Agent skill

Earnings Preview

by ginlix-ai in ginlix-ai/LangAlpha

Pre-print setup for a company about to report: the expectation bar, EPS-quality watch, call questions, scenarios and the reaction framework.

Apache-2.0Auto-check passedBusiness, Finance & HR

Install Earnings Preview

skills CLI
$ npx skills add ginlix-ai/LangAlpha --skill earnings-preview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ginlix-ai/LangAlpha earnings-preview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ginlix-ai/LangAlpha.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/langalpha_research/skills/earnings-preview .claude/skills/earnings-preview && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
earnings-preview
GitHub stars
1.8k
Token cost
~3.7k tokens
SKILL.md length
2,153 words
Files
1
Skills in repo
37
Repo updated
First seen
Licence
Apache-2.0

At a glance

Pre-print setup for a company about to report: the expectation bar, EPS-quality watch, call questions, scenarios and the reaction framework.

  • Works in 9 steps: Freeze the clock and map the period → Build the expectation bar → Watch the EPS quality before the print → …
  • Earnings preview
  • SKILL.md covers Route, Correctness rules, Step 1: Freeze the clock and… and Step 2: Build the expectation…, plus 8 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Earnings Preview is an agent skill from ginlix-ai/LangAlpha. Pre-print setup for a company about to report: the expectation bar, EPS-quality watch, call questions, scenarios and the reaction framework. Triggers on earnings preview, what to watch for [company] earnings, pre-earnings setup, preview Q[N].

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Stock and market analysis. The repository describes itself as: Claude Code for Financial Market. The licence is Apache-2.0.

When your agent uses it

  • Earnings preview
  • What to watch for [company] earnings
  • Pre-earnings setup

Example prompts

  • “/earnings-preview”

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Freeze the clock and map the period
  2. Build the expectation bar
  3. Watch the EPS quality before the print
  4. Bridge the guide to an implied bar
  5. Choose the metrics that decide the quarter
  6. Read through from everyone who already reported
  7. Scenarios and the reaction framework
  8. Write the call questions
  9. Assemble and deliver

What it can do on your machine

Read from SKILL.md and the folder at commit 2855e43. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Earnings Preview loads about 3.7k tokens when it runs. Until then it costs about 65 tokens; SKILL.md has 2,153 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~65
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ginlix-ai/LangAlpha at commit 2855e43, republished under its Apache-2.0 licence (© ginlix-ai). 2,153 words, ~3,688 tokens.

Download SKILL.mdSave it as .claude/skills/earnings-preview/SKILL.md (or your agent's skills folder).
name
earnings-preview
description
Pre-print setup for a company about to report: the expectation bar, EPS-quality watch, call questions, scenarios and the reaction framework. Triggers on earnings preview, what to watch for [company] earnings, pre-earnings setup, preview Q[N].

Earnings Preview

Everything in this note locates the stock against one object: the bar, the level of results the market is already positioned for. Consensus is one input to the bar, not the bar itself. The preview says where the bar sits, what could clear or miss it, what to listen for on the call, and what the reaction is likely to be if the print lands each way.

After the print, route to .agents/skills/earnings-analysis/SKILL.md.

Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first deliverable.

Route

The full preview is the default. A short version is produced only when the user asks for one, and it is rebuilt at the lower depth rather than truncated (.agents/skills/research-conventions/references/depth.md): the freeze time, the source posture, the bar numbers, the top two debates, the must-watch call questions and the missing-evidence block all survive the rebuild.

Missing inputs never shorten the note. A section whose input is unavailable keeps its heading and carries a labelled gap saying what is missing and what would supply it. A short note reads as a thin setup; a full note with three labelled gaps reads as the truth.

Correctness rules

Every number follows the shared market-data rules: .agents/skills/research-conventions/references/market-data-rules.md covers fiscal periods, per-company LTM, NTM as four quarterly estimates, one base date for comparative returns, shown arithmetic, rate against level changes, margin selection and date-stamping. Read it before the first pull.

Preview-specific, on top of those:

  • Freeze the clock. Record one as-of moment for the whole set of time-sensitive inputs: consensus, whisper, options, price, positioning. State it at the top of the note. An input with no as-of is a gap, not an estimate.
  • Suppress a percent change when the denominator is zero, negative or definitionally unstable, and show the absolute delta instead.
  • Mark a year-over-year comparison non-comparable when the company changed the metric's definition and published no restated bridge.

Step 1: Freeze the clock and map the period

Pull the setup: get_company_overview for consensus, earnings history and price targets; get_daily_prices for the price history and the event window; get_sec_filing for the prior quarter, with its transcript attached, to recover the guidance in force; get_shares_float and get_short_data for the positioning picture; WebSearch and WebFetch for news since that filing.

Pin the period map once and reuse it everywhere, taking each fiscal label from the earnings event name get_company_overview returns: the preview quarter, the prior quarter, the year-ago quarter and the two-years-ago quarter. Every table, chart, scenario and options reference uses those same four labels. Ask the user only when the preview quarter is genuinely ambiguous, for example when the company reports two quarters inside one month.

Complete when the four period labels are written down, the freeze-time as-of is recorded, and the earnings date is confirmed from the company or get_earnings_calendar rather than inferred.

Step 2: Build the expectation bar

Five expectation sources, kept separate because they say different things. Collapsing them is how a preview ends up measuring the print against the wrong number.

SourceWhat it isWhere it comes from
Company guidethe range management put in force, with its date and venueprior release, prior transcript, any 8-K since
Published consensusthe mean estimate and the analyst count, with its vintageyf_analysis MCP get_earnings_estimates and get_revenue_estimates, the 0q record's avg and numberofanalysts; the feed carries no observation date, so the vintage is the retrieval time, labelled as such
Whisperthe buy-side number, when it is genuinely sourcedsee the whisper rules below
Our basewhat we expect, built from the driversour own model or the driver work in this note
Last-reported baselinethe operating run-rate the company actually printed last quarterprior release

Close the table with one sentence stating where the bar sits and why: which of the five the stock is trading against, and how far the others sit from it.

Whisper rules. A whisper is never blended into consensus without showing the bridge. With strong support, carry it as a value with its provenance and a confidence label. With weak support, convert it to qualitative setup language rather than a number. With no external whisper at all, either derive an implied whisper from the guide midpoint plus this management's beat history and label it analyst-derived, or write "not provided" and leave the row empty.

Complete when all five rows are filled or carry an absence reason, and the bar sentence states the selected bar's value, source, vintage and basis, labelled judgement wherever the five rows leave competing bars in play.

Step 3: Watch the EPS quality before the print

Headline EPS is a bad proxy for recurring earnings more often than a preview assumes, and the items that break it are knowable in advance.

ItemWhy it distortsPre-print riskWhat resolves it after the printModel line it hits

Screen at least these: the effective tax rate against the guided or trailing rate, the diluted share count (buyback timing, issuance, convertible dilution), below-the-line and mark-to-market items, FX translation and remeasurement, disposals, impairments, restructuring and litigation.

Then check the basis: does published consensus measure the same thing the company reports? Where it does not, say so, and say which figure the bar actually rests on. The real bar is often revenue, operating income, segment profit or free cash flow rather than EPS, and a preview that assumes EPS measures the wrong quarter.

Complete when each screened item carries a pre-print risk, and the note states whether consensus and company bases match.

Step 4: Bridge the guide to an implied bar

A guide is a starting point, not an expectation. Convert it using this management's own record.

  • Beat frequency over the last eight quarters, against its own guide, per metric.
  • Typical beat size, in currency for level metrics and basis points for rate metrics.
  • Whether the conservatism is steady, widening or fading, and what changed if it moved.
  • The limits behind those statistics: how many quarters the sample has, whether the management team or the guidance policy changed inside it.

Complete when the implied bar is stated as a number or range with the beat history that produced it, and the sample limits are named.

Step 5: Choose the metrics that decide the quarter

Financial: revenue in total and by segment, EPS, gross and operating margin, free cash flow, and forward guidance against consensus. Rank them by which one moves the stock, not by which one leads the P&L.

Operational, by sector, as the default pack: software and internet (ARR, net revenue retention, remaining performance obligation, customer count), retail (comparable-store sales, traffic, basket, inventory), industrials (backlog, book-to-bill, price against volume), financials (net interest margin, credit quality, loan growth, fee income), healthcare (scripts, patient volumes, pipeline events), energy and materials (volumes, realised price, unit cost).

Selection rule. The issuer's own disclosure model beats the generic pack. Use the metrics this company guides on and gets asked about, and where an important sector metric is not disclosed, list it as a data request rather than dropping it silently.

Flag as material: a gap between guide, consensus and whisper wider than the sector norm; a metric breaking its trailing four-quarter slope; growth decelerating or margin compressing past a stated threshold in basis points. For a seasonally distorted metric or one with a distorted base year, show the two-year stacked growth rate beside the year-over-year rate.

Complete when the metric list is ranked, each entry carries the expectation it will be measured against, and undisclosed metrics appear as data requests.

Show full SKILL.md (902 more words)Show less

Step 6: Read through from everyone who already reported

NameRelationshipWhat they reportedWhy it matters hereRead-throughConfidence

Cover competitors, suppliers, customers and sector bellwethers that have printed since the subject's last report, plus the macro releases that bear on the quarter (get_economic_calendar, get_economic_indicator). A read-through with no named transmission channel is a coincidence, not a signal, so state the channel or drop the row.

Complete when every row names the channel and carries a confidence label, and the table says which read-throughs are already in consensus.

Step 7: Scenarios and the reaction framework

Three scenarios against the bar, not against last year:

ScenarioRevenueEPSKey driverManagement tellFalsifierLikely reaction
Bull
Base
Bear

Calibrate the reaction column on history rather than intuition:

QuarterResult against the barNext-day moveWhat drove the moveContinued or reversed

Then state what is already priced, and the two asymmetries that matter: what would make a weak print buyable, and what would make a strong print fadeable.

Options tenor caveat. The implied move is a two-step pull on the options MCP server, in the same session the note is written. First discover the contracts with get_options_chain: expiration_date_gte and expiration_date_lte bracketing the print date, strike_price_gte and strike_price_lte straddling spot, contract_type run once as call and once as put. It returns contract tickers, strikes and expiries and no prices at all. Then price the at-the-money pair with get_options_snapshot, handing those tickers back as a comma-separated options_tickers string: the last_quote midpoint while the market is open, the session close once it has shut. The implied move is the straddle mid over spot, and the expiry is recorded beside it. An options-implied move is an earnings hurdle only when the expiry brackets the event tightly. When the nearest expiry sits well past the print, relabel the figure as expiry-tenor volatility context, keep it out of the headline tiles, and say what it does and does not measure.

Complete when each scenario names its driver and its falsifier, the reaction column is grounded in the historical table, and any implied move carries its expiry and tenor.

Step 8: Write the call questions

Three or four must-watch items, ranked, and nothing else at the top. A list of eight equally weighted questions is not a plan. Everything else goes into an overflow bank below them.

Each must-watch question carries four things:

  1. Why it matters, in one clause tied to the bar or a thesis pillar.
  2. The answer that validates the view.
  3. The answer that breaks it.
  4. The specific phrases to listen for, including the hedges that signal an answer is being avoided.

Any material news since the last report that creates a contradiction or an open diligence item becomes a question here rather than a standalone news bullet. The overflow bank holds the sector add-ons and the second-tier items, and can run to eight or so.

Complete when the must-watch list is three or four items, each ranked with all four parts, and every open news item is either a question or explicitly closed.

Step 9: Assemble and deliver

Save deliverables to {task}/. A formatted document, when the user wants one, is built through .agents/skills/docx/SKILL.md. Sections, in order:

  1. Company, quarter, earnings date, freeze-time as-of, readiness posture.
  2. The expectation bar table and the bar sentence.
  3. Metrics to watch, ranked, with the materiality flags.
  4. EPS-quality watch and the guidance-credibility bridge.
  5. Peer and macro read-throughs.
  6. Scenarios, historical reactions, what is priced.
  7. Must-watch call questions, then the overflow bank.
  8. Trading setup: recent performance, positioning, and the implied move with its tenor caveat.
  9. Missing evidence.
  10. Position action.

Salience first. When a growth rate, an acceleration, a surprise percentage or a guide delta is what moves the stock, that is the headline number and the absolute figure is the supporting detail. Trend charts pick the margin the business is actually run on (operating, adjusted operating, EBITDA, contribution or free cash flow) rather than defaulting to net margin, and the note says which one and why.

Missing evidence block. Close with the exact refreshes required before taking event risk: each missing input, the tool or document that supplies it, and the conclusion it currently blocks.

Position action. One verb from the closed vocabulary in .agents/skills/research-conventions/references/judgment.md, gated by the inputs actually in hand. Without a sourced implied move, positioning context and adequate consensus or whisper evidence, the note delivers a setup and a reaction framework rather than a trade-ready instruction, and says which input is missing.

Pre-delivery checks

  • Period map explicit, and the same four labels used in every table and chart.
  • Freeze-time as-of stated, and every time-sensitive figure inside its freshness threshold or marked aging.
  • Every bar number carries source, as-of, unit and definition.
  • Guidance, consensus and whisper appear as separate rows, never blended.
  • EPS-quality section present whenever EPS is the bar, with the consensus-basis check stated.
  • Rate figures in basis points, level figures in percent or currency, no percent on an unstable denominator.
  • Chart axes agree with the units in the adjacent table.
  • Each scenario states its driver and its falsifier.
  • Implied move either brackets the event or is relabelled as tenor context.
  • Must-watch questions capped at four and ranked.
  • Missing-evidence block present, even when empty, and every gap labelled rather than dropped.
  • Readiness posture stated, and it names the input holding it back when it is below decision-grade.

© ginlix-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/langalpha_research/skills/earnings-preview of ginlix-ai/LangAlpha.

Open the folder on GitHubat commit 2855e43

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Questions about Earnings Preview

What does Earnings Preview do?

Pre-print setup for a company about to report: the expectation bar, EPS-quality watch, call questions, scenarios and the reaction framework. Earnings Preview is an agent skill from ginlix-ai/LangAlpha. Pre-print setup for a company about to report: the expectation bar, EPS-quality watch, call questions, scenarios and the reaction framework.

When should I use Earnings Preview?

Earnings Preview fits situations like: earnings preview; what to watch for [company] earnings; pre-earnings setup.

How do I install Earnings Preview in Claude Code?

Run `npx skills add ginlix-ai/LangAlpha --skill earnings-preview -a claude-code`. Or copy the skill folder (plugins/langalpha_research/skills/earnings-preview in ginlix-ai/LangAlpha) into .claude/skills/earnings-preview in your project. Claude Code loads it when a task matches its description.

How do I install Earnings Preview in Codex?

Run `npx skills add ginlix-ai/LangAlpha --skill earnings-preview -a codex`. Or copy the skill folder (plugins/langalpha_research/skills/earnings-preview in ginlix-ai/LangAlpha) into .agents/skills/earnings-preview in your project. Codex loads it when a task matches its description.

Can I use Earnings Preview in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ginlix-ai/LangAlpha --skill earnings-preview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/earnings-preview, .gemini/skills/earnings-preview, .github/skills/earnings-preview and .opencode/skills/earnings-preview in your project.

What does Earnings Preview need to run?

SKILL.md names no scripts, command-line tools or credentials: Earnings Preview is instructions for the agent only.

Does Earnings Preview access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Earnings Preview safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Earnings Preview use?

Earnings Preview is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Earnings Preview use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Earnings Preview?

Skills that share tags, products or a category with Earnings Preview: Stock API (zhangxiangliang/stock-api, 2k stars), Tushare Data (zillionare/zillionare, 322 stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars) and Digital Oracle (komako-workshop/digital-oracle, 878 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Earnings Preview?

ginlix-ai (a GitHub organization) maintains it in ginlix-ai/LangAlpha, which has 1,811 GitHub stars. The repository holds 37 skills in this directory. The repository was last updated on October 10, 2026.

Source: ginlix-ai/LangAlpha on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.