Borrowing it
Nothing to install: this file belongs to KalarisLabs/Skill-Doctor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/KalarisLabs/Skill-Doctor/main/.agents/skills/research-survey/SKILL.mdgit clone --depth 1 https://github.com/KalarisLabs/Skill-DoctorWrote 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/kalarislabs/skill-doctor/research-survey)<a href="https://agentmods.dev/skills/kalarislabs/skill-doctor/research-survey"><img src="https://agentmods.dev/badge/skills/kalarislabs/skill-doctor/research-survey/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/kalarislabs/skill-doctor/research-survey"><img src="https://agentmods.dev/badge/skills/kalarislabs/skill-doctor/research-survey.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 | $0.00125 | $0.02144 |
| Opus 5.5 | $0.00050 | $0.00858 |
| Sonnet 5.5 | $0.00025 | $0.00429 |
| Haiku 4.5 | $0.00013 | $0.00214 |
Grade A, and why
research-survey 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 1mo 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 research-survey — 0 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 Summaries
↓
Stage 5: Refine Summary Sections (Abstract/Intro/Conclusion)
↓
Stage 6: Assemble + References
When to Use
- User asks for a "literature review", "survey", "field overview", or "systematic review"
- User has collected papers and wants them synthesized into a structured report
- User wants to understand the full landscape of a research field
When NOT to Use
- Finding papers → use
paper-navigatorfirst, then come here - Generating research ideas → use
research-ideation - Writing a Related Work section for a paper → use
paper-writing
Dependency: paper-navigator
This skill requires papers as input. If the user hasn't provided papers, first invoke paper-navigator (Workflow 1, target 30-120 papers) to collect them.
CRITICAL: All paper discovery MUST use the paper-navigator skill and its scripts (scholar_search, citation_traverse, arxiv_monitor, recommend, etc.). Using WebSearch, WebFetch, or any generic web search tool for finding papers is PROHIBITED. Generic web search cannot access Semantic Scholar, citation graphs, or academic recommendation systems. Only paper-navigator provides the academic search infrastructure needed for survey-quality literature collection.
Stage 1: Generate Outline
This is a two-phase process. Different fields have different survey conventions — a clinical systematic review looks nothing like a CS methods survey. First generate a domain-appropriate template, then create the detailed outline.
Phase 1A: Generate Domain-Specific Survey Template
Before outlining, identify the field and adapt the structure:
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 211 lines · 125 tokens per session scan A 2d547783c85a
research-survey is a skill published in the GitHub repository KalarisLabs/Skill-Doctor (13 stars, last pushed 5d ago), licensed Apache-2.0. It adds 125 tokens to every session and 2,144 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to research-survey, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
clinical-trial-protocol-skill
Generate clinical trial protocols for medical devices or drugs. This skill should be used when users say "Create a clinical trial protocol", "Generate protocol for [device/drug]", "Help me design a clinical study", "Research similar trials for [intervention]", or when developing FDA submission documentation for…
petrinaut
Using the Petrinaut tools and writing net code. Use before the first net change, before writing lambda, kernel, dynamics, visualizer, metric or scenario code, before drafting or creating an experiment, and when the USER asks how something in the Petrinaut UI works.
modelling
Maps the operational account to a Petrinaut SDCPN, and revises, checks and delivers it. Use before any net mutation, net revision, construction-led analysis, experiment proposal or net delivery.
jupyter-live-kernel
Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a…
segment-anything-model
Foundation model for image segmentation with zero-shot transfer. Use when you need to segment any object in images using points, boxes, or masks as prompts, or automatically generate all object masks in an image.
gtars
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.