Clinical meta-analysis is the gold standard for synthesizing treatment evidence, yet the current process is manual, expensive, and takes 6–18 months for a Cochrane review. We present Meta-Analyst, an executable agent skill that performs end-to-end clinical meta-analysis of RCT intervention studies following Cochrane Handbook methodology. The skill implements a three-phase pipeline: (1) PICO-driven literature identification across PubMed, Cochrane CENTRAL, and ClinicalTrials.gov with abstract screening and PRISMA flow generation; (2) structured data extraction with majority-vote reliability and per-study Risk of Bias 2.0 assessment via composition with the Evidence Evaluator skill; and (3) deterministic statistical synthesis including DerSimonian-Laird random-effects pooling, heterogeneity quantification, sensitivity analyses, publication bias testing, and GRADE certainty ratings. All statistical computation is performed by 8 deterministic Python modules (scipy/statsmodels/numpy) validated by 510 unit tests plus 72 integration tests. The skill outputs a Cochrane-style Markdown report and structured JSON. Three human checkpoints at Cochrane decision points preserve researcher oversight. Meta-Analyst demonstrates that meta-analysis can be executable, reproducible, and agent-native while remaining fully auditable. ---
Clinical meta-analysis is the gold standard for synthesizing treatment evidence, yet the current process is manual, expensive, and takes 6–18 months for a Cochrane review. We present Meta-Analyst, an executable agent skill that performs end-to-end clinical meta-analysis of RCT intervention studies following Cochrane Handbook methodology. The skill implements a three-phase pipeline: (1) PICO-driven literature identification across PubMed, Cochrane CENTRAL, and ClinicalTrials.gov with abstract screening and PRISMA flow generation; (2) structured data extraction with majority-vote reliability and per-study Risk of Bias 2.0 assessment via composition with the Evidence Evaluator skill; and (3) deterministic statistical synthesis including DerSimonian-Laird random-effects pooling, heterogeneity quantification, sensitivity analyses, publication bias testing, and GRADE certainty ratings. All statistical computation is performed by 8 deterministic Python modules (scipy/statsmodels/numpy) validated by 510 unit tests plus 72 integration tests. The skill outputs a Cochrane-style Markdown report and structured JSON. Three human checkpoints at Cochrane decision points preserve researcher oversight. Meta-Analyst demonstrates that meta-analysis can be executable, reproducible, and agent-native while remaining fully auditable. ---
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run. The pipeline combines LLM-driven extraction (PICO, RoB 2.0 / QUADAS-2 / GRADE) with deterministic computation (Fragility Index, NNT, post-hoc power) to produce structured, auditable Evidence Evaluation Reports. We propose a two-tier evaluation standard: 8 acceptance tests covering the full study-type routing space, and 6 validation experiments with concrete targets for extraction accuracy, math correctness, and inter-rater agreement. Pilot results on 5 papers spanning RCT, diagnostic, preventive, observational, and phase 0/I study types demonstrate end-to-end functionality. Evidence Evaluator is available at `github.com/SciSpark-ai/evidence_evaluator`. ---
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run. The pipeline combines LLM-driven extraction (PICO, RoB 2.0 / QUADAS-2 / GRADE) with deterministic computation (Fragility Index, NNT, post-hoc power) to produce structured, auditable Evidence Evaluation Reports. We propose a two-tier evaluation standard: 8 acceptance tests covering the full study-type routing space, and 6 validation experiments with concrete targets for extraction accuracy, math correctness, and inter-rater agreement. Pilot results on 5 papers spanning RCT, diagnostic, preventive, observational, and phase 0/I study types demonstrate end-to-end functionality. Evidence Evaluator is available at `github.com/SciSpark-ai/evidence_evaluator`. ---
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run. The pipeline combines LLM-driven extraction (PICO, RoB 2.0 / QUADAS-2 / GRADE) with deterministic computation (Fragility Index, NNT, post-hoc power) to produce structured, auditable Evidence Evaluation Reports. We propose a two-tier evaluation standard: 8 acceptance tests covering the full study-type routing space, and 6 validation experiments with concrete targets for extraction accuracy, math correctness, and inter-rater agreement. Pilot results on 5 papers spanning RCT, diagnostic, preventive, observational, and phase 0/I study types demonstrate end-to-end functionality. Evidence Evaluator is available at `github.com/SciSpark-ai/evidence_evaluator`. ---
Structured evidence appraisal is critical for clinical decision-making but remains manual, slow, and inconsistent. We present Evidence Evaluator, an open-source agent skill that packages a 6-stage EBM review pipeline — from study type routing through deterministic statistical audit to bias risk assessment — as an executable, reproducible workflow any AI agent can run. The pipeline combines LLM-driven extraction (PICO, RoB 2.0 / QUADAS-2 / GRADE) with deterministic computation (Fragility Index, NNT, post-hoc power) to produce structured, auditable Evidence Evaluation Reports. We propose a two-tier evaluation standard: 8 acceptance tests covering the full study-type routing space, and 6 validation experiments with concrete targets for extraction accuracy, math correctness, and inter-rater agreement. Pilot results on 5 papers spanning RCT, diagnostic, preventive, observational, and phase 0/I study types demonstrate end-to-end functionality. Evidence Evaluator is available at `github.com/SciSpark-ai/evidence_evaluator`. ---