turnaround

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Turnaround analysis, stagnation detection, performance inflection, operational improvement, restructuring analysis, management change impact, cost cutting effectiveness, business transformation, recovery trajectory, operational metrics improvement

AI & Automation 204 stars 16 forks Updated today Apache-2.0

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Skill Content

<!-- analog: thesis-tracker --> ## Preflight Run the canonical pre-flight sequence — MCP health probe, ticker resolution, workspace `style.md` override, memory load, and coverage check. See `contracts/preflight.md`. Include the `X-Agentii-Trace` header on every tool call per `contracts/x-agentii-trace-header.md`. ## Triggers - analyze dim turnaround stagnation - run dim turnaround stagnation analysis - produce dim turnaround stagnation report - dim turnaround stagnation breakdown - dim turnaround stagnation deep dive - build a dim turnaround stagnation - assess dim turnaround stagnation - quantify dim turnaround stagnation - compare dim turnaround stagnation across peers - review dim turnaround stagnation for - generate dim turnaround stagnation on - dim turnaround stagnation for investment decision ## Defaults | Parameter | Default | Notes | |---|---|---| | lookback_years | 3 | Historical data window | | include_peers | false | Whether to surface a peer comparison block | <!-- BEGIN port-dimension-prompts methodology + modes --> ## Methodology ### Retrieval Scope This skill performs unstructured document search at scale (10-K, 10-Q, 8-K filings and earnings call transcripts spanning multiple fiscal periods). The three-layer agent-use-ready retrieval protocol (Document Discovery → Page Map → Deep Read) applies to all unstructured document search at scale. ### Retrieval Strategy Follow the retrieval strategy decision tree in `contracts/retrieval.md`. This skill use...

Details

Author
agentii-ai
Repository
agentii-ai/agentii-investment-intelligence
Created
4 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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