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Gather complete target intelligence by exploring 9 parallel research paths. Supports targets identified by gene symbol, UniProt accession, Ensembl ID, or gene name.
KEY PRINCIPLES:
Report-first approach - Create report file FIRST, then populate progressively
Tool parameter verification - Verify params via get_tool_info before calling unfamiliar tools
Evidence grading - Grade all claims by evidence strength (T1-T4)
Citation requirements - Every fact must have inline source attribution
Mandatory completeness - All sections must exist with data minimums or explicit "No data" notes
Disambiguation first - Resolve all identifiers before research
Negative results documented - "No drugs found" is data; empty sections are failures
Collision-aware literature search - Detect and filter naming collisions
English-first queries - Always use English terms in tool calls, even if the user writes in another language. Translate gene names, disease names, and search terms to English. Only try original-language terms as a fallback if English returns no results. Respond in the user's language
LOOK UP, DON'T GUESS
When asked about a specific protein or gene target, look it up in UniProt/Ensembl/OpenTargets BEFORE reasoning about it. Verify the gene name, function, and disease associations from databases. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.
When to Use This Skill
Apply when users:
Ask about a drug target, protein, or gene
Need target validation or assessment
Request druggability analysis
Want comprehensive target profiling
Ask "what do we know about [target]?"
Need target-disease associations
Request safety profile for a target
When NOT to use: Simple protein lookup, drug-only queries, disease-centric queries, sequence retrieval, structure download β use specialized skills instead.
Target Evaluation Reasoning Framework
Evaluating a drug target requires reasoning across four interconnected questions. Answer all four before forming a recommendation.
1. Is there genetic evidence linking this target to the disease?
Genetic evidence is the strongest predictor of drug success β targets with human genetic support have approximately twice the clinical success rate as those without (Nelson et al. 2015). Ask: Are there GWAS associations connecting this gene to the disease? Do rare loss-of-function or gain-of-function variants cause or protect against the disease? Does the mouse knockout phenotype match the human disease (from OpenTargets mouse models)? OpenTargets assigns genetic evidence scores; a score > 0.7 indicates strong support. ClinVar rare variant evidence and DisGeNET curated gene-disease association scores add complementary layers. A target with no genetic link to the disease of interest carries a fundamental validation risk that cannot be resolved by downstream data.
2. Is the target druggable?
Druggability has two components: structural accessibility and prior chemical matter. Structural accessibility means the target has a binding pocket where a small molecule or biologic can engage β surface-exposed receptors, enzymes with well-defined active sites, and protein-protein interaction interfaces with hot spots are tractable. Intrinsically disordered proteins and transcription factors with flat, featureless binding surfaces are typically harder. Pharos TDL classification provides a tiered assessment: Tclin (approved drug), Tchem (known active compounds), Tbio (biological function known but no drugs), Tdark (poorly characterized). If ChEMBL or BindingDB have compounds with IC50 < 1ΞΌM, the target is chemically tractable. Chemical probes (from OpenTargets chemical probes endpoint) confirm a target can be modulated, which is distinct from drug-like compounds. For GPCRs, check GPCRdb for curated agonists and antagonists.
3. Is the target safe to modulate?
Safety concerns arise from two sources. First, on-target effects: if the target is essential in normal tissues (mouse KO is lethal, or gnomAD pLI is high / LOEUF is low), full inhibition will produce toxicity β the question becomes whether a partial agonist or tissue-targeted delivery can provide a therapeutic window. Second, off-target effects: does the gene have family members that could be inadvertently hit? The OpenTargets safety profile aggregates known toxicity annotations, and DepMap essentiality scores tell you which cancer cell lines require this gene for survival (useful but not directly translatable to normal tissues). Expression specificity matters: a target expressed only in the disease-relevant tissue is far safer than one expressed ubiquitously in critical organs (heart, kidney, brain).
4. What is the competitive landscape?
A target with approved drugs may already be validated but competitive; a target with clinical-stage programs from competitors establishes feasibility while creating IP barriers. An entirely novel target with no drug history requires more extensive internal validation. Assess: number of ChEMBL bioactivity records (chemical matter depth), approved drugs from OpenTargets drug associations, and literature activity trends (recent paper count and key research groups). A dark target (Tdark) with strong genetic evidence but no chemical matter is a high-risk, high-reward opportunity.
Synthesizing the four dimensions: The ideal target has strong genetic evidence (GWAS + rare variant), a tractable binding site (Tclin or Tchem), acceptable safety profile (tissue-specific expression, non-lethal KO), and manageable competition. Gaps in any dimension represent validation tasks, not disqualifiers β but they must be acknowledged. A target with perfect druggability but no genetic link to disease is a tractability exercise, not a validated therapeutic hypothesis.
Phase 0: Tool Parameter Verification (CRITICAL)
BEFORE calling ANY tool for the first time, verify its parameters:
tool_info = tu.tools.get_tool_info(tool_name="Reactome_map_uniprot_to_pathways")# Reveals: takes `id` not `uniprot_id`
Known parameter corrections:
Reactome_map_uniprot_to_pathways: param is id (not uniprot_id)
ensembl_get_xrefs: param is id (not gene_id)
GTEx_get_median_gene_expression: requires gencode_id + operation="median"; try versioned Ensembl ID if empty
OpenTargets_*: param is ensemblId (camelCase, not ensemblID)
STRING_get_protein_interactions: takes protein_ids (list) + species
intact_get_interactions: takes identifier (UniProt accession, not gene symbol)
Critical Workflow Requirements
Report-First (MANDATORY): Create [TARGET]_target_report.md with all section headers and [Researching...] placeholders before starting research. Update progressively. Do not show raw tool outputs to the user.
Evidence Grading (MANDATORY): Grade every claim T1-T4. T1 = clinical/genetic data; T2 = curated databases or multiple studies; T3 = computational or single study; T4 = annotation or catalog entry.
Core Strategy: 9 Research Paths
Target Query (e.g., "EGFR" or "P00533")
|
+- IDENTIFIER RESOLUTION (always first)
| +- Check if GPCR -> GPCRdb_get_protein
|
+- PATH 0: Open Targets Foundation (ALWAYS FIRST - fills gaps in all other paths)
|
+- PATH 1: Core Identity (names, IDs, sequence, organism)
| +- InterProScan_scan_sequence for novel domain prediction
+- PATH 2: Structure & Domains (3D structure, domains, binding sites)
| +- If GPCR: GPCRdb_get_structures (active/inactive states)
+- PATH 3: Function & Pathways (GO terms, pathways, biological role)
+- PATH 4: Protein Interactions (PPI network, complexes)
+- PATH 5: Expression Profile (tissue expression, single-cell)
+- PATH 6: Variants & Disease (mutations, clinical significance)
| +- DisGeNET_search_gene for curated gene-disease associations
+- PATH 7: Drug Interactions (known drugs, druggability, safety)
| +- Pharos_get_target for TDL classification (Tclin/Tchem/Tbio/Tdark)
| +- BindingDB_get_ligands_by_uniprot for known ligands
| +- PubChem_search_assays_by_target_gene for HTS data
| +- If GPCR: GPCRdb_get_ligands (curated agonists/antagonists)
| +- DepMap_get_gene_dependencies for target essentiality
+- PATH 8: Literature & Research (publications, trends)
For detailed code implementations of each path, see IMPLEMENTATION.md.
Identifier Resolution (Phase 1)
Resolve ALL identifiers before any research path. Required IDs:
UniProt accession (for protein data, structure, interactions)
Ensembl gene ID + versioned ID (for Open Targets, GTEx)
Gene symbol (for DGIdb, gnomAD, literature)
Entrez gene ID (for KEGG, MyGene)
ChEMBL target ID (for bioactivity)
Synonyms/full name (for collision-aware literature search)
After resolution, check if target is a GPCR via GPCRdb_get_protein. See IMPLEMENTATION.md for resolution and GPCR detection code.
PATH 0: Open Targets Foundation (ALWAYS FIRST)
Run OpenTargets endpoints first to populate baseline data before specialized queries:
Reasoning: Expression specificity directly informs safety. Note whether expression is enriched in the disease-relevant tissue vs. critical organs. Ubiquitous essential expression narrows the therapeutic window.
Populates: Section 7 (Expression Profile)
PATH 6: Variants & Disease
Separate SNVs from CNVs in ClinVar results. Integrate DisGeNET for curated gene-disease association scores.
Required constraint scores: pLI (probability of loss-of-function intolerance), LOEUF (loss-of-function observed/expected upper bound), missense Z-score, pRec (recessive probability). High pLI (> 0.9) or low LOEUF (< 0.35) indicates the gene is intolerant to loss-of-function β a major safety flag for inhibitory therapeutic strategies.
Reasoning: Pharos TDL tells you where the target sits in the knowledge landscape. BindingDB Ki/IC50/Kd values tell you whether the target has been demonstrated tractable experimentally. DepMap essentiality tells you whether cancer cells require this gene (proxy for toxicity risk, not a definitive answer).
Disease association: Based on OpenTargets score (> 0.7 strong, 0.3-0.7 moderate, < 0.3 weak)
Druggability: Approved drug exists / Tractable (known binding site, chemical probes) / Predicted tractable (structural pocket) / Undruggable
Safety: Non-essential gene (viable KO, low pLI) / Essential with phenotype / Lethal KO or high pLI / Known toxicity target
Selectivity: Disease-specific or enriched expression / Ubiquitous / Expressed in critical organs
Structural data: High-res crystal with ligand / AlphaFold confident (pLDDT > 80) / Homology model / No structural info
Total score guides recommendation: strong target (all dimensions favorable), promising with defined validation tasks (2-3 gaps), speculative (multiple critical gaps), or deprioritize (no genetic link and poor druggability).
βΊAccess to product documentation and roadmap tools (Jira, Notion, etc.)
βΊUnderstanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
βΊStakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
β Not validating competitive researchβverify facts before sharing
β Accepting user stories without involving engineering team
β Over-relying on frameworks without qualitative judgment
β Not customizing outputs to company culture and communication style
β Skipping stakeholder validation of generated requirements
Best Practices
β Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
β Don't
βDon't publish competitive analysis without fact-checking
βDon't finalize user stories without engineering review
βDon't make prioritization decisions solely on AI scoring
βDon't skip customer validation of generated requirements
βDon't ignore company-specific context and culture
π‘ Pro Tips
β Provide context: company goals, constraints, customer feedback
β Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
β Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
β Use skill for 70% generation + 30% customization to company needs
When to Use This
β Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
β Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
