Medical Imaging Review
LeonChaoX/qinyan-academic-skills
Write comprehensive literature reviews for medical imaging AI research.
Retrieves ClinGen gene-disease validity assertions for a public gene or disease, and reviews source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple…
$ npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills folklore-variant-evidence --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/folklore-variant-evidence .claude/skills/folklore-variant-evidence && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "folklore-variant-evidence" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/folklore-variant-evidence into .claude/skills/folklore-variant-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "folklore-variant-evidence", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/folklore-variant-evidenceType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills folklore-variant-evidence --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/folklore-variant-evidence .agents/skills/folklore-variant-evidence && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "folklore-variant-evidence" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/folklore-variant-evidence into .agents/skills/folklore-variant-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "folklore-variant-evidence", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills folklore-variant-evidence --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/folklore-variant-evidence .cursor/skills/folklore-variant-evidence && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "folklore-variant-evidence" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/folklore-variant-evidence into .cursor/skills/folklore-variant-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "folklore-variant-evidence", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/K-Dense-AI/scientific-agent-skills.git --path skills/folklore-variant-evidence--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills folklore-variant-evidence --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/folklore-variant-evidence .gemini/skills/folklore-variant-evidence && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "folklore-variant-evidence" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/folklore-variant-evidence into .gemini/skills/folklore-variant-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "folklore-variant-evidence", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install K-Dense-AI/scientific-agent-skills folklore-variant-evidenceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/folklore-variant-evidence .github/skills/folklore-variant-evidence && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "folklore-variant-evidence" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/folklore-variant-evidence into .github/skills/folklore-variant-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "folklore-variant-evidence", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install K-Dense-AI/scientific-agent-skills folklore-variant-evidence --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/folklore-variant-evidence .opencode/skills/folklore-variant-evidence && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "folklore-variant-evidence" agent skill from https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/folklore-variant-evidence into .opencode/skills/folklore-variant-evidence/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "folklore-variant-evidence", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
folklore-variant-evidenceRetrieves ClinGen gene-disease validity assertions for a public gene or disease, and reviews source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple…
Folklore Variant Evidence is an agent skill from K-Dense-AI/scientific-agent-skills. Retrieves ClinGen gene-disease validity assertions for a public gene or disease, and reviews source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple indel through Folklore Clinical Variant Interpretation MCP. Used when a scientific agent must branch deterministically on resolved, ambiguous, not-found, invalid, unsupported, or unavailable variant outcomes; chain a resolved public variant into related literature or publication details; or preserve evidence provenance…
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/mcp-contract.md`). Compatibility notes: Requires network access to api.helena.bio (stateless Streamable HTTP MCP, no credentials) and a host supporting its advertised protocol, or curl for direct…
It sits in Research & Science, covering Clinical and healthcare research. It works with Model Context Protocol. The repository describes itself as: Turn any AI agent into an AI Scientist. The 1 Agent Skills library for science, used by 250,000+ scientists worldwide. 177 ready-to-use validated skills plus 100+ scientific… The licence is MIT.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 92ace75. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Shell commands in SKILL.md call:
curlFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
api.helena.bioAlso links to:
github.comfolklore.helena.bioFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires network access to api.helena.bio (stateless Streamable HTTP MCP, no credentials) and a host supporting its advertised protocol, or curl for direct JSON-RPC POST requests.
From compatibility in the SKILL.md frontmatter.
Folklore Variant Evidence loads about 3.4k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 155 tokens; SKILL.md has 1,442 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from K-Dense-AI/scientific-agent-skills at commit 92ace75, republished under its MIT licence (© K-Dense-AI). 1,442 words, ~3,384 tokens.
.claude/skills/folklore-variant-evidence/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.Use Folklore Clinical Variant Interpretation MCP to retrieve structured public variant evidence, automated variant-level ACMG/AMP decision support, provenance, and source-linked literature for professional review. Keep the workflow limited to public identifiers and preserve every explicit outcome state. Adapter 1.5.0 also provides ClinGen Gene-Disease Validity assertions; source coverage is bounded, not every known association.
Folklore Clinical Variant Interpretation MCP is published by Helena Bioinformatics. Its hosted endpoint is:
https://api.helena.bio/folklore/v1/mcpNo account or API key is required. The public Apache-2.0 adapter and contract are available at https://github.com/helena-bioinformatics/folklore-mcp.
A host without native MCP support can make the same public JSON-RPC call:
curl --silent --show-error --fail-with-body --max-time 60 \
-X POST https://api.helena.bio/folklore/v1/mcp \
-H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-H 'MCP-Protocol-Version: 2026-07-28' \
-H 'Mcp-Method: tools/call' \
-H 'Mcp-Name: search_variant_evidence' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"_meta":{"io.modelcontextprotocol/protocolVersion":"2026-07-28","io.modelcontextprotocol/clientCapabilities":{}},"name":"search_variant_evidence","arguments":{"assembly":"GRCh38","query":"rs80357914"}}}'The routing headers must match the JSON-RPC method and tool name. A successful
HTTP response is not sufficient: inspect JSON-RPC error, then
result.structuredContent.adapter_error, then this tool's
result.structuredContent.result.status. Other tools use different response
shapes; use the contract table.
Inspect the returned outcome before continuing. This example can return
ambiguous with multiple candidates: stop and request an unambiguous public
variant notation instead of selecting a candidate automatically.
Use this skill when the task is one public variant to structured Folklore evidence, explicit resolution-state handling, variant-linked literature, or ClinGen gene-to-disease/disease-to-gene assertions.
database-lookup for broad direct queries across ClinVar, dbSNP, gnomAD,
Ensembl VEP, COSMIC, or multiple databases.genomic-coordinates first when the assembly, coordinate convention,
contig name, or variant representation is uncertain.Folklore Clinical Variant Interpretation MCP complements those skills with one source-linked public evidence contract. It does not replace direct database review or qualified clinical judgment.
Before a variant tool call:
unsupported status exists.Accepted public variant forms include genomic coordinates, genomic/coding/
protein HGVS, SPDI, rsID, or a canonical_key returned by Folklore Clinical
Variant Interpretation MCP.
Connect to the hosted endpoint and call tools/list. Verify the available tools
instead of relying on model memory. The documented public catalog contains:
search_variant_evidencesearch_variant_literatureget_publication_detailssearch_literature_corpusget_gene_disease_associationssearch_disease_genesThe separate seventh tool support_helena is not scientific evidence; use it only when explicitly requested.
If discovery or a tool call fails, preserve the failure as an availability problem. Do not reinterpret it as lack of scientific evidence.
Read the public MCP contract before composing tool calls or interpreting response states.
Use get_gene_disease_associations for one exact gene symbol or HGNC identifier, or search_disease_genes for an exact MONDO identifier or public disease-name substring. Both accept limit (default 20, 1–50) and offset (default 0, 0–1000). See the reference for request examples. This is a separate source lookup and requires no variant input or assembly.
Preserve each returned disease identity, inheritance, evidence assessment, source
URL, date, source.version and source.snapshotSha256. This is a local ClinGen
snapshot, not a guarantee of the latest ClinGen release. Follow
pagination.nextOffset while it is present; preserve pagination-ceiling warnings.
An empty page after the total has been passed does not mean the initial query had
no matches. Do not combine distinct diseases or silently choose among name matches.
Gene-disease validity does not classify a particular variant. A not_found
response means no matching assertion in the available source, not no association.
No patient, phenotype, family, segregation, private case data or sequencing files
may be sent. Qualified professional review remains required.
Call search_variant_evidence with:
assembly: GRCh38
query: <one public variant identifier or notation>Do not add phenotype, disease, patient, family, or treatment context to this call. Preserve the returned contract fields, source links, limitations, and usage boundary.
Read transport/JSON-RPC errors and adapter_error first. For
search_variant_evidence, let envelope = result.structuredContent; only if
envelope.result is non-null, branch on envelope.result.status. An
invalid_arguments adapter error is distinct from scientific invalid_request.
Preserve isError and any typed failure, including resolution_unavailable.
Treat the status as a control-flow value, not prose:
| Status | Required action |
|---|---|
resolved | Reuse the returned canonical_key; review the structured interpretation, provenance, source links, and limitations. |
ambiguous | Show the returned candidates and ask for an explicit public variant selection. Never choose a candidate automatically. |
not_found | Report that no result was found within this service and query scope. Do not claim universal absence. |
invalid_request | Report the validation problem and request a corrected public variant. Do not silently reinterpret the input. |
unsupported | State the relevant service boundary and stop. Do not force the query into a supported form. |
resolution_unavailable | Report a temporary resolution or availability failure. Do not treat it as evidence absence. |
Only a resolved result may proceed automatically into a variant-linked
literature workflow. Its canonical key is envelope.result.identity.canonical_key.
If envelope.result.interpretation.status is unavailable, preserve its typed
error; identity resolution has succeeded, but classification has not. Do not read
or invent a classification for that outcome.
For a resolved result:
canonical_key.After a resolved evidence call, pass the returned canonical_key to
search_variant_literature. Keep assembly as GRCh38. An optional question
may narrow the literature focus, but it must remain a public scientific question
and must not contain patient context.
Distinguish each result's match type:
exact_variant: direct match to the resolved variantvariant_alias: match through a reported aliasgene_association: broader gene-level association, not variant-specific proofLiterature associations do not alter the returned ACMG/AMP classification.
Call get_publication_details only with a PMID returned by the literature tools.
Preserve PubMed URLs, DOI/PMCID fields when present, retraction status, and the
distinction between gene mentions and variant mentions.
Use search_literature_corpus for a public natural-language scientific question
or for discovery by publication identifier, gene, variant, phenotype, HPO, or
OMIM concept. Treat results as source-linked candidates for professional review.
A zero-result response means no result was returned for that bounded query, not
that no relevant publication exists anywhere.
The query is 3–200 characters, with limit 1–25 and sort set to relevance,
newest, or oldest. For another page, reuse the returned opaque next_cursor
with the same query and sort; do not construct an offset. Keep match_types and
article_entities distinct from variant-literature match types. Report
semantic_index_used and semantic_degraded_reason; a returned lexical match
does not prove semantic retrieval worked. Preserve additional retrieval metadata
returned by the live service.
Do not place patient information into a corpus query, even if the query is not variant-specific.
Include:
GRCh38 assembly.canonical_key.This is public, variant-level decision support for qualified professional review. It does not evaluate patient, phenotype, family, segregation, or private case data and is not a diagnosis or treatment recommendation.
Use the public rsID rs80357914 to test ambiguity handling:
Call search_variant_evidence with assembly GRCh38 and query rs80357914. If the
result is ambiguous, list the returned candidates and stop for explicit
selection. Do not select a candidate or call downstream literature tools.The ambiguity branch passes only if an ambiguous response causes the workflow to stop without automatic candidate selection. If the live response changes, record the actual status; a resolved response does not test ambiguity handling.
On 2026-09-30, live discovery reported adapter 1.5.0 and protocol 2026-07-28.
The rsID example returned ambiguous; a separate public HGVS query resolved and
was chained through its returned key to literature and a returned PMID. Corpus
cursor pagination and gene-disease offset pagination were exercised. These are
dated protocol checks, not validation of clinical accuracy. See the reference
for exact response shapes and source/live differences.
io.github.helena-bioinformatics/folklore© K-Dense-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 1 other file (references) in skills/folklore-variant-evidence of K-Dense-AI/scientific-agent-skills.
Open the folder on GitHubat commit 92ace75
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in K-Dense-AI/scientific-agent-skills, which our catalogue first saw on October 7, 2026.
Folklore Variant Evidence next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Folklore Variant Evidence this skillK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Medical Imaging ReviewLeonChaoX/qinyan-academic-skills | 944 | 3 repos | ~1.1k | Automated safety check: Notes | MIT | |
| Hcls Build Agentaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | — | ~885 | Automated safety check: Pass | MIT-0 | |
| Medical Research ToolkitFreedomIntelligence/OpenClaw-Medical-Skills | 3.1k | 1 repos | ~2.4k | Automated safety check: Pass | None | |
| Indication DossierJimLiu/science-skills | 228 | 4 repos | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Clinical Protocol Draftingaws-samples/amazon-bedrock-agents-healthcare-lifesciences | 274 | — | ~1.4k | Automated safety check: Pass | MIT-0 |
LeonChaoX/qinyan-academic-skills
Write comprehensive literature reviews for medical imaging AI research.
aws-samples/amazon-bedrock-agents-healthcare-lifesciences
A skill your agent uses when a developer wants to build a new healthcare or life sciences agent, structure tools and system prompts for an HCLS workflow, or create a Strands agent with…
FreedomIntelligence/OpenClaw-Medical-Skills
Query 14+ biomedical databases for drug repurposing, target discovery, clinical trials, and literature research.
JimLiu/science-skills
Generate a therapeutic indication dossier. An agent skill from JimLiu/science-skills.
aws-samples/amazon-bedrock-agents-healthcare-lifesciences
A skill your agent uses when drafting clinical trial protocol sections (objectives, background, study design) grounded in ICH guidelines (E6, E8, E9) and FDA regulations (21 CFR Part 312).
aws-samples/amazon-bedrock-agents-healthcare-lifesciences
A skill your agent uses when a developer asks how to get started building healthcare or life sciences agents, wants to understand the HCLS Agents Toolkit, or asks what's available in this repository.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
K-Dense-AI/scientific-agent-skills
Plans, runs, and documents analytical method validation, verification, or transfer studies under ICH Q2(R2)/Q14, USP, ICH M10, CLSI EP, or ISO/IEC 17025.
K-Dense-AI/scientific-agent-skills
Runs Cantera constant-volume or constant-pressure ignition simulations and reports temperature-based ignition delay with mechanism provenance and checks.
K-Dense-AI/scientific-agent-skills
Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.
K-Dense-AI/scientific-agent-skills
Plans and audits runs of the HypoGeniC and HypoRefine packages, which propose hypotheses from labeled text datasets, with local checks before any model call.
K-Dense-AI/scientific-agent-skills
Organizes scope, controlled documents, risk files and traceability into draft evidence for human review against ISO 13485, 14971, 17025 and 15189.
Works with
Categories
Retrieves ClinGen gene-disease validity assertions for a public gene or disease, and reviews source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple…. Folklore Variant Evidence is an agent skill from K-Dense-AI/scientific-agent-skills. Retrieves ClinGen gene-disease validity assertions for a public gene or disease, and reviews source-linked public evidence and literature for one supported GRCh38 germline nuclear SNV or simple indel through Folklore Clinical Variant Interpretation MCP.
Folklore Variant Evidence fits situations like: tasks that involve Clinical and healthcare research.
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a claude-code`. Or copy the skill folder (skills/folklore-variant-evidence in K-Dense-AI/scientific-agent-skills) into .claude/skills/folklore-variant-evidence in your project. Claude Code loads it when a task matches its description.
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a codex`. Or copy the skill folder (skills/folklore-variant-evidence in K-Dense-AI/scientific-agent-skills) into .agents/skills/folklore-variant-evidence in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add K-Dense-AI/scientific-agent-skills --skill folklore-variant-evidence -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/folklore-variant-evidence, .gemini/skills/folklore-variant-evidence, .github/skills/folklore-variant-evidence and .opencode/skills/folklore-variant-evidence in your project.
Going by SKILL.md and its folder, Folklore Variant Evidence needs the command-line tools its instructions call (curl). Compatibility (from SKILL.md): Requires network access to api.helena.bio (stateless Streamable HTTP MCP, no credentials) and a host supporting its advertised protocol, or curl for direct JSON-RPC POST requests..
SKILL.md names 3 domains. In commands or code: api.helena.bio; the agent is likely to contact it when it follows the instructions. As links in the text: github.com and folklore.helena.bio. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Folklore Variant Evidence is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.8k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Folklore Variant Evidence: Medical Imaging Review (LeonChaoX/qinyan-academic-skills, 944 stars), Hcls Build Agent (aws-samples/amazon-bedrock-agents-healthcare-lifesciences, 274 stars), Medical Research Toolkit (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Indication Dossier (JimLiu/science-skills, 228 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
K-Dense-AI (a GitHub organization) maintains it in K-Dense-AI/scientific-agent-skills, which has 48,215 GitHub stars. The repository holds 153 skills in this directory. The repository was last updated on October 5, 2026.
Source: K-Dense-AI/scientific-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.