القائمة
Generate source-backed AI/ML survey paper artifacts with curated bibliographies and Fireworks/Kimi HTML rendering.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/phoroth/AGENTIC --skill survey-generatorيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Development skill from AGENTIC (https://github.com/phoroth/AGENTIC)
Development skill from AGENTIC (https://github.com/phoroth/AGENTIC)
Generate clean, human-sounding, SEO-optimized WordPress blog posts with optional Yoast metadata, JSON-LD schema markup, and image SEO planning. Supports modular batch output.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | survey-generator |
| description | Generate source-backed AI/ML survey paper artifacts with curated bibliographies and Fireworks/Kimi HTML rendering. |
| allowed-tools | Read, Write, Bash, WebFetch, AskUserQuestion |
| category | research |
| risk | safe |
| source | official |
| source_repo | dair-ai/dair-academy-plugins |
| source_type | official |
| date_added | 2026-06-19 |
| author | DAIR.AI |
| license | MIT |
| license_source | https://github.com/dair-ai/dair-academy-plugins/blob/main/README.md#license |
| tags | ["dair-academy","ai","workflow"] |
| tools | ["claude-code","codex-cli","cursor"] |
Use when this workflow matches the user request: Use this skill for its documented workflow.
Source: dair-ai/dair-academy-plugins (MIT).
Generate an academic-style survey paper as a single self-contained HTML file.
Given a topic and a public anchor resource, this skill:
research_bundle.json (title, taxonomy, sections, bibliography of real papers).style_spec.json.The agent using this skill is responsible only for research curation. All prose, figures, and HTML are generated by Kimi K2.6 in one API call.
The user invokes this skill with at minimum:
topic: a concise survey topic, for example "Agentic Engineering" or "Reasoning Models".source_url: a public anchor resource. Any curated list, canonical blog post, arXiv survey, GitHub awesome-list, or index page works. Suggested starting points: DAIR.AI AI Papers of the Week (a continuously updated open-source index of notable AI/ML papers, well suited for broad topics), a GitHub awesome-* repo, an arXiv survey PDF, or a well-maintained papers page.Optional:
bibliography_size: target bibliography size. Default 20 for a quick survey. Use 40 to 50 for a comprehensive survey, 80 to 100 for an exhaustive one. Section length and token budget scale with this.section_count: number of sections, default 6 to 10.If the user has not provided these, use AskUserQuestion to collect them before proceeding.
FIREWORKS_API_KEY exported in the enprojectnment. The build script reads it from os.enprojectn.Follow these steps in order. Do not skip steps.
Fetch and read source_url. If it is a GitHub repo, fetch the README and any relevant or indices. If it is an arXiv survey, use the abstract, figures, and section headings. If it is a blog post, read it in full. Extract the key subtopics and the papers or systems it references by name.
README-*.mdpapers.mdFor broad AI/ML topics, DAIR.AI AI Papers of the Week is a particularly rich anchor: it has weekly issues going back years, each with short summaries of 6 to 10 notable papers, so it is easy to scan across time and filter to the subset that matches your topic.
If a paper-search tool is available to your agent (a Papers-of-the-Week MCP, arXiv search, Semantic Scholar, Google Scholar, an organization's internal index, etc.), use it to expand the candidate pool beyond what the anchor resource cites directly.
Draft a taxonomy rooted at the topic with 4 to 8 branches, each with 2 to 4 children. Branches should cover distinct subareas of the topic, not overlap. Draft 6 to 10 numbered sections that match the taxonomy progression: introduction, foundations, methods, evaluation, open problems. Figure 1's viewport height scales automatically with the total leaf count via the geometry contract in style_spec.json, so deeper taxonomies render cleanly.
Pick real papers sized to bibliography_size. For a comprehensive survey, 40 to 50 entries is the sweet spot; the skill has been tested up to 100 entries with max_tokens=81920 in build_artifact.py. Every entry must have: key, authors, year, title, venue, and a 1 to 2 sentence summary. Do not invent papers. Every section's papers array must reference keys that exist in the bibliography.
research_bundle.jsonWrite research_bundle.json in the skill directory (next to build_artifact.py). Use templates/research_bundle_template.json as the structural scaffold. Required top-level fields: title, authors_placeholder, anchor_source, abstract_hints, taxonomy, paradigms, stack, sections, table, bibliography. See examples/agentic-engineering/research_bundle.json for a complete worked example.
python3 build_artifact.py
Run this from the skill directory. The script reads research_bundle.json and style_spec.json, calls Kimi K2.6 on Fireworks, and writes output/survey_kimi-k2p6_v{N}.html. Each run produces a new versioned file.
To use a different Fireworks model (for example Kimi K2.5 for side-by-side comparison):
FIREWORKS_MODEL=accounts/fireworks/models/kimi-k2p5 python3 build_artifact.py
Output filenames are slugged by model so you can compare versions across models.
Open the HTML file locally. It is a fully self-contained HTML document, so you can also serve it from any static host, embed it in a dashboard, or hand it to any artifact-preview mechanism your agent exposes.
If figures look weak, sharpen style_spec.json (the required_figures and figure_quality_note keys) and rerun. If prose is thin or sections are missing, tighten the section guidance fields in research_bundle.json. Do not edit the Kimi output directly; iterate on inputs.
Common figure failure modes and the style_spec patterns that fix them:
<g transform="translate(OFFSET,0)"> groups with panel-local coordinates (enforced for Figure 2).When adding a new figure or changing an existing one, follow the same pattern: declare an absolute viewport, per-element coordinates or a deterministic formula, and a hard-invariant check clause at the end of the description.
SKILL.md - this file.build_artifact.py - Python script that calls Fireworks.style_spec.json - visual and structural spec (topic-agnostic).templates/research_bundle_template.json - empty template for new topics.examples/agentic-engineering/ - reference 100-paper run (research_bundle.json + survey.html).papers array must reference keys in the bibliography.research_bundle.json or style_spec.json and rerun.style_spec.json.hard_rules_for_generation.research_bundle.json.