
- References
- Hand off to
Scripts executed separately
scripts/arxiv_monitor.pyscripts/author_search.pyscripts/citation_traverse.pyscripts/dataset_search.pyscripts/deepxiv_client.pyscripts/download_paper.pyscripts/fetch_paper.pyscripts/find_code.pyscripts/github_search.pyscripts/library_search.pyscripts/literature_report.pyscripts/match_paper_by_title.pyscripts/recommend.pyscripts/saturation.pyscripts/scholar_search.pyscripts/snippet_search.pyscripts/sota.pyscripts/trending.pyscripts/utils.py
Files
SKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesSKILL.mdcore25,709 bytesassets/paper-summary-template.mddoc992 bytesreferences/api-reference.mddoc4,400 bytesreferences/arxiv-categories.mddoc1,911 bytesreferences/disambiguation.mddoc2,178 bytesreferences/env-vars.mddoc3,319 bytesreferences/iterative-collection.mddoc11,430 bytesreferences/output-formats.mddoc2,608 bytesreferences/reading-strategy.mddoc4,135 bytesreferences/search-principles.mddoc13,920 bytesrequirements.txtdoc26 bytesscripts/arxiv_monitor.pyruns5,242 bytesscripts/author_search.pyruns3,987 bytesscripts/citation_traverse.pyruns20,543 bytesscripts/dataset_search.pyruns3,138 bytesscripts/deepxiv_client.pyruns5,028 bytesscripts/download_paper.pyruns9,081 bytesscripts/fetch_paper.pyruns11,256 bytesscripts/find_code.pyruns5,620 bytesscripts/github_search.pyruns2,963 bytesscripts/library_search.pyruns4,867 bytesscripts/literature_report.pyruns16,549 bytesscripts/match_paper_by_title.pyruns11,001 bytesscripts/recommend.pyruns5,521 bytesscripts/saturation.pyruns14,615 bytesscripts/scholar_search.pyruns8,015 bytesscripts/snippet_search.pyruns6,722 bytesscripts/sota.pyruns5,326 bytesscripts/trending.pyruns4,024 bytesscripts/utils.pyruns43,337 bytes
Read SKILL.md
# Paper Navigator
Find and read academic papers. Route by **intent**, judge by **relevance**.
```
User
│
▼
┌── Router ──┐
│ │
▼ ▼
POINT LIST/ITERATIVE
(1 paper) (Probe + up to 3 paper rounds:
R2 breadth / R3 deepen / R4 close)View the complete source on GitHubpaper-planning
When your agent uses it
Guides pre-writing planning for academic papers with 4 structured steps: story design (task-challenge-insight-contribution-advantage), experiment planning (comparisons + ablations), figure design (pipeline + teaser), and 4-week timeline management. Includes counterintuitive planning tactics (write a mock rejection letter to identify weaknesses before writing, narrow before broad claims, design ablations first). Use when: user wants to plan a paper before writing, design story/contributions, plan experiments, create figure sketches, set a writing timeline, or write a pre-emptive rejection letter for planning purposes. Do NOT use for actual writing (use paper-writing), running experiments (use experiment-pipeline), self-reviewing a finished draft (use paper-review), or finding research problems (use research-ideation).
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
2,055 tokensWhen the skill is activated
Reference documents
5,688 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Planning Overview
- Counterintuitive Planning First
- Step 1: Story Design
- Step 2: Experiment Planning
- Step 3: Figure Design
- Step 4: Timeline
- Handoff to Writing
- Reference Navigation
- Handoff to Presentation
Files
SKILL.mdcore9,834 bytesassets/experiment-plan-template.mddoc3,409 bytesreferences/counterintuitive-planning.mddoc3,615 bytesreferences/experiment-planning.mddoc5,652 bytesreferences/figure-design.mddoc3,720 bytesreferences/story-design.mddoc4,750 bytesreferences/timeline-4week.mddoc4,534 bytes
Read SKILL.md
# Paper Planning
A structured approach to planning academic papers before writing begins. Covers four key activities: Story design, Experiment planning, Figure design, and Timeline management.
## When to Use This Skill
> If you don't yet have an idea, use the `research-ideation` skill first to find a problem and design a solution.
- User wants to plan a paper before writing
- User asks about structuring a paper's story or contributions
- User needs to plan experiments (comparisons, ablations)
- User wants to design pipeline figures or teaser figures
- User asks about writing timelines or submission schedules
View the complete source on GitHubpaper-rebuttal
When your agent uses it
Guides writing effective rebuttals after receiving peer review feedback. Covers review diagnosis (score-driven color-coding), response strategy (champion identification, common-theme consolidation), tactical writing (18 rules), and counterintuitive rebuttal principles. Use when: user received reviewer scores/comments, needs to write a rebuttal or author response, wants to respond to specific criticism (e.g. 'limited novelty', 'missing baselines'), mentions 'rebuttal', 'reviewer comments', 'author response', or 'respond to reviewers'. Do NOT use for pre-submission self-review (use paper-review instead).
What it loads into context
Frontmatter
179 tokensAlways available to the agent
SKILL.md
2,873 tokensWhen the skill is activated
Reference documents
2,033 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Step 1: Diagnose Reviews
- Step 2: Plan Response Strategy
- Step 3: Write the Rebuttal
- Counterintuitive Rebuttal Principles
- Common Reviewer Concerns
- Handoff from Paper Review
- Reference Navigation
Files
SKILL.mdcore13,553 bytesassets/rebuttal-template.mddoc2,692 bytesreferences/rebuttal-tactics.mddoc6,581 bytes
Read SKILL.md
# Paper Rebuttal
A systematic approach to writing rebuttals after receiving peer review feedback. The goal is not to defend every point — it's to move scores by addressing the concerns that actually drive them.
## When to Use This Skill
- User received reviewer comments and needs to write a rebuttal
- User asks how to respond to specific reviewer criticism
- User wants to analyze reviews strategically before responding
- User mentions "rebuttal", "reviewer comments", "review feedback", "respond to reviewers"
> For pre-submission self-review and catching weaknesses before they become reviewer complaints, use the `paper-review` skill.
## Step 1: Diagnose ReviewsView the complete source on GitHubpaper-review
When your agent uses it
Guides self-review of YOUR OWN academic paper before submission with adversarial stress-testing. Core method: three passes (adversarial deep read; 5-aspect checklist — contribution sufficiency, writing clarity, results quality, testing completeness, method design; mechanical consistency scans and experimental protocol audit of data flow, assumptions, leakage), counterintuitive protocol (reject-first simulation, delete unsupported claims, score trust, promote limitations, attack novelty), reverse-outlining, and figure/table quality checks. Use when: user wants to self-review or self-check their own paper draft before submission, stress-test their claims, prepare for reviewer criticism, or mentions 'self-review', 'check my draft', 'is my paper ready'. Do NOT use for writing a peer review of someone else's paper, and do NOT use after receiving actual reviews (use paper-rebuttal instead). Also runs as a background expert: dispatch it async with a draft path and it reviews end-to-end while you keep working.
What it loads into context
Frontmatter
260 tokensAlways available to the agent
SKILL.md
3,766 tokensWhen the skill is activated
Reference documents
3,074 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Prerequisites
- How to Run the Review: Three Passes
- Reporting Findings: Calibrated Suspicion
- The Perfectionist Approach
- Counterintuitive Review Protocol
- 5-Aspect Self-Review Checklist
- Experimental Protocol Audit
- Critical Reminder: Claims Must Have Support
- Reverse-Outlining Technique
- Figure and Table Quality Checklist
- Conclusion and Limitation Check
- Mechanical Consistency Scans
- Pre-Submission Final Checks
- Handoff to Rebuttal
Scripts executed separately
scripts/five_aspect_review.js
Files
EXPERT.mddoc3,751 bytesSKILL.mdcore18,007 bytesSKILL.mdcore18,007 bytesSKILL.mdcore18,007 bytesSKILL.mdcore18,007 bytesreferences/counterintuitive-review.mddoc2,685 bytesreferences/review-checklist.mddoc7,026 bytesscripts/five_aspect_review.jsruns8,458 bytes
Read SKILL.md
# Paper Review
A systematic approach to self-reviewing academic papers before submission, run as **three passes**: an adversarial deep read, a checklist sweep (5 aspects, reverse-outlining, figure/table quality), and mechanical consistency scans plus an experimental protocol audit. Ends with rebuttal preparation.
## When to Use This Skill
- User wants to review or check a paper draft before submission
- User asks for feedback on paper quality or completeness
- User wants to prepare for potential reviewer criticism
- User mentions "review paper", "check my draft", "self-review"
> If the user has already received reviewer comments and needs to write a rebuttal, use the `paper-rebuttal` skill instead.
## PrerequisitesView the complete source on GitHubpaper-writing
When your agent uses it
Guides writing academic papers section by section using an 11-step workflow with LaTeX templates and counterintuitive writing tactics. Covers Abstract, Introduction, Method, Experiments, Related Work, Conclusion, and Supplementary. Use when: user asks to write or draft a paper section, needs LaTeX templates, wants to improve academic writing quality, optimize novelty framing, or mentions 'write introduction', 'draft method', 'paper writing'. Do NOT use for pre-submission review (use paper-review), experiment execution (use experiment-pipeline), or paper planning/story design (use paper-planning).
What it loads into context
Frontmatter
177 tokensAlways available to the agent
SKILL.md
2,398 tokensWhen the skill is activated
Reference documents
9,912 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Artifact Sources
- The 11-Step Writing Process
- Counterintuitive Writing Rules
- Section Quick Reference
- Core Writing Principles
- Key Insight
- Paper Title Guidelines
- LaTeX Assets
- Handoff to Review
- Section Navigation
Files
../paper-planning/references/figure-design.mddoc3,720 bytesSKILL.mdcore11,271 bytesassets/paper-skeleton.texasset5,122 bytesassets/table-style.texasset3,079 bytesreferences/abstract-templates.mddoc3,444 bytesreferences/counterintuitive-writing.mddoc2,477 bytesreferences/experiments-guide.mddoc8,122 bytesreferences/introduction-templates.mddoc8,745 bytesreferences/method-templates.mddoc6,715 bytesreferences/related-work-guide.mddoc2,935 bytesreferences/supplementary-guide.mddoc2,962 bytesreferences/writing-practice.mddoc2,508 bytesreferences/writing-principles.mddoc4,400 bytes
Read SKILL.md
# Paper Writing
A systematic 11-step workflow for writing academic papers, with section-specific templates and battle-tested writing principles.
## When to Use This Skill
- User asks to write or draft a paper or paper section
- User needs LaTeX templates for Abstract, Introduction, Method, Experiments, etc.
- User wants to improve academic writing quality
- User mentions "paper writing", "write introduction", "draft method section", etc.
## Artifact Sources
If you used upstream EvoSkills, pull these artifacts before writing:View the complete source on GitHubresearch-ideation
When your agent uses it
End-to-end research ideation pipeline: literature grounding → multi-track idea generation (3 personas: innovator/pragmatist/critic) → iterative refinement → ELO tournament ranking → update evo-memory (IDE) → user selects direction → expand into manuscript-quality proposal. Use when: user wants to find a research direction, brainstorm ideas, evaluate idea novelty, design a novel solution, rank/compare research ideas, or generate a research proposal. Do NOT use for finding/searching/reading papers (use paper-navigator), literature survey reports (use research-survey), or planning a paper (use paper-planning).
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
3,515 tokensWhen the skill is activated
Reference documents
11,186 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- When NOT to Use
- Step 0: Load Prior Knowledge from evo-memory
- Step 1: Define a Long-Term Research Goal
- Step 2: Literature Grounding (via paper-navigator)
- Step 3: Generate Ideas
- Step 4: Refine Ideas
- Step 5: ELO Tournament → Present Top-3
- Step 6: Update evo-memory
- Step 7: Expand into Proposal
- Step 8: Validate and Iterate
- Counterintuitive Rules
- Dependency: paper-navigator
- evo-memory Integration
- Handoff
- References & Assets
Files
SKILL.mdcore15,822 bytesassets/direction-summary-template.mddoc2,138 bytesassets/idea-candidate-template.mddoc1,281 bytesassets/paper-summary-template.mddoc895 bytesassets/proposal-template.mddoc5,910 bytesassets/ranking-scorecard-template.mddoc975 bytesreferences/elo-ranking-guide.mddoc7,566 bytesreferences/literature-tree.mddoc5,196 bytesreferences/paper-reading.mddoc4,531 bytesreferences/problem-selection.mddoc6,196 bytesreferences/proposal-extension.mddoc9,223 bytesreferences/solution-design.mddoc7,412 bytes
Read SKILL.md
# Research Ideation
From research goal to ranked ideas and a detailed proposal.
```
Step 0: Load evo-memory (M_I)
↓
Step 1: Define Goal
↓
Step 2: Literature Grounding (MUST use paper-navigator scripts)
↓
Step 3: Generate Ideas (3 directions × 3 personas)
↓
Step 4: Refine Ideas (3 tracks × N iterations)View the complete source on GitHubresearch-survey
When your agent uses it
Generates structured literature survey reports from collected papers using a multi-stage pipeline: outline generation (query-type adaptive) → draft survey → section-by-section expansion → summary section refinement → final assembly. Produces survey-grade output with taxonomy-based method analysis, LaTeX formalizations, comparative tables, and dense citations. Use when: user wants a literature review, research survey, field overview, or systematic synthesis of multiple papers. Do NOT use for finding/searching papers (use paper-navigator), generating research ideas (use research-ideation), or writing a paper's Related Work section (use paper-writing).
What it loads into context
Frontmatter
177 tokensAlways available to the agent
SKILL.md
2,144 tokensWhen the skill is activated
Reference documents
3,183 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- When NOT to Use
- Dependency: paper-navigator
- Stage 1: Generate Outline
- Stage 2: Draft Survey
- Stage 3: Expand Sections
- Stage 4: Generate Section Summaries
- Stage 5: Refine Summary Sections
- Stage 6: Assemble Final Survey
- Core Quality Principles
- Reference Materials
- Handoff
Files
SKILL.mdcore10,249 bytesassets/survey-template.mddoc7,382 bytesreferences/section-quality-checklist.mddoc2,229 bytesreferences/survey-methodology.mddoc6,005 bytes
Read SKILL.md
# Research Survey
Generates high-quality, survey-grade literature reviews from papers collected by `paper-navigator`.
```
paper-navigator (collect 30-120 papers)
↓
Stage 1: Generate Outline (query-type adaptive structure)
↓
Stage 2: Draft Survey (outline + top-30 papers)
↓
Stage 3: Expand Sections (draft + all papers, section-by-section)
↓
Stage 4: Generate Section SummariesView the complete source on GitHubSkills included
- academic-slides
- evo-memory
- evomath-tao
- experiment-craft
- experiment-iterative-coder
- experiment-pipeline
- nano-banana
- paper-figures
- paper-graph
- paper-navigator
- paper-planning
- paper-rebuttal
- paper-review
- paper-writing
- research-ideation
- research-survey
Repository facts
- License
- Apache-2.0
- Created
- Jan 31, 2026
- Last pushed
- Oct 27, 2026
- Forks
- 51
- Open issues
- 1
- Project activity
- Active
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 4, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 48 notes
skills/evomath-tao/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/evomath-tao/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/evomath-tao/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/experiment-iterative-coder/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/experiment-iterative-coder/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/nano-banana/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-figures/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-figures/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-graph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-graph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-graph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-graph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/paper-graph/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- 178 network referencesin 32 files: paper-navigator/scripts/utils.py:38 paper-graph/scripts/web_api.py:105 paper-navigator/scripts/fetch_paper.py:49 and 29 more
- No hidden or obfuscated text
- No instruction overrides
- 37 executable scriptsin 37 files: evomath-tao/scripts/evomath_workspace.py:1 nano-banana/scripts/atlas_image.py:1 nano-banana/scripts/edit_slide.py:1 and 34 more
- 100 references to secretsin 21 files: nano-banana/scripts/generate_ppt.py:92 paper-navigator/scripts/utils.py:107 nano-banana/scripts/atlas_image.py:61 and 18 more
- No requests to vote in Hot
Show all 315 signals by file
skills/evomath-tao/scripts/evomath_workspace.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/nano-banana/SKILL.md
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Tell the user: > Review server is running at http://localhost:8080. Open it in your browser to review each slide. Write feedback in the text box below any slide that needs changes, then click "Save Feedback". Tell me when you're done. - Network accessline 246
> Review server is running at http://localhost:8080. Open it in your browser to review each slide. Write feedback in the text box below any slide that needs changes, then click "Save Feedback". Tell me when you're done.
skills/nano-banana/scripts/atlas_image.py
- Executable scriptline 1
Executable file - Network accessline 16
API_BASE_URL = "https://api.atlascloud.ai" - Network accessline 53
def _urlopen( request: urllib.request.Request, timeout: int = 60, redirects: str = "none" - Network accessline 54
request: urllib.request.Request, timeout: int = 60, redirects: str = "none" - Access to secretsline 61
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request = urllib.request.Request(url, data=body, headers=headers, method=method) - Access to secretsline 103
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temporary = Path(handle.name) try: download = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Network accessline 177
try: download = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) with _urlopen(download, timeout=60, redirects="https") as response: - Network accessline 178
download = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - Access to secretsline 195
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skills/nano-banana/scripts/edit_slide.py
- Executable scriptline 1
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skills/nano-banana/scripts/generate_ppt.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 92
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skills/nano-banana/scripts/package_pptx.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/nano-banana/scripts/serve_viewer.py
skills/nano-banana/templates/viewer.html
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async function loadFeedback() { try { const res = await fetch('/api/feedback'); - Network accessline 202
try { const res = await fetch('/api/feedback'); if (res.ok) return await res.json(); - Network accessline 203
const res = await fetch('/api/feedback'); - Network accessline 271
try { const res = await fetch('/save-feedback', { - Network accessline 272
const res = await fetch('/save-feedback', {
skills/paper-figures/scripts/validate_figure.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/paper-graph/SKILL.md
- Network accessline 39
- A **research query**: a topic, a seed paper title/citation, or a hybrid. Free-form text. - **Output path** (required): path for the final Markdown report. If not given, ask before running. - *(Optional)* number of papers to fetch (`--n` f - Network accessline 40
- **Output path** (required): path for the final Markdown report. If not given, ask before running. - *(Optional)* number of papers to fetch (`--n` flag on `fetch_papers`). Default **10**. - *(Optional)* Mermaid theme `light` or `dark` (`-- - Network accessline 41
- *(Optional)* number of papers to fetch (`--n` flag on `fetch_papers`). Default **10**. - Access to secretsline 57
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skills/paper-graph/scripts/audit.py
- Executable scriptline 1
Executable file
skills/paper-graph/scripts/cli.py
- Executable scriptline 1
Executable file - Network accessline 113
async def _run() -> list[dict[str, Any]]: async with httpx.AsyncClient() as client: - Network accessline 114
async with httpx.AsyncClient() as client: - Access to secretsline 117
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async def _run() -> list[dict[str, Any]]: async with httpx.AsyncClient() as client: - Network accessline 171
async with httpx.AsyncClient() as client: - Network accessline 215
async def _run() -> None: async with httpx.AsyncClient() as client: - Network accessline 216
async with httpx.AsyncClient() as client: - Access to secretsline 772
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) p.add_argument( "--n", type=int, default=10, help="Number of papers to fetch (default: 10)." - Network accessline 1188
p.add_argument( "--n", type=int, default=10, help="Number of papers to fetch (default: 10)." ) - Network accessline 1189
"--n", type=int, default=10, help="Number of papers to fetch (default: 10)."
skills/paper-graph/scripts/config.py
- Executable scriptline 1
Executable file - Access to secretsline 4
[Credential-related source omitted] - Network accessline 19
S2_SEARCH_URL = "https://api.semanticscholar.org/graph/v1/paper/search" - Network accessline 20
S2_LOOKUP_URL = "https://api.semanticscholar.org/graph/v1/paper/{external_id}" - Network accessline 21
S2_REFS_URL = "https://api.semanticscholar.org/graph/v1/paper/{paper_id}/references" - Network accessline 22
S2_CITES_URL = "https://api.semanticscholar.org/graph/v1/paper/{paper_id}/citations" - Network accessline 36
# Matches: # https://arxiv.org/abs/2202.01855 - Network accessline 37
# https://arxiv.org/abs/2202.01855 - Network accessline 38
# https://arxiv.org/pdf/2202.01855v1.pdf - Access to secretsline 47
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skills/paper-graph/scripts/deepxiv_client.py
- Executable scriptline 1
Executable file - Network accessline 3
Replaces direct calls to the arXiv API (``export.arxiv.org/api/query``), which enforce a 3-second delay and frequently return HTTP 429. DeepXiv (https://github.com/qhjqhj00/deepxiv_sdk) is a token-based, agent-oriented API - Network accessline 4
enforce a 3-second delay and frequently return HTTP 429. DeepXiv (https://github.com/qhjqhj00/deepxiv_sdk) is a token-based, agent-oriented API that returns pre-parsed, structured results. - Network accessline 5
(https://github.com/qhjqhj00/deepxiv_sdk) is a token-based, agent-oriented API - Access to secretsline 30
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted]
skills/paper-graph/scripts/logger.py
- Executable scriptline 1
Executable file
skills/paper-graph/scripts/mermaid.py
- Executable scriptline 1
Executable file - Access to secretsline 28
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 100
[Credential-related source omitted] - Network accessline 297
# - "ESCAPE" (default): decimal entity codes like `#40;` — # the documented Mermaid escape syntax, see # https://mermaid.ai/open-source/syntax/flowchart.html#entity-codes-to-escape-charact - Network accessline 298
# the documented Mermaid escape syntax, see # https://mermaid.ai/open-source/syntax/flowchart.html#entity-codes-to-escape-characters # Decoded by every modern Mermaid renderer; - Network accessline 299
# https://mermaid.ai/open-source/syntax/flowchart.html#entity-codes-to-escape-characters - Access to secretsline 363
[Credential-related source omitted]
skills/paper-graph/scripts/paper_md.py
- Executable scriptline 1
Executable file - Network accessline 30
PRIMARY_URL = "https://arxiv2md.org/api/json?url={arxiv_id}" - Network accessline 31
FALLBACK_URL = "https://markxiv.org/abs/{arxiv_id}" - Access to secretsline 58
[Credential-related source omitted] - Network accessline 181
async def _amain(arxiv_id: str, *, no_cache: bool, full: bool) -> int: async with httpx.AsyncClient() as client: - Network accessline 182
async with httpx.AsyncClient() as client:
skills/paper-graph/scripts/pipeline.py
skills/paper-graph/scripts/prompts.py
- Executable scriptline 1
Executable file
skills/paper-graph/scripts/web_api.py
- Executable scriptline 1
Executable file - Network accessline 105
arxiv = ext.get("ArXiv") or ext.get("arxiv") or ext.get("arXiv") if arxiv: return f"https://arxiv.org/abs/{arxiv}" - Network accessline 106
if arxiv: return f"https://arxiv.org/abs/{arxiv}" doi = ext.get("DOI") or ext.get("doi") - Network accessline 107
return f"https://arxiv.org/abs/{arxiv}" - Network accessline 108
doi = ext.get("DOI") or ext.get("doi") if doi: return f"https://doi.org/{doi}" - Network accessline 109
if doi: return f"https://doi.org/{doi}" pid = item.get("paperId") or "" - Network accessline 110
return f"https://doi.org/{doi}" - Network accessline 111
pid = item.get("paperId") or "" if pid: return f"https://www.semanticscholar.org/paper/{pid}" - Network accessline 112
if pid: return f"https://www.semanticscholar.org/paper/{pid}" return "" - Network accessline 113
return f"https://www.semanticscholar.org/paper/{pid}" - Network accessline 239
pass url = ( f"https://arxiv.org/abs/{arxiv_id}" if arxiv_id else (item.get("src_url") or "") - Network accessline 240
url = ( f"https://arxiv.org/abs/{arxiv_id}" if arxiv_id else (item.get("src_url") or "") ) - Network accessline 241
f"https://arxiv.org/abs/{arxiv_id}" if arxiv_id else (item.get("src_url") or "")
skills/paper-navigator/SKILL.md
- Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 39
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Access to secretsline 41
[Credential-related source omitted] - Access to secretsline 174
[Credential-related source omitted] - Network accessline 238
**Round caps:** LIST and ITERATIVE up to 3 paper rounds (R2/R3/R4). POINT is a single fetch (no multi-round). If still not saturated at the cap, go to Step 6 and report which criteria / angle tags were not covered. - Access to secretsline 351
[Credential-related source omitted]
skills/paper-navigator/references/api-reference.md
- Network accessline 7
**Base URL:** `https://api.semanticscholar.org/graph/v1` - Access to secretsline 9
[Credential-related source omitted] - Network accessline 20
| ACL | `ACL:N18-3011` | Prefix with `ACL:` | | PubMed | `PMID:19872477` | Prefix with `PMID:` | | URL | `https://arxiv.org/abs/1706.03762` | Full URL also accepted | - Network accessline 22
| URL | `https://arxiv.org/abs/1706.03762` | Full URL also accepted | - Network accessline 37
**Base URL:** `https://api.semanticscholar.org/recommendations/v1` - Network accessline 55
**Base URL:** `http://export.arxiv.org/api/query` - Network accessline 81
Returns Atom XML. Namespace: `{http://www.w3.org/2005/Atom}` - Network accessline 87
**URL pattern:** `https://r.jina.ai/{target_url}` - Access to secretsline 89
[Credential-related source omitted] - Network accessline 101
**Base URL:** `https://huggingface.co/api` - Network accessline 123
GET https://huggingface.co/papers/{arxiv_id}.md - Network accessline 134
**Base URL:** `https://api.github.com`
skills/paper-navigator/references/disambiguation.md
- Network accessline 39
├── Resolution: "Engram" is a memory module from DeepSeek AI │ ├── Paper: "Conditional Memory via Scalable Lookup" (ArXiv:2601.07372) │ └── GitHub: https://github.com/deepseek-ai/Engram - Network accessline 40
│ ├── Paper: "Conditional Memory via Scalable Lookup" (ArXiv:2601.07372) │ └── GitHub: https://github.com/deepseek-ai/Engram └── Next: searching forward citations + related memory modules - Network accessline 41
│ └── GitHub: https://github.com/deepseek-ai/Engram
skills/paper-navigator/references/env-vars.md
- Network accessline 7
| Variable | Required for | Notes | |----------|-------------|-------| | `S2_API_KEY` | All S2 scripts (recommended) | [Request here](https://www.semanticscholar.org/product/api#api-key). Without it: `scholar_search` falls back to arXiv (vi - Network accessline 8
|----------|-------------|-------| | `S2_API_KEY` | All S2 scripts (recommended) | [Request here](https://www.semanticscholar.org/product/api#api-key). Without it: `scholar_search` falls back to arXiv (via DeepXiv); `citation_traverse` / `r - Network accessline 9
[Credential-related source omitted] - Access to secretsline 9
[Credential-related source omitted] - Network accessline 10
[Credential-related source omitted] - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 11
[Credential-related source omitted]
skills/paper-navigator/references/iterative-collection.md
skills/paper-navigator/references/search-principles.md
skills/paper-navigator/scripts/arxiv_monitor.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 4
Uses the DeepXiv SDK (https://github.com/qhjqhj00/deepxiv_sdk) to fetch recent - Network accessline 35
categories = [categories] pdf_url = item.get("src_url") or ( f"https://arxiv.org/pdf/{arxiv_id}" if arxiv_id else "" - Network accessline 36
pdf_url = item.get("src_url") or ( f"https://arxiv.org/pdf/{arxiv_id}" if arxiv_id else "" ) - Network accessline 37
f"https://arxiv.org/pdf/{arxiv_id}" if arxiv_id else ""
skills/paper-navigator/scripts/author_search.py
skills/paper-navigator/scripts/citation_traverse.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 137
try: with httpx.Client(timeout=10) as client: - Network accessline 138
with httpx.Client(timeout=10) as client: - Network accessline 331
limit, ) with httpx.Client() as c: - Network accessline 332
) with httpx.Client() as c: return _paged_citation_fetch( - Network accessline 333
with httpx.Client() as c: - Network accessline 383
limit, ) with httpx.Client() as c: - Network accessline 384
) with httpx.Client() as c: return _paged_citation_fetch( - Network accessline 385
with httpx.Client() as c: - Network accessline 403
with httpx.Client() as client: - Network accessline 467
enriched_map: dict[str, dict] = {} with httpx.Client() as client: - Network accessline 468
with httpx.Client() as client:
skills/paper-navigator/scripts/dataset_search.py
skills/paper-navigator/scripts/deepxiv_client.py
- Executable scriptline 1
Executable file - Network accessline 3
Replaces direct calls to the arXiv API (``export.arxiv.org/api/query``), which enforce a 3-second delay and frequently return HTTP 429. DeepXiv (https://github.com/qhjqhj00/deepxiv_sdk) is a token-based, agent-oriented API - Network accessline 4
enforce a 3-second delay and frequently return HTTP 429. DeepXiv (https://github.com/qhjqhj00/deepxiv_sdk) is a token-based, agent-oriented API that returns pre-parsed, structured results. - Network accessline 5
(https://github.com/qhjqhj00/deepxiv_sdk) is a token-based, agent-oriented API - Access to secretsline 30
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted]
skills/paper-navigator/scripts/download_paper.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 29
[Credential-related source omitted] - Network accessline 42
"tldr,isOpenAccess,openAccessPdf,abstract,venue" ) with httpx.Client() as client: - Network accessline 43
) with httpx.Client() as client: meta = request_with_retry( - Network accessline 44
with httpx.Client() as client: - Network accessline 60
if arxiv_id_raw: arxiv_id = _strip_arxiv_version(arxiv_id_raw) return f"https://arxiv.org/pdf/{arxiv_id}.pdf", arxiv_id - Network accessline 62
return f"https://arxiv.org/pdf/{arxiv_id}.pdf", arxiv_id - Network accessline 153
try: with httpx.Client(follow_redirects=True, timeout=60) as c: - Network accessline 154
with httpx.Client(follow_redirects=True, timeout=60) as c:
skills/paper-navigator/scripts/fetch_paper.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 42
[Credential-related source omitted] - Network accessline 49
UNPAYWALL_API = "https://api.unpaywall.org/v2" - Access to secretsline 57
[Credential-related source omitted] - Network accessline 60
url = f"{UNPAYWALL_API}/{doi}?email={email}" try: with httpx.Client() as client: - Network accessline 61
try: with httpx.Client() as client: resp = client.get(url, timeout=10, follow_redirects=True) - Network accessline 62
with httpx.Client() as client: - Network accessline 88
with httpx.Client() as client: - Network accessline 101
elif (meta.get("externalIds") or {}).get("ArXiv"): arxiv_id = meta["externalIds"]["ArXiv"] url = f"https://arxiv.org/abs/{arxiv_id}" - Network accessline 103
url = f"https://arxiv.org/abs/{arxiv_id}" - Network accessline 111
url = f"https://doi.org/{doi}" - Network accessline 138
with httpx.Client() as client: - Network accessline 209
) parser.add_argument("--paper-id", "-p", help="Paper ID (S2, ArXiv:, DOI:, or URL)") parser.add_argument("--url", "-u", help="Direct URL to fetch (skip S2 lookup)") - Network accessline 210
parser.add_argument("--paper-id", "-p", help="Paper ID (S2, ArXiv:, DOI:, or URL)") parser.add_argument("--url", "-u", help="Direct URL to fetch (skip S2 lookup)") parser.add_argument( - Network accessline 211
parser.add_argument("--url", "-u", help="Direct URL to fetch (skip S2 lookup)")
skills/paper-navigator/scripts/find_code.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 30
arxiv_id = arxiv_id.strip().replace("ArXiv:", "").replace("arxiv:", "") for prefix in [ "https://arxiv.org/abs/", - Network accessline 31
for prefix in [ "https://arxiv.org/abs/", "http://arxiv.org/abs/", - Network accessline 32
"https://arxiv.org/abs/", - Network accessline 33
"http://arxiv.org/abs/", - Network accessline 34
"https://arxiv.org/pdf/", - Network accessline 35
"http://arxiv.org/pdf/", - Network accessline 104
repos = [] with httpx.Client() as client: - Network accessline 105
with httpx.Client() as client:
skills/paper-navigator/scripts/github_search.py
skills/paper-navigator/scripts/library_search.py
skills/paper-navigator/scripts/literature_report.py
skills/paper-navigator/scripts/match_paper_by_title.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 215
def _run_single(args) -> int: with httpx.Client() as client: - Network accessline 216
with httpx.Client() as client: - Network accessline 256
resolved: list[dict] = [] matched = fallback_used = missed = 0 with httpx.Client() as client: - Network accessline 257
matched = fallback_used = missed = 0 with httpx.Client() as client: for title, original in pairs: - Network accessline 258
with httpx.Client() as client:
skills/paper-navigator/scripts/recommend.py
skills/paper-navigator/scripts/saturation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/paper-navigator/scripts/scholar_search.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 36
DeepXiv (https://github.com/qhjqhj00/deepxiv_sdk) replaces the rate-limited - Network accessline 60
pdf_url = item.get("src_url") or ( f"https://arxiv.org/pdf/{arxiv_id}" if arxiv_id else "" - Network accessline 61
f"https://arxiv.org/pdf/{arxiv_id}" if arxiv_id else "" - Network accessline 111
with httpx.Client() as client: - Network accessline 126
with httpx.Client() as client:
skills/paper-navigator/scripts/snippet_search.py
skills/paper-navigator/scripts/sota.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 4
Finds top models by task, sorted by downloads or likes. For detailed benchmark scores, see: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard - Network accessline 5
For detailed benchmark scores, see: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard """ - Network accessline 6
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard - Network accessline 58
with httpx.Client() as client: - Network accessline 88
url = f"https://huggingface.co/{model_id}" - Network accessline 101
description="Query top models by task via HuggingFace", epilog="For detailed SOTA benchmarks, visit: " "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard", - Network accessline 102
epilog="For detailed SOTA benchmarks, visit: " "https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard", ) - Network accessline 103
"https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard", - Network accessline 169
print("---") print("*For detailed benchmark scores, visit:*") print("*https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard*") - Network accessline 171
print("*https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard*")
skills/paper-navigator/scripts/trending.py
skills/paper-navigator/scripts/utils.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 38
S2_BASE = "https://api.semanticscholar.org/graph/v1" - Network accessline 39
S2_RECOMMEND_BASE = "https://api.semanticscholar.org/recommendations/v1" - Network accessline 40
HF_API = "https://huggingface.co/api" - Network accessline 41
GITHUB_API = "https://api.github.com/search/repositories" - Network accessline 42
JINA_PREFIX = "https://r.jina.ai/" - Network accessline 43
MARKXIV_PREFIX = "https://markxiv.com/" - Network accessline 44
COHERE_API = "https://api.cohere.com/v2" - Network accessline 45
OPENROUTER_API = "https://openrouter.ai/api/v1" - Network accessline 69
ARXIV_API = "https://export.arxiv.org/api/query" - Network accessline 70
OPENALEX_BASE = "https://api.openalex.org" - Network accessline 79
if _is_s2_url(url): return "S2" if url.startswith(ARXIV_API) or url.startswith("http://export.arxiv.org/api/query"): - Network accessline 80
return "S2" if url.startswith(ARXIV_API) or url.startswith("http://export.arxiv.org/api/query"): return "ARXIV" - Network accessline 81
if url.startswith(ARXIV_API) or url.startswith("http://export.arxiv.org/api/query"): - Access to secretsline 107
[Credential-related source omitted] - Access to secretsline 142
[Credential-related source omitted] - Access to secretsline 168
[Credential-related source omitted] - Access to secretsline 169
[Credential-related source omitted] - Access to secretsline 224
[Credential-related source omitted] - Access to secretsline 233
[Credential-related source omitted] - Access to secretsline 245
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Network accessline 398
raw = raw.strip() for prefix in [ "https://arxiv.org/abs/", - Network accessline 399
for prefix in [ "https://arxiv.org/abs/", "http://arxiv.org/abs/", - Network accessline 400
"https://arxiv.org/abs/", - Network accessline 401
"http://arxiv.org/abs/", - Network accessline 402
"https://arxiv.org/pdf/", - Network accessline 403
"http://arxiv.org/pdf/", - Network accessline 547
with httpx.Client() as client: - Network accessline 611
with httpx.Client() as client: - Network accessline 658
async with sem: async with httpx.AsyncClient() as client: - Network accessline 659
async with httpx.AsyncClient() as client: - Access to secretsline 793
[Credential-related source omitted] - Access to secretsline 795
[Credential-related source omitted] - Access to secretsline 804
[Credential-related source omitted] - Access to secretsline 806
[Credential-related source omitted] - Network accessline 824
with httpx.Client() as client: - Network accessline 851
with httpx.Client() as client: - Access to secretsline 880
[Credential-related source omitted] - Access to secretsline 882
[Credential-related source omitted] - Access to secretsline 886
[Credential-related source omitted] - Network accessline 1008
results_by_id: dict[str, dict] = {} with httpx.Client() as client: - Network accessline 1009
with httpx.Client() as client:
skills/paper-review/scripts/five_aspect_review.js
- Executable scriptline 1
Executable file
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Declared and executed: 26 facts
- Contacts api.atlascloud.ai, which SKILL.md, the README and the plugin manifest do not mentionnano-banana/scripts/atlas_image.py:16High confidence
- Contacts api.semanticscholar.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-graph/scripts/config.py:19High confidence
- Contacts arxiv2md.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-graph/scripts/paper_md.py:30High confidence
- Contacts markxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-graph/scripts/paper_md.py:31High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-graph/scripts/web_api.py:107High confidence
- Contacts doi.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-graph/scripts/web_api.py:110High confidence
- Contacts www.semanticscholar.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-graph/scripts/web_api.py:113High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/arxiv_monitor.py:37High confidence
- Contacts huggingface.co, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/dataset_search.py:47High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/download_paper.py:62High confidence
- Contacts api.unpaywall.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/fetch_paper.py:49High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/fetch_paper.py:103High confidence
- Contacts doi.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/fetch_paper.py:111High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/find_code.py:32High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/scholar_search.py:61High confidence
- Contacts huggingface.co, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/sota.py:88High confidence
- Contacts api.semanticscholar.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:38High confidence
- Contacts huggingface.co, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:40High confidence
- Contacts api.github.com, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:41High confidence
- Contacts r.jina.ai, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:42High confidence
- Contacts markxiv.com, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:43High confidence
- Contacts api.cohere.com, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:44High confidence
- Contacts openrouter.ai, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:45High confidence
- Contacts export.arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:69High confidence
- Contacts api.openalex.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:70High confidence
- Contacts arxiv.org, which SKILL.md, the README and the plugin manifest do not mentionpaper-navigator/scripts/utils.py:400High confidence
Signal confidence: 310 facts
- Executable script in code of a file that runsevomath-tao/scripts/evomath_workspace.py:1High confidence
- Access to secrets in code of the SKILL.md: a variable, placeholder or call, not a stored valuenano-banana/SKILL.md:39Low confidence
- Access to secrets in prose of the SKILL.md: a variable, placeholder or call, not a stored valuenano-banana/SKILL.md:42Low confidence
- Access to secrets in prose of the SKILL.mdnano-banana/SKILL.md:46Low confidence
- Network access in prose of the SKILL.mdnano-banana/SKILL.md:245Low confidence
- Network access in prose of the SKILL.mdnano-banana/SKILL.md:246Low confidence
- Executable script in prose of a file that runsnano-banana/scripts/atlas_image.py:1Low confidence
- Network access in code of a file that runsnano-banana/scripts/atlas_image.py:16High confidence
- Network access in code of a file that runsnano-banana/scripts/atlas_image.py:53High confidence
- Network access in code of a file that runsnano-banana/scripts/atlas_image.py:54High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:61Low confidence
- Access to secrets in code of a file that runsnano-banana/scripts/atlas_image.py:64High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:75Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:77Low confidence
- Access to secrets in code of a file that runsnano-banana/scripts/atlas_image.py:82High confidence
- Access to secrets in code of a file that runsnano-banana/scripts/atlas_image.py:89High confidence
- Network access in code of a file that runsnano-banana/scripts/atlas_image.py:95High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:103Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:110Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:114Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:120Low confidence
- Network access in code of a file that runsnano-banana/scripts/atlas_image.py:176High confidence
- Network access in code of a file that runsnano-banana/scripts/atlas_image.py:177High confidence
- Network access in code of a file that runsnano-banana/scripts/atlas_image.py:178High confidence
- Access to secrets in code of a file that runsnano-banana/scripts/atlas_image.py:195High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/atlas_image.py:199Low confidence
- Executable script in code of a file that runsnano-banana/scripts/edit_slide.py:1High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/edit_slide.py:72Low confidence
- Access to secrets in prose of a file that runsnano-banana/scripts/edit_slide.py:73Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/edit_slide.py:82Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/edit_slide.py:86Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/edit_slide.py:89Low confidence
- Access to secrets in code of a file that runsnano-banana/scripts/edit_slide.py:102High confidence
- Access to secrets in prose of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/edit_slide.py:113Low confidence
- Access to secrets in code of a file that runsnano-banana/scripts/edit_slide.py:129High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/edit_slide.py:137Low confidence
- Access to secrets in code of a file that runsnano-banana/scripts/edit_slide.py:215High confidence
- Executable script in code of a file that runsnano-banana/scripts/generate_ppt.py:1High confidence
- Access to secrets in prose of a file that runsnano-banana/scripts/generate_ppt.py:92Low confidence
- Access to secrets in prose of a file that runsnano-banana/scripts/generate_ppt.py:96Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:268Low confidence
- Access to secrets in prose of a file that runsnano-banana/scripts/generate_ppt.py:272Low confidence
- Access to secrets in prose of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:275Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:291Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:297Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:298Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:301Low confidence
- Access to secrets in code of a file that runsnano-banana/scripts/generate_ppt.py:310High confidence
- Access to secrets in prose of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:323Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valuenano-banana/scripts/generate_ppt.py:336Low confidence
and 260 more
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/academic-slides/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/academic-slides/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/academic-slides/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/academic-slides/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/academic-slides/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/academic-slides/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.