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Earnings Preview

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agentii-ai
earnings-preview

Earnings preview deck, quarterly earnings presentation, earnings summary slides, consensus vs actual presentation, earnings preview report, pre-earnings analysis, earnings expectations deck, quarterly preview, upcoming earnings summary, earnings announcement preview

Overview

Publisheragentii-ai
Repositoryagentii-investment-intelligence
Skill nameearnings-preview
Stars
207
Forks
16
Bundled files
8
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by agentii-ai on GitHub. Read the source before you install it.

Installation

Install the Earnings Preview AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/earnings-preview
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/agentii-ai/agentii-investment-intelligence.git /tmp/agentii-investment-intelligence
mkdir -p .claude/skills
cp -r /tmp/agentii-investment-intelligence/plugins/vertical-plugins/models-and-pitches/skills/agentii/earnings-preview .claude/skills/earnings-preview
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Earnings Preview in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Earnings Preview on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Earnings Preview is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Preflight

Run canonical pre-flight per contracts/preflight.md. Run the canonical pre-flight sequence — MCP health probe, ticker resolution, workspace style.md override, memory load, and coverage check. See contracts/preflight.md.

Office dependency probe (FR-043) — this skill produces .pptx via Bash + python-pptx:

  1. Live Office session? If mcp__office__* tools are present (Cowork), drive the live document instead of headless Python.
  2. Python library: Bash: python3 -c "import pptx" — if exit ≠ 0, fall back to .md slide spec with data_availability: degraded + python_pptx_missing: true.
  3. LibreOffice: Bash: which soffice for structural validation and PDF export.

If the Python library is absent, report the exact remediation (install the python-pptx package) and produce the .md degraded fallback per contracts/office-tooling.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

  • generate earnings preview deck
  • build earnings preview presentation
  • create quarterly earnings slides
  • earnings preview pptx
  • earnings summary presentation
  • consensus estimates presentation
  • earnings surprise summary deck
  • quarterly results presentation
  • earnings catalyst calendar slides
  • pre-earnings analyst deck

Defaults

ParameterDefaultNotes
slide_count4-6Title, Company Overview, Consensus Estimates, Historical Surprises, Catalysts, Outlook
lookback_quarters4Trailing 4 quarters for trend analysis
peer_count3-5From search_companies sector peers
source_footersrequiredEvery slide has standard agentii citation footer
templateinstitutional-defaultDark header bar, agentii blue accent, 12pt body

Methodology

Retrieval Scope

This skill performs structured data retrieval (earnings calendar, XBRL facts, company profile) plus earnings call transcript document search (search_documents(form_type="earnings_call_transcript") → read_source_outline → read_source_pages) for forward-looking catalyst and guidance context. See references/formula-sheet.md for presentation structure guidelines.

Retrieval Strategy

See contracts/retrieval.md for the canonical decision tree; skill-specific retrieval detail is in references/methodology.md.

Temporal Scope

Default: 4 fiscal quarters (max 8). Trailing 4 quarters captures current estimates and YoY comparisons. Maximum 8 quarters for analysts who want 2-year trend context on the estimates slide.

Tool Allowlist

See frontmatter allowed_tools. This skill produces a polished .md slide-deck specification; .pptx rendering is available via the companion financial-analysis:pptx-author skill (separate install; see contracts/office-tooling.md).

Protocol

Step-by-step execution detail is in references/methodology.md.

Deliverable Chain

Inputs → Build → Validate → Output → Next

  1. Inputs: resolved ticker + earnings calendar, consensus estimates, and trailing XBRL facts (search_earnings_calendar, search_xbrl_facts, search_companies, get_company_profile).
  2. Build: write a self-contained Python script using python-pptx that creates the 4–6 slide .pptx deck per ## Output Structure. Execute via Bash: python3 script.py. Verify the .pptx file exists. If python-pptx is absent, fall back to .md slide spec per contracts/office-tooling.md.
  3. Validate: run the ## Validation Gates below.
  4. Output: write the artifact path per ## Output File.
  5. Next: append to agentii.md; hand off to a downstream pitch/review skill if requested.

Validation Gates

  1. slide count: between 4 and 6. If failed: If outside range: refuse delivery.
  2. estimates slide: includes consensus, high, and low estimates. If failed: If missing: flag in Coverage Gaps.
  3. source footers: every slide has source_footer with standard agentii citation. If failed: If any missing: refuse delivery.
  4. peer comparison: has >= 3 peers. If failed: If < 3: flag in Coverage Gaps.

Tool Fallbacks

Per-tool failure modes and fallback actions are tabulated in references/tool-fallbacks.md.

Disclaimer (Q139/T137) — REQUIRED, template-owned

This output is presentation-shaped and MUST carry the canonical disclaimer from scenarios/templates/disclaimer.md — verbatim, not restated. The clause set is the contract; the wording is the workspace's rendering (Q126). Placeholders are filled, never shipped. The full requirement, the clause-set rule, and the per-slide footer rule for price targets are in references/disclaimer.md.

Output File

Primary deliverable: {ticker}/{YYYY-MM-DD_HHMM}_earnings-preview_{affix}.pptx — real PowerPoint binary via Bash + python-pptx per contracts/office-tooling.md. Degraded fallback: {ticker}/{YYYY-MM-DD_HHMM}_earnings-preview_{affix}.md when python-pptx is absent (FR-044).

Output Structure

The deliverable is a structured markdown report written to the path in ## Output File. Full section-by-section template (headings, tables, and field definitions) lives in references/output-structure.md. Required elements:

  1. Executive Summary — headline conclusions (≤200 words).
  2. Core analysis sections — per this skill's methodology and analyst modes.
  3. Data classification — tag findings [FACT] / [DEDUCTED] / [VIEW] per contracts/snapshot-synthesis.md.
  4. Coverage Gaps & Citations — inline /v/ citations are PRIMARY (immediately after each fact); the bottom Citations section is a non-duplicative roll-up index.
  5. Output frontmatter — emit the FR-090 structured block per contracts/output-frontmatter-schema.md.

Citations & memory: follow contracts/citation-and-memory.md — ≥1 citation per 200 words; every material fact, table row, and metric is immediately followed by its inline clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link; a bottom Citations section provides a non-duplicative roll-up index; the closing TUI reply includes a compact Key Citations list (headline 5–10 facts) of clickable /v/ URLs; and append the run to agentii.md per contracts/agentii-md-schema.md.

Memory & Snapshot

  • Memory load (pre-flight): load prior workspace context for the ticker before retrieval — see contracts/memory-load.md.
  • Structured output frontmatter: emit the FR-090 block (key_metrics, conclusions, facts_count, deducted_count, views_count, citation_count) per contracts/output-frontmatter-schema.md.
  • Snapshot synthesis: after writing the deliverable, update the two-tier snapshot and classify findings as [FACT]/[DEDUCTED]/[VIEW] — see contracts/snapshot-synthesis.md.
  • Session archival: record the run under sessions/{YYYY-MM-DD}/ and update sessions/INDEX.md per contracts/session-format.md.

Final Summary (TUI)

End the closing chat reply with a compact Key Citations list (headline 5–10 facts), each a clickable https://agentii.ai/v/{ticker}/{citation_id}/{N} link, so the user can cmd+click straight to the exact SEC page. See contracts/citation-and-memory.md.

Error Handling

Failure ModeDetectionActionUser-Facing Message
Missing earnings datasearch_earnings_calendar returns emptyUse search_xbrl_facts for historical actuals only; flag estimates as unavailable"Consensus estimates not available for {ticker}; presentation based on historical actuals only."
Partial data<80% expected fields returnedProceed with coverage gaps section"Presentation based on partial data; see Coverage Gaps."
Sector mismatchPeer sector != target sectorFilter out mismatched peers"Removed {n} peer(s) due to sector mismatch."
Insufficient history<4 quarters of data availableDowngrade to limited-history presentation (3 slides min)"Limited historical data available; presentation adjusted."
MCP unreachableagentii Preflight probe failsHalt with actionable error"agentii data plane unreachable; check connection and AGENTII_API_KEY."
Office backend unreachableAll 3 office backends fail PreflightHalt with AGENTII_OFFICE_UNREACHABLE"No office backend available. Options: (a) set AGENTII_API_KEY for agentii-office, (b) pip install python-pptx, (c) install OfficeCLI."
Knowledge Store unavailableget_entity_knowledge returns 503Fall back to get_company_profile + search_companies; flag with knowledge_store_degraded: true"Knowledge Store not yet available; peer analysis based on filing-derived entity context."

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Earnings Preview AI skill do?

Earnings preview deck, quarterly earnings presentation, earnings summary slides, consensus vs actual presentation, earnings preview report, pre-earnings analysis, earnings expectations deck, quarterly preview, upcoming earnings summary, earnings announcement preview

Why use Earnings Preview on TypingMind?

Because you install it once and use it with any model. Earnings Preview is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Earnings Preview in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/agentii-ai/agentii-investment-intelligence/tree/main/plugins/vertical-plugins/models-and-pitches/skills/agentii/earnings-preview. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Earnings Preview?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Earnings Preview?

As many as you like. As long as a model supports skills, you can use Earnings Preview with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Earnings Preview AI skill free?

Yes. It is published on GitHub by agentii-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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