본문으로 건너뛰기
cccskills
무료GitHub에 공개

trial-readout-analysis

Clinical-trial readout analysis: pull the trial, evaluate the readout with AdCom-style scrutiny (endpoints, statistics, subgroups, missing data, safety, tolerability/persistence), place it in a cross-trial comparison lattice vs SoC and class peers, and size the stock reaction with historical grounding. The judgment core for binary biotech events.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

실행 점수 미검증

설치 방법 보기

포함된 파일(3)

  • SKILL.md6.8 KB
  • references/knowledge-frameworks.md3.2 KB
  • references/modes.md1.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Methodology inspired by publicly taught clinical-trial frameworks; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
scrutiny_axesall six + tolerabilitySafety/stats/subgroups/missing data/endpoints/benefit-risk + tolerability/persistence as co-equal axis
lattice_comparatorsSoC + class peersEvery readout is placed against standard of care and same-class peers
safety_imbalancedefer to outcomesSmall-N safety imbalances defer the verdict to a larger outcomes trial
conflict_policymateriality-ratedConflicting readings surfaced verbatim, rated by materiality or deferred
outcome_framingbase/bull/bearBinary readouts need scenario sizing
reaction_contexthistorical casesSize moves from past analogues

Preflight

Run canonical pre-flight per contracts/preflight.md. Include the X-Agentii-Trace header on every tool call per contracts/x-agentii-trace-header.md — carry the _run_id from your first tool result and name yourself (and your parent, if you were spawned).

Triggers

  • "Evaluate [ticker]'s upcoming trial readout."
  • "What should I look for in [trial]'s data?"
  • "Size the readout for [drug] phase 3."
  • "What did the AdCom-style scrutiny say about similar trials?"
  • "Base/bull/bear for [ticker]'s readout."
  • "Which endpoints matter for [trial]?"
  • "How has the market reacted to similar readouts?"
  • "Readout checklist for [ticker]."
  • "Is this trial design adequate?"
  • "What are the red flags in [trial]'s design?"

Production Grounding

  • Readout ≠ approval: phase-3 success is necessary but not sufficient; FDA re-analyzes sponsor data.
  • Apply the six scrutiny axes (safety signals, statistical adequacy, subgroup analyses, missing data, endpoint appropriateness, benefit-risk) plus tolerability/persistence as a co-equal axis — discontinuation rates, dose reductions, and AE-driven dropout often decide commercial uptake. The 道/法 frameworks in references/knowledge-frameworks.md are the authoritative checklist.
  • Cross-trial lattice: every readout is placed against standard of care and same-class peers on aligned endpoints; a readout judged in isolation is incomplete.
  • Safety-imbalance deferral: a safety imbalance seen at readout scale defers the verdict to a larger outcomes trial; never over-weight small-N imbalances.
  • Materiality-rated conflicts: conflicting characterizations of the same data are surfaced verbatim and rated by materiality or deferred — never averaged.
  • Readout framing: readout design, then stock sizing (binary-risk expected value), then historical analogue comparison.

Data Source Priority

  1. search_clinical_trials / get_clinical_trial — design, status, endpoints, dates.
  2. search_documents / read_source_* — sponsor disclosure, prior data cuts.
  3. search_fda_approvals — regulatory history of the drug/program.
  4. Knowledge layer: search_investment_cases(event_type=trial_readout|adcom_vote) + strategies for judgment frameworks.

Methodology

Retrieval Scope

unstructured_document_search

Retrieval Strategy

  1. Pull the trial record (get_clinical_trial by NCT id, or search_clinical_trials by drug/ticker).
  2. Assess design + endpoint quality against scrutiny axes, including tolerability/persistence.
  3. Build the cross-trial lattice: comparator trials for SoC and class peers on aligned endpoints.
  4. Frame base/bull/bear outcomes with sizing; defer safety-imbalance verdicts to outcomes trials where needed.
  5. Ground in historical readout/adcom cases via knowledge tools.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Trial record
  2. Scrutiny-axes assessment (incl. tolerability/persistence)
  3. Cross-trial lattice vs SoC and class peers
  4. Outcome scenarios + sizing
  5. Analogue grounding

Modes

  • Pre-readout (default): design scrutiny + scenario sizing.
  • Post-readout: results evaluation + reaction context.
  • Program view: multiple trials across a program.

Tool Fallbacks

FailureFallback
search_clinical_trials emptyUse filings + press via search_documents; annotate coverage_gap
Trial record thinNote undisclosed fields; do not fabricate
Knowledge tools emptyProceed with structured data + static frameworks

Output File

{ticker}/{YYYY-MM-DD_HHMM}_trial-readout-analysis_{affix}.md

Output Structure

  1. Executive Summary — readout stance in 2-3 sentences
  2. Trial Profile — design, endpoints, status, dates
  3. Scrutiny Assessment — the six axes plus tolerability/persistence, with evidence
  4. Cross-Trial Lattice — aligned endpoints vs standard of care and class peers
  5. Outcome Scenarios — base/bull/bear with sizing; safety-imbalance deferrals flagged
  6. Historical Analogues — cases with /v/ citations
  7. Coverage Gaps — degraded flags

Error Handling

ErrorFallback
NCT id unknownSearch by drug/ticker; flag if unresolved
Endpoints undisclosedFlag explicitly; scrutiny limited to disclosed data
Conflicting readings of the same dataSurface both verbatim; rate materiality or defer to a larger outcomes trial — never average
No comparator data for the latticeMark lattice cells unavailable; flag the gap — do not guess

Memory Load

See contracts/memory-load.md.

Snapshot

See contracts/snapshot-synthesis.md.

Final Summary (TUI)

Include ### Key Citations block with 0-10 clickable /v/ URLs.

References

  • contracts/citation-and-memory.md
  • contracts/output-frontmatter-schema.md
  • contracts/memory-load.md
  • contracts/snapshot-synthesis.md
  • contracts/preflight.md
  • references/knowledge-frameworks.md

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

3-statement financial model, integrated IS BS CF, income statement projection, balance sheet forecast, cash flow statement, cross-statement balancing, financial model build, operating model, three statement model, integrated financial statements

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agentii-ai/agentii-investment-intelligence2072026년 10월 20일 업데이트

Institutional-grade equity research skills for AI agents. 80 Claude-type skills across 14 verticals (equity-research-core, models-and-pitches, bio-pharm, scenarios, quantitative-analysis, idea-generation, options-derivatives, business-intelligence, industry-analysis, macro-strategy, portfolio-strategy, technical-analysis, risk-and-psychology, trading-as-business) powered by agentii.ai's agent-use-ready SEC filing data plane — SEC filings and 15K+ earnings call transcripts, 15.99M XBRL facts, and company profiles for 1,146+ US-public-equity tickers. Features the three-layer retrieval protocol (Document Discovery → Page Map → Deep Read, with deep-outline escalation) — measured, not asserted: 97.5% saved against a sequential read, and the page map itself is 10.0% of one (spec 062 T030, 2026-09-23) — server-side parallel multi-period search via search_cross_period, a full Excel/PPT generation pipeline with 3-tier office backend support, and the spec-046 governance layer (constitution, thesis and single-skill modes, mechanical gates, and a template-owned disclaimer on generated reports).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agentii-ai/agentii-investment-intelligence2072026년 10월 20일 업데이트

audit-xls

무료

Audit spreadsheet, formula error detection, hardcoded cell finder, cross-sheet reference audit, workbook auditor, Excel model audit, financial model QA, spreadsheet review, cell dependency trace, formula integrity check

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agentii-ai/agentii-investment-intelligence2072026년 10월 20일 업데이트

Business model classification, business model analysis, structural analysis of how a company makes money, product offering decomposition, distribution channel analysis, customer segment analysis, revenue model identification, market sizing TAM SAM SOM, competitive positioning, business unit performance, management team & leadership analysis, what does the company sell, how does the company go to market, business model type platform service product, channel mix direct vs indirect, revenue concentration risk, CEO CFO executive backgrounds and changes

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agentii-ai/agentii-investment-intelligence2072026년 10월 20일 업데이트

challenge

무료

Adversarial verification of research theses — cross-run/cross-thesis contradiction via the entity index, pre-mortem (Klarman/Kahneman: assume the loss already happened, reverse the path), inversion (Munger: invert, always invert), and wrong_if falsifiability review. ≈50-finding cap sorted by severity; content-derived finding IDs; incremental scoping with three backstops; lifecycle hooks at pillar-complete / thesis-complete / pre-reduction.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agentii-ai/agentii-investment-intelligence2072026년 10월 20일 업데이트

Chart pattern recognition, price action patterns, candlestick signal bars, pullback bar counting H1/H2/H3/H4, trend channels, micro channels, trading ranges, breakouts, major trend reversals 5-step sequence, wedges, double tops and bottoms, triangles, head and shoulders, climaxes, measured moves, support and resistance

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

agentii-ai/agentii-investment-intelligence2072026년 10월 20일 업데이트

agentii-ai의 스킬 모두 보기

이 스킬의 문제 신고