本文へ移動
cccskills
無料GitHub で公開

sector-overview-med

Med-sector overview across med.medicines_biotech and med.medical_devices with med trends, catalysts, FDA-decision context, and a cross-sector ripple map (quantified sign/magnitude per name). Use to frame any biotech/pharma/device analysis before diving into a single name.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md6.0 KB
  • references/knowledge-frameworks.md2.2 KB
  • references/modes.md1.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Methodology inspired by publicly taught sector-framing approaches; all text is an original paraphrase.

Defaults

ParameterDefault ValueRationale
taxonomy_levelindustry (2 med industries)Med sector frames at industry granularity
include_catalyststrueFDA decisions are the defining med-sector driver
lookback_quarters4Standard trend window

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

  • "Give me an overview of the biotech sector."
  • "How is the pharma industry structured?"
  • "What are the main med sector trends right now?"
  • "Which med sub-sectors are most catalyst-rich this quarter?"
  • "Map the medical-devices landscape for me."
  • "What's the regulatory backdrop for healthcare this year?"
  • "How do med.medicines_biotech names differ from med.medical_devices?"
  • "Summarize the FDA approval environment for the sector."
  • "What are the structural drivers of biotech valuations?"
  • "Which committees are most active in AdCom reviews lately?"

Production Grounding

  • Med industry scope: med.medicines_biotech and med.medical_devices; healthcare services and life-sciences tools are out of scope for the bio-pharm vertical (servable by generic skills).
  • FDA decisions (approve/CRL/AdCom) are the strongest sector catalysts; sector framing must include the catalyst calendar (search_adcom_meetings).
  • Cross-sector ripple map: for every sector-level catalyst (FDA decision, pricing action, funding regime, class readout), map the quantified sign and magnitude per covered name — a have/have-not stock map, not a headline. Magnitudes carry sources or [VIEW] labels; names with no exposure are listed as have-nots, not omitted.
  • Living-exhibit convention: sector exhibits are append-only — new observations append to the existing exhibit, prior entries are never rewritten. Every fact carries its source line.
  • Grounding frameworks: references/knowledge-frameworks.md (道/法 layered review knowledge).

Data Source Priority

  1. search_companies / list_coverage — sector membership and data freshness.
  2. search_adcom_meetings — catalyst calendar by committee/date.
  3. Knowledge layer: search_investment_strategies(sectors=med) + search_investment_cases(sectors=med) for sector-level plays.
  4. search_unified / search_documents — filings/news context.

Methodology

Retrieval Scope

unstructured_document_search

Retrieval Strategy

  1. Resolve sector scope via search_companies + taxonomy; count names per industry via list_coverage.
  2. Pull catalyst density: search_adcom_meetings for the window.
  3. Ground with med strategies/cases via the knowledge tools (sectors=med).
  4. Synthesize trends with cited evidence.

Temporal Scope

See frontmatter temporal_scope block.

Tool Allowlist

See frontmatter allowed_tools.

Protocol

  1. Taxonomy framing
  2. Catalyst mapping
  3. Knowledge grounding
  4. Trend synthesis

Modes

  • Full sector (default): both med industries with catalyst overlay.
  • Single industry: deep-dive one industry (e.g., medicines_biotech).
  • Catalyst-focused: sector view organized around upcoming FDA events.
  • Ripple map: one sector catalyst propagated to per-name sign/magnitude, appended to the living exhibit.

Tool Fallbacks

FailureFallback
search_companies emptyUse search_unified keyword search; annotate coverage_gap
search_adcom_meetings emptyDegrade to search_documents keyword "AdCom"; flag
Knowledge tools emptyProceed with structured data only; annotate knowledge coverage_gap

Output File

_sector/{YYYY-MM-DD_HHMM}_med-sector-overview-med_{affix}.md

Output Structure

  1. Executive Summary — sector stance in 2-3 sentences
  2. Taxonomy Map — industries + representative names + coverage stats
  3. Catalyst Calendar — dated FDA/earnings events shaping the sector
  4. Trends & Structural Drivers — with evidence
  5. Cross-Sector Ripple Map — per-name quantified sign/magnitude per sector catalyst (have/have-not stock map), appended to the living exhibit
  6. Knowledge Grounding — med strategies/cases with /v/ citations
  7. Coverage Gaps — data limitations and degraded flags

Error Handling

ErrorFallback
No taxonomy matchesBroaden via search_companies name search; flag degraded
Empty catalyst calendarNote sector catalysts may be sparse; rely on knowledge layer

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

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

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

日本語の概要は準備中です。原文の説明を表示しています。

agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

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).

日本語の概要は準備中です。原文の説明を表示しています。

agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

audit-xls

無料

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

日本語の概要は準備中です。原文の説明を表示しています。

agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

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

日本語の概要は準備中です。原文の説明を表示しています。

agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

challenge

無料

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.

日本語の概要は準備中です。原文の説明を表示しています。

agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

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

日本語の概要は準備中です。原文の説明を表示しています。

agentii-ai/agentii-investment-intelligence2072026年9月29日 更新

agentii-ai のスキルをすべて見る

このスキルの問題を報告する