.claude/skills/pharma-analystRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
Produce current, evidence-graded pharmaceutical and biotechnology analysis at the company, platform, pipeline, asset, indication, trial, competitive-landscape, catalyst, risk, and valuation levels. Use for biopharma diligence, investment-research-style reports, pipeline reviews, target or mechanism
These states come from the source or distribution context. None of the entries below are SkillVetAI compatibility test results.
These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
.claude/skills/pharma-analystRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
.agents/skills/pharma-analystRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
skills/pharma-analystRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.
clawhub install @sciminer/pharma-analystclawhub inspect @sciminer/pharma-analyst --version 1.0.0This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.
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The catalog stores hashes and an inventory summary for change detection. It does not republish the package contents.
sha256:0edd7b2d7892d590905783d0d08ad6524f232d08c12ffa28e580a1ce5e6f0622references/report-template.mdreferences/research-framework.mdskill-card.mdSKILL.mdInitial release of pharma-analyst skill. - Provides structured, evidence-graded biopharma analysis across company, platform, pipeline, clinical, competitive, and valuation levels. - Distinguishes between verified facts, company claims, analyst inferences, and speculation; maintains rigorous source citation and transparency. - Applies clear evidence grading (A–E) and calibration of terminology to reflect data strength and limitations. - Supports a range of standardized deliverables, from quick snapshots to full deep-dive reports. - Outlines stepwise workflow: framing, sourcing, fact ledger, modular analysis, evidence grading, synthesis, and reporting. - Enforces quality controls to ensure current, reliable, and decision-useful output.