initiate-new-disorder-creation

name: initiate-new-disorder-creation description: > Skill for initiating new disorder YAML files in the dismech knowledge base. Use this skill when the user asks to create a new disorder entry. Also useful for enhancing existing entries.

Initiate New Disorder Creation Skill

Overview

Guide the creation of new disorder YAML files in the dismech knowledge base. This skill emphasizes a research-first approach to ensure scientific accuracy and prevent AI hallucinations by requiring deep research queries before file creation.

When to Use

• User asks to create a new disorder/disease entry
• User names a disorder that doesn't exist in kb/disorders/

This skill can also be consulted for ongoing curation of existing disorders.

Workflow

Step 1: Select Disorder Name and Run Duplicate Preflight

Choose the clinically preferred name for the disorder, use title case (e.g. Foo Bar Syndrome). For file names, spaces will. be replaced by underscores, and characters such as apostrophes removed.

Before creating a new disorder file, check all three duplicate surfaces:

1. Most recent knowledgebase: fetch origin/main and search the current upstream disorder YAMLs by MONDO ID, preferred label, and important synonyms.
2. All pull requests: search open, closed, and merged PRs for the same ID and names.
3. All issues: search open and closed issues for the same ID and names.

Use specific identifiers first, then human-readable labels and synonyms:

git fetch origin main

# Knowledgebase on the latest origin/main, not just the local working tree.
git grep -n -i -e "<MONDO_ID>" -e "<preferred disorder name>" origin/main -- kb/disorders || true
git grep -n -i -e "<important synonym>" origin/main -- kb/disorders || true

# PRs and issues across all states.
gh pr list --repo monarch-initiative/dismech --state all \
  --search "\"<MONDO_ID>\" OR \"<preferred disorder name>\"" \
  --json number,title,state,url,headRefName --limit 100
gh issue list --repo monarch-initiative/dismech --state all \
  --search "\"<MONDO_ID>\" OR \"<preferred disorder name>\"" \
  --json number,title,state,url,labels --limit 100

Repeat the PR and issue searches for important synonyms if the first search is empty. If the disorder already exists in the knowledgebase, edit the existing file instead of creating a new one. If an open PR or issue already covers the same disorder, continue there rather than starting duplicate work. If a closed PR or issue appears relevant, inspect it before deciding whether new curation is still needed.

Step 2a: Setup git worktree

The preferred mode of working is to use git worktrees, unless the user has expressed a preference not to do this in advance.

Step 2b: Create initial YAML file

Create an initial yaml file using the underscore form of the disease, e.g.

kb/disorders/Foo_Bar.yaml:

name: Foo Bar
creation_date: "2025-06-12T20:16:27Z"
updated_date: "2025-06-12T20:16:27Z"
category: Complex
disease_term:
  term:
    id: MONDO:nnnnnnn
    label: foo bar  ## mondo name will follow OBO case conventions
parents:
  <yaml list of strings>
has_subtypes:
  <optional yaml list of Subtype objects>
pathophysiology:
  <yaml list of Pathophysiology objects>
phenotypes:
  <yaml list of Phenotype objects>
biochemical:
  <optional yaml list of Biochemical objects>
genetic:
  <optional yaml list of Genetic objects>
environmental:
  <optional yaml list of Environmental objects>
treatments:
  <optional yaml list of Treatment objects>
datasets:

creation_date and updated_date must be ISO 8601/RFC 3339 datetime strings. When editing an existing file, preserve creation_date and bump updated_date.

The objects must follow the LinkML schema in src/dismech/schema.

It can be validated with just validate kb/disorders/Foo_Bar.yaml

This first pass should use textbook knowledge about the disease: you will later refine this.

Step 3: Perform Deep Research (REQUIRED)

Execute at least one deep research query. Always do this via the just command, do not perform your own deep research.

Depending on user preference, use one or more of the following commands

• just research-disorder asta DISORDER_NAME
• just research-disorder perplexity DISORDER_NAME
• just research-disorder falcon DISORDER_NAME
• just research-disorder openai DISORDER_NAME
• just research-disorder cyberian DISORDER_NAME
• just research-disorder openscientist DISORDER_NAME
• just research-disorder claude_code DISORDER_NAME

Use the filesystem-friendly name here.

claude_code needs no separate API key. It wraps the local claude CLI as a subprocess (claude --print --output-format json), reusing the Claude Code credential that is already present — CLAUDE_CODE_OAUTH_TOKEN is exported in both dragon-ai.yml and curation-scanner.yml, so it works in CI with no new secret. The provider auto-enables whenever claude is on PATH (disable it with DISABLE_CLAUDE_CODE_PROVIDER=true). For security it restricts the subprocess to read-only research tools (WebSearch, WebFetch) — no filesystem mutations from the research prompt. It captures run provenance (the model used, cost, turn count, web-search count) in a run_metadata field and forces the report inline rather than deferring to a background workflow artifact. This makes it the natural default when you are already inside a Claude Code agentic session and just want a web-grounded report without standing up extra credentials. Requires deep-research-client >= 0.2.7.

falcon requires EDISON_API_KEY or FUTUREHOUSE_API_KEY to be exported in the environment — both names refer to the same key and are accepted interchangeably by deep-research-client. The provider was originally named "FutureHouse Falcon" and later rebranded as "Edison Scientific"; the falcon provider slug in just research-disorder is unchanged. Use whichever key name your environment/secrets manager provides; if you have FUTUREHOUSE_API_KEY that is sufficient. Edison is a large-scale literature agent that performs deep bibliographic research. falcon runs may take 20 minutes or longer. In addition to the narrative report, Edison runs frequently produce artifacts — structured tables, figures, or supplementary documents — that summarise key findings in machine-readable form. When EDISON_API_KEY is set, artifact retrieval happens automatically at the end of just research-disorder falcon …. Artifacts are written to a sub-directory alongside the report:

research/DISORDER_NAME-deep-research-falcon_artifacts/

The report's YAML frontmatter records the trajectory_id used to retrieve them and lists each artifact under the artifacts key. An ## Artifacts section is inserted into the report body for any image artifacts so they render in Markdown viewers. If artifact retrieval was skipped (e.g. EDISON_API_KEY was not set at the time), you can run it later with:

just fetch-research-artifacts <trajectory_id> research/DISORDER_NAME-deep-research-falcon.md

asta requires ASTA_API_KEY to be exported in the environment. Asta behaves more like a literature search agent than a full narrative deep-research agent: its outputs are primarily lists of relevant papers, usually with summaries, evidence snippets, and relevance scores. The just research-disorder asta ... command automatically uses an Asta-specific template tailored for this output style.

openscientist requires OPENSCIENTIST_API_KEY to be exported in the environment. OpenScientist (https://www.openscientist.io) is an autonomous AI research agent from Berkeley Lab that runs iterative hypothesis-driven research using PubMed search and code execution. It produces markdown reports with PMID citations. To set up:

1. Sign up at https://www.openscientist.io
2. Wait for admin approval (required before jobs can run)
3. Generate an API key (shown once in name:secret format)
4. export OPENSCIENTIST_API_KEY="name:secret"

OpenScientist jobs are asynchronous — the provider submits a job, then polls until completion. Jobs are queued server-side and processed sequentially, so wait times depend on queue depth. The API's /report endpoint returns PDF; the provider automatically extracts the markdown final_report.md from the /artifacts ZIP. The artifacts ZIP also contains provenance data (iteration transcripts, generated plots as PNG/JSON) and agent logs.

Timing varies by provider. As a rule of thumb:

• asta usually completes in seconds
• openai and perplexity usually complete within a few minutes
• falcon may take 20 minutes or longer
• cyberian runtime varies with workflow complexity and can also be long-running
• openscientist typically takes 10–30 minutes depending on queue depth and iteration count
• claude_code typically completes in a few minutes — it runs a single bounded agentic session of web searches/fetches rather than a long iterative pipeline, so it is usually faster than falcon/openscientist but with web-grounded (not exhaustive bibliographic) coverage. A representative run (Sarcoidosis, #4761) took ~4m50s, did 11 web searches over 13 turns, returned 24 citations, and cost ~$2 in Claude Code usage. The report's YAML frontmatter records this provenance under run_metadata (models used, web_search_requests, num_turns, total_cost_usd, session_id).

On completion, this will create a file here:

./research/DISORDER_NAME-deep-research-PROVIDER.md

and a separate citations file here:

./research/DISORDER_NAME-deep-research-PROVIDER.md.citations.md

For Edison (falcon) runs, artifacts (figures, structured tables, etc.) are also saved in:

./research/DISORDER_NAME-deep-research-falcon_artifacts/

and referenced in the report's YAML frontmatter under the artifacts key.

For example:

• research/Urticaria-deep-research-openai.md
• research/Urticaria-deep-research-openai.md.citations.md
• research/Urticaria-deep-research-falcon_artifacts/ (falcon only)

You MUST read this before progressing.

Step 3b: GeneReviews Baseline (REQUIRED when applicable)

GeneReviews (https://www.ncbi.nlm.nih.gov/books/NBK1116/) is the authoritative expert-curated clinical reference for Mendelian disorders. Before curating phenotypes, you MUST check whether a GeneReviews article exists for the disease. If one exists, it is the mandatory phenotype baseline — not just a convenient source.

Scope: This step applies primarily to Mendelian (single-gene) disorders. For complex, multifactorial, infectious, or cancer entries where GeneReviews coverage is unlikely, skip directly to Step 4 — the PubMed search below will confirm either way.

1. Search PubMed for a GeneReviews article

curl -sG "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi" \
  --data-urlencode "db=pubmed" \
  --data-urlencode "retmode=json" \
  --data-urlencode "term=<DISEASE_NAME>[TI] GeneReviews[TI]"

If no results, try a broader search: <DISEASE_NAME> GeneReviews[All Fields]

2. If a PMID is found, fetch and cache it

just fetch-reference PMID:XXXXXXXX

3. Tag it in the top-level references: block

references:
  - reference: PMID:XXXXXXXX
    title: "<GeneReviews article title>"
    tags:
      - GeneReviews

You can also run just tag-references after adding the PMID to inline evidence items — the script detects GeneReviews PMIDs from the cached abstract and writes the top-level tag automatically.

4. Cross-reference Clinical Characteristics against your YAML

• Read the cached abstract at references_cache/PMID_XXXXXXXX.md
• Identify every phenotype, anomaly, and comorbidity listed in the Clinical Characteristics section of the abstract
• Compare against your YAML phenotypes: section
• Any GeneReviews-documented phenotype absent from your YAML must either be added (with an HPO term and evidence item quoting the GeneReviews abstract) or explicitly explained as out of scope

Note: The cached abstract captures only the structured PubMed abstract, which is a condensed summary of the full GeneReviews chapter. The full Clinical Characteristics section in the chapter body often lists additional phenotypes not in the abstract. Cross-reference the deep-research artifact (from Step 3) for comprehensive coverage — treat the abstract as the minimum baseline, not the ceiling.

5. Capture drug-safety warnings

GeneReviews often has an Agents/Circumstances to Avoid section. If the abstract mentions any, add a note in the relevant treatment entry's description: and include a GeneReviews evidence item quoting it exactly.

6. Frequency mapping — prose → FrequencyEnum

GeneReviews uses narrative frequency language. Map to the enum as follows:

When frequency is ambiguous, omit frequency: rather than guessing.

7. No GeneReviews article? Document it

If no GeneReviews article exists for the disease, proceed to Step 4 without this baseline. No action needed — the absence itself is not a problem.

Step 4: Enhance YAML file with evidence for assestions

Use the results of deep research to enhance the yaml file, providing evidence for as many assertions as possible.

Find the pubmed IDs or DOIs for the papers in the deep research and retrieve these:

• just fetch-reference PMID:nnnnnnn
• just fetch-reference DOI:...

The just fetch-reference command can accept multiple identifiers of different types, such as:

• just fetch-reference PMID:nnnnnnn DOI:nn.nnnn

You can also find additional references relevant to individual assertions, on top of what is in the deep research.

Including Images from Deep Research Artifacts

When an Edison (falcon) run produces artifacts, check whether any images in the artifact directory directly support a specific evidence claim you are curating. If so, include the image path in the images slot on the evidence item.

CRITICAL relevance rule: Only include an image if it directly illustrates the specific claim made in that evidence item. Do NOT include images for general background, unrelated figures, or mere "this might be interesting" reasons. Every listed image must be clearly connected to the snippet or explanation it accompanies.

To check available artifacts for a falcon report:

ls research/DISORDER_NAME-deep-research-falcon_artifacts/

The images slot is a list of paths relative to the research/ directory:

evidence:
  - reference: PMID:35533128
    supports: SUPPORT
    evidence_source: HUMAN_CLINICAL
    snippet: "Exactly quoted text from the abstract..."
    explanation: "Why this supports the claim."
    images:
      - Dimethylglycine_Dehydrogenase_Deficiency-deep-research-falcon_artifacts/figure-01.png

Multiple images per evidence item are allowed when each is distinctly relevant:

evidence:
  - reference: PMID:35533128
    supports: SUPPORT
    snippet: "..."
    images:
      - MyDisorder-deep-research-falcon_artifacts/pathway-diagram.png
      - MyDisorder-deep-research-falcon_artifacts/clinical-data-table.png

Do not invent image paths. Only reference files that actually exist in the artifact directory and have been committed to the repository. Non-image artifacts (e.g., .md tables, .json data) should generally not be listed under images; they are already linked in the report's ## Artifacts section.

Finding Additional References

Use PubMed first whenever possible. Use Semantic Scholar as a backup discovery tool if PubMed search is not finding the paper you want.

Search the PubMed API

Use the NCBI E-utilities API to search PubMed directly. A typical workflow is:

1. Search for candidate papers with esearch
2. Inspect metadata with esummary
3. Retrieve the abstract text with efetch if needed
4. Cache the paper locally with just fetch-reference

Example search:

curl -sG "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi" \
  --data-urlencode "db=pubmed" \
  --data-urlencode "retmode=json" \
  --data-urlencode "term=<SEARCH_TERMS>"

Example summary lookup once you have one or more PMIDs:

curl -sG "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi" \
  --data-urlencode "db=pubmed" \
  --data-urlencode "retmode=json" \
  --data-urlencode "id=<PMID1>,<PMID2>"

Example abstract fetch:

curl -sG "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" \
  --data-urlencode "db=pubmed" \
  --data-urlencode "id=<PMID>" \
  --data-urlencode "rettype=abstract" \
  --data-urlencode "retmode=text"

Once you identify the paper you want, cache it with:

just fetch-reference PMID:nnnnnnn

Search the Semantic Scholar API as a backup

If PubMed search is sparse or the deep research output only gives you a title, DOI, or Semantic Scholar paper ID, use the Semantic Scholar API to find the paper and recover identifiers.

Important: the Semantic Scholar search endpoint may return 429 Too Many Requests without an API key, even for simple queries. If you have a Semantic Scholar API key, include it as an x-api-key header. If you do not, use Semantic Scholar as a secondary/manual fallback rather than your primary search method.

Example paper search (often requires an API key):

curl -sG "https://api.semanticscholar.org/graph/v1/paper/search" \
  -H "x-api-key: <SEMANTIC_SCHOLAR_API_KEY>" \
  --data-urlencode "query=<SEARCH_TERMS>" \
  --data-urlencode "limit=10" \
  --data-urlencode "fields=title,year,externalIds,url"

Example lookup by Semantic Scholar paper ID (this should work without an API key):

curl -s "https://api.semanticscholar.org/graph/v1/paper/<S2_PAPER_ID>?fields=title,year,externalIds,url"

Prefer records that expose externalIds such as DOI, PubMed, or PMC.

Get a PMID and PMCID from a DOI or Semantic Scholar ID

If you have a DOI, use the PMC ID Converter API to recover PubMed/PMC IDs when available:

curl -sG "https://pmc.ncbi.nlm.nih.gov/tools/idconv/api/v1/articles/" \
  --data-urlencode "ids=<DOI>" \
  --data-urlencode "format=json" \
  --data-urlencode "tool=dismech" \
  --data-urlencode "email=<YOUR_EMAIL>"

This can return:

• pmid
• pmcid
• the normalized doi

If you start from a Semantic Scholar paper ID:

1. Query the Semantic Scholar paper endpoint and inspect externalIds
2. If PubMed is present, use that PMID directly
3. If PMC is present, keep the PMCID as supporting metadata
4. If only DOI is present, run the DOI through the PMC ID Converter API above

Then cache the article locally with whichever identifier you recovered:

just fetch-reference PMID:nnnnnnn
just fetch-reference DOI:10.xxxx/xxxxx

Use PMID-based references in YAML evidence whenever possible. Keep PMCID as useful supporting metadata, but DisMech evidence validation is centered on PMID abstracts.

Then use this to provide snippets/excerpts and explanations for assertions. For example, for a phenotype assertion:

phenotypes:
- name: <Phenotype Name>
  description: <Description from research>
  evidence:
  - reference: PMID:XXXXXXXX
    supports: <SUPPORT | REFUTE | PARTIAL>
    evidence_source: <HUMAN_CLINICAL | MODEL_ORGANISM | IN_VITRO | COMPUTATIONAL>
    snippet: "<Exact quote from abstract>"
    explanation: "<Why this supports the phenotype>"

IMPORTANT: The evidence_source field classifies the type of evidence in the cited publication (human study, animal model, cell culture, computational simulation), NOT whether the curation was performed by an AI agent. Always classify based on what the paper reports, regardless of who or what is doing the curation.

The same generic evidence list schema is used for most types.

Step 5: Add term objects

Add term objects using ontology term IDs; for example, for a pathophsyiology object, it might look like this:

pathophysiology:
- name: <Mechanism Name>
  description: >
    <Detailed mechanism description from research>
  cell_types:
  - preferred_term: <Cell Type>
    term:
      id: CL:XXXXXXX
      label: <exact CL label>
  biological_processes:
  - preferred_term: <Process Name>
    term:
      id: GO:XXXXXXX
      label: <exact GO label>

Consult the LinkML schema to see what terms are appropriate for any given object type. These will be validated.

You can use OAK commands to find relevant terms.

General term search (use mondo for diseases)

uv run runoak -i sqlite:obo:mondo info "l~<disease name>"

starts-with queries (use hp for phenotypes)

uv run runoak -i sqlite:obo:hp info "l^<phenotype>"

exact:

uv run runoak -i sqlite:obo:cl info CL:nnnnnnn

relationships (up and down):

uv run runoak -i sqlite:obo:go relationships --direction both GO:nnnnnnn

Step 6: Validation

Strict validation check (adherence to schema, term and reference checks):

just validate kb/disorders/<Disease_Name>.yaml

Compliance report (completeness, term and evidence coverage):

just compliance kb/disorders/<Disease_Name>.yaml

Step 7: Review

Use the dismech-pr-review/ to do an initial round of review. Use a subagent for fresh context (note that we haven't made the PR yet, but we want to do our own "red team" before making the actual PR

Step 8: Make a PR

IF THE USER asks, then go ahead and make a PR on behalf of the user

Step 9: Check reviews on the PR

Some time (~5 mins) after making the PR, a review will appear. You should prioritize in order:

1. reviews from a human/curator -- always take precedence
2. reviews from claude -- high quality but sometimes focuses on wrong thing
3. copilot -- useful for targeted line-level edits but in general lower quality

Follow these priorities but use judgment. If something doesn't sit right, ask for clarification on the PR. Be proactive. If the review says "moderate" go ahead and fix it as you are fixing things anyway. Ignore things that seem super-minor but if there is no cost in making a fix and you agree, do it.

Once you have made the changes:

1. Stage ONLY disorder-relevant files — never use git add -A or git add .:

   git add kb/disorders/ references_cache/ research/
   

   This prevents committing unrelated generated files (HTML, schema docs, cache CSVs) that cause merge conflicts.

2. Commit and push:

   git commit --no-verify -m "feat: Add <Disease Name> (<gene>) with deep research and validated evidence"
   git push
   

3. Post a PR comment summarizing what you did:

   • What was created/changed
   • Key PMIDs used
   • Validation results
   • Any issues you found but intentionally did NOT fix (with reasoning)

File Naming Convention

Convert the disease name to a file-safe format:

• Replace spaces with underscores
• Remove special characters
• Use title case

Examples:

• "Type 2 Diabetes" → Type_2_Diabetes.yaml
• "Alzheimer's Disease" → Alzheimers_Disease.yaml
• "COVID-19" → COVID-19.yaml

Minimum Required Fields

A new disorder file MUST include at minimum:

Evidence Requirements

All evidence items MUST:

1. Use real PMIDs from the research query results
2. Have snippets that are exact quotes from abstracts
3. Include explanations linking evidence to claims
4. Set evidence_source based on the publication's evidence type (human clinical, animal model, in vitro, computational), NOT based on whether an AI agent performed the curation

NEVER fabricate PMIDs or paraphrase snippets.

Evidence Source Classification: When adding evidence_source, ask "What kind of study does this paper report?" not "How was this entry curated?" A computational fluid dynamics study gets COMPUTATIONAL, a mouse model study gets MODEL_ORGANISM, a human clinical trial gets HUMAN_CLINICAL - regardless of whether the curation was done by a human or an AI agent.

Validation Errors and Fixes

"Term not found in ontology"

• Re-run OAK lookup with fuzzy search: info "l~<term>"
• Use the exact label from the ontology

"Snippet not found in reference"

• The quoted text must be from the PMID's abstract
• Fetch and verify: just validate-references <file>
• Use --fix-threshold 0.80 to auto-repair minor mismatches

"Required field missing"

• Check the schema for required fields
• Ensure name, category, and at least one pathophysiology entry

Integration with Other Skills

Use all loaded skills, including:

• Use dismech-terms to add additional ontology term bindings
• Use dismech-references to validate/repair evidence items
• Use dismech-compliance to check completeness and identify gaps

Responding to PR Review Comments

When asked to address review comments on an existing PR:

1. Read the full review carefully — understand each issue before making changes
2. Address ALL 🔴 CRITICAL and 🟡 IMPORTANT issues — don't skip any
3. For issues you disagree with, don't silently ignore them. Post a PR comment explaining why:
   • e.g. "The reviewer flagged X as a typo, but this matches the canonical MONDO label (verified with OAK). Filed upstream issue."
4. After pushing fixes, post a PR comment with a table summarizing:
   • Each reviewer issue and how you addressed it
   • Any issues you intentionally did NOT fix, with reasoning
   • Validation results after fixes
5. Use supports: PARTIAL when evidence is indirect — don't overstate evidence strength
6. If evidence doesn't support a claim, find better evidence rather than arguing about evidence_source classification
7. Verify ontology terms with OAK when the reviewer questions them — don't assume

Git discipline for review fixes

# ONLY stage disorder-relevant files
git add kb/disorders/ references_cache/ research/

# NEVER do this — picks up generated files from other disorders
# git add -A
# git add .

git commit --no-verify -m "fix: Address PR review comments"
git push

Anti-Hallucination Checklist

Before finalizing a new disorder file, verify:

• Deep research query was performed (document which tool)
• GeneReviews article searched for this disease
  • If found: PMID fetched, cached, and tagged GeneReviews in top-level references:
  • If found: all Clinical Characteristics phenotypes either captured in YAML or explicitly noted as out of scope
  • If found: any drug-safety warnings (Agents to Avoid) reflected in relevant treatment entries
  • If not found: no action needed
• All PMIDs exist and are for relevant papers
• All snippets are exact quotes from abstracts
• MONDO term exists and label matches exactly
• HPO terms exist and labels match exactly
• CL terms exist and labels match exactly
• GO terms exist and labels match exactly
• MAXO terms (if used) exist and labels match exactly
• just validate passes
• just validate-terms-file passes
• just validate-references passes