Financial Report
monarchjuno/vibe-investing
Package investing analysis into a publishable financial-report style with institutional section flow, thesis framing, valuation context, catalysts, risks, tables, and financial charts.
Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact.
Install Earnings Analysis
$ npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ginlix-ai/LangAlpha earnings-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-analysis .claude/skills/earnings-analysis && rm -rf skills-srcUse ~/.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/
Install the "earnings-analysis" agent skill from https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysis into .claude/skills/earnings-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ginlix-ai/LangAlpha earnings-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ginlix-ai/LangAlpha.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugins/langalpha_research/skills/earnings-analysis .agents/skills/earnings-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "earnings-analysis" agent skill from https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysis into .agents/skills/earnings-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ginlix-ai/LangAlpha earnings-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ginlix-ai/LangAlpha.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugins/langalpha_research/skills/earnings-analysis .cursor/skills/earnings-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "earnings-analysis" agent skill from https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysis into .cursor/skills/earnings-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/ginlix-ai/LangAlpha.git --path plugins/langalpha_research/skills/earnings-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ginlix-ai/LangAlpha earnings-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ginlix-ai/LangAlpha.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugins/langalpha_research/skills/earnings-analysis .gemini/skills/earnings-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "earnings-analysis" agent skill from https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysis into .gemini/skills/earnings-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install ginlix-ai/LangAlpha earnings-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ginlix-ai/LangAlpha.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugins/langalpha_research/skills/earnings-analysis .github/skills/earnings-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "earnings-analysis" agent skill from https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysis into .github/skills/earnings-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ginlix-ai/LangAlpha earnings-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ginlix-ai/LangAlpha.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugins/langalpha_research/skills/earnings-analysis .opencode/skills/earnings-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "earnings-analysis" agent skill from https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/earnings-analysis into .opencode/skills/earnings-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
earnings-analysis- GitHub stars
- 1.8k
- Token cost
- ~2k tokens
- SKILL.md length
- 1,130 words
- Files
- 4 (incl. references)
- Skills in repo
- 37
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact.
- Works in 5 steps: Freshness gate → Extraction and beat/miss → Quality and drivers → …
- Earnings update
- SKILL.md covers Output modes, Evidence contract, The run and Length budget (deep dive), plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Earnings Analysis is an agent skill from ginlix-ai/LangAlpha. Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact. Also the call-only ask that wants the transcript Q&A and the debate map alone. Triggers on earnings update, post-earnings report, analyze quarterly results, Q[N] update, what management said on the call.
Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/best-practices.md`, `references/report-structure.md` and `references/workflow.md`).
It sits in Business, Finance & HR, covering Financial analysis and Essays and academic help. It works with Microsoft Word. The repository describes itself as: Claude Code for Financial Market. The licence is Apache-2.0.
When your agent uses it
- Earnings update
- Post-earnings report
- Analyze quarterly results
- What management said on the call
Example prompts
- “/earnings-analysis”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
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 Analysis loads about 2k tokens when it runs, and up to ~9.3k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 1,130 words of instructions outside code blocks.
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). 1,130 words, ~2,008 tokens.
Download SKILL.mdSave it as
.claude/skills/earnings-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- earnings-analysis
- description
- Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact. Also the call-only ask that wants the transcript Q&A and the debate map alone. Triggers on earnings update, post-earnings report, analyze quarterly results, Q[N] update, what management said on the call.
Earnings Update
A post-print report on a company already under coverage: what changed this quarter, whether the change recurs, what it does to estimates, and what it does to the thesis. Eight to twelve pages of DOCX, inside 48 hours of the release, written for a reader who already knows the company.
Route elsewhere when the request is a first-time initiation (.agents/skills/initiating-coverage/SKILL.md), a pre-print setup (.agents/skills/earnings-preview/SKILL.md), or a same-morning reaction blurb (.agents/skills/morning-note/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.
Output modes
| Mode | Fires when | Contract |
|---|---|---|
| Deep dive | the default | every phase present, the length budget below, assembled as a DOCX through .agents/skills/docx/SKILL.md |
| One-pager | the user asks for a one-pager, quick take or flash note | one page, in this order: decision box, beat/miss table with revenue and EPS variance, EPS-quality verdict, debate map, changed estimate lines with old against new. Delivered in chat unless the user asked for a document, and as a DOCX through .agents/skills/docx/SKILL.md when they did. No chart minimum |
| Debate map alone | the whole ask is the call or the Q&A | the transcript Q&A map and the debate map, each side carrying a falsifier tied to a dated catalyst. Delivered in chat, no report and no charts around them |
A mode is chosen once and holds, and a shorter mode is rebuilt at that depth rather than truncated (.agents/skills/research-conventions/references/depth.md). Missing inputs never shorten the note: a missing artifact stays visible as a labelled gap in the section that wanted it, using the absence vocabulary below.
The length budget, the chart count and the DOCX assembly belong to the deep dive. A short mode is complete on the contract in its own row; the freshness gate, the evidence contract and the tier 1 hard fails in references/best-practices.md bind every mode.
Evidence contract
Every user-facing number and every quote carries a findable citation: the artifact plus a location pointer that puts a reader on the figure in under thirty seconds. A location pointer is a page plus table, a page plus section heading, a slide number, or a transcript line range with the speaker. The document name alone is not a citation.
Sources resolve down one ladder, highest first:
- The filed 10-Q or 10-K for the quarter.
- The 8-K exhibit that carried the results.
- The earnings press release.
- The investor deck and the prepared remarks.
- The transcript, which is narrative support and never the source for a filed number.
When a document was reissued, cite the final version and keep the original timestamp beside it.
- Guidance that lives only in call commentary and in no filed document is labelled call-only guidance wherever it appears.
- Every non-GAAP figure appears with its closest GAAP comparable and the reconciliation source that bridges them.
- Consensus names the estimate set and its as-of timestamp, or states that the timestamp is unavailable.
- Absence is one of four words, never a blank and never a bare n/a: not guided (the company declined to guide it), not disclosed (the company does not publish it), not provided (it exists but is absent from the materials in hand), source not found (searched and unresolved, which is the
needs-sourcelabel in.agents/skills/research-conventions/references/evidence.md). - Every source ships as a hyperlink with display text, so a reader sees "10-Q" rather than the raw address, and SEC links point at the EDGAR viewer. The closing Sources section lists every material with its date and its link.
The delivered document is self-contained: a reader holding only the DOCX can follow every number in it without opening the model or the chart folder.
The run
Five phases. Each ends on its stated criterion; the detail behind each lives in references/workflow.md.
Phase 1: Freshness gate
Training data is old and the wrong quarter is the most expensive mistake this skill can make. Write down today's date, search for the most recent release rather than assuming which quarter is latest, and open the actual materials.
Complete when today's date, the release date, the transcript date and the filing date are all written down; the release is within 90 days of today; and every artifact names the same fiscal period, taken verbatim from the event name per .agents/skills/research-conventions/references/market-data-rules.md.
Show full SKILL.md (426 more words)Show less
Phase 2: Extraction and beat/miss
Pull reported results, pre-print consensus and our own prior estimates into one comparison, then decompose the variance by segment, geography, product and channel.
Complete when every headline metric has reported, expected and variance side by side; each cell carries a findable citation; every rate variance is stated in basis points; and reported and constant-currency figures sit in separate columns.
Phase 3: Quality and drivers
The analytical core: the EPS-quality screen, the two or three load-bearing drivers, the cash-quality check, the guidance read, the transcript Q&A map and the debate map.
Complete when the EPS-quality screen has either produced a recurring-EPS bridge or recorded "no material trigger identified"; two to three drivers are named with what moved, why it moved and what it does to forward expectations; the cash-quality module reconciles earnings to cash; and the debate map carries a falsifier on each side tied to a dated catalyst.
Phase 4: Estimates, valuation and model update
Revise forward estimates, restate or move the price target, and produce the model update in packet form unless the user supplied a workbook and asked for it to be written.
Complete when every changed line shows old, new and a one-clause reason; the price target is explicitly changed or explicitly maintained with its reason; and the update mode (packet or apply) is stated in the delivery message.
Phase 5: Charts, report and gates
In the deep dive, build eight to twelve charts and assemble the DOCX through .agents/skills/docx/SKILL.md. In a short mode, skip the charts and the document. Either way, run the three quality gates in references/best-practices.md.
Complete when the hard-fail list is clean, the delivery checklist is ticked for everything the chosen mode produces, the judgement gate passes on a note that answers what changed rather than summarising the quarter, and one posture from the ladder in .agents/skills/research-conventions/SKILL.md is stated near the top.
Length budget (deep dive)
| Dimension | Target |
|---|---|
| Pages | 8 to 12 |
| Words | 3,000 to 5,000 |
| Summary tables | 1 to 3, never a full P&L |
| Charts | 8 to 12, quarterly trends and changes |
| Typography | set by .agents/skills/docx/SKILL.md |
Deliverable
[Company]_Q[X]_[Year]_Earnings_Update.docx, for example Nike_Q2_FY24_Earnings_Update.docx. Charts come from Python (matplotlib, pandas). A workbook update is optional and follows the packet-or-apply rule in Phase 4.
Reference files
- The phase you are running, its steps and its tables:
references/workflow.md. - Writing a page or a section of the report, or the exact shape of the decision box, the recurring-EPS bridge, the Q&A map or the debate map:
references/report-structure.md. - Before delivery, and whenever a headline or a claim needs calibrating:
references/best-practices.md.
© 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
SKILL.md and 3 other files (references) in plugins/langalpha_research/skills/earnings-analysis of ginlix-ai/LangAlpha.
- SKILL.md
- references/best-practices.md
- references/report-structure.md
- references/workflow.md
Open the folder on GitHubat commit 2855e43
Compare with similar skills
Earnings Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Earnings Analysis this skillginlix-ai/LangAlpha | 1.8k | — | ~2k | Automated safety check: Pass | Apache-2.0 | |
| Financial Reportmonarchjuno/vibe-investing | 299 | — | ~1.3k | Automated safety check: Pass | MIT | |
| Earnings AnalysisWind-Alice/AliceMarket | 134 | 3 repos | ~2.2k | Automated safety check: Pass | None | |
| Earnings Report Analysisbyteseek/Mira | 275 | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Healthcare Equityhh-health-AI/healthcare-equity | 101 | — | ~770 | Automated safety check: Pass | MIT | |
| Longbridge Earningshelsome/folio | 271 | 1 repos | ~2.5k | Automated safety check: Pass | None |
Similar skills
Earnings Analysis
Wind-Alice/AliceMarket
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage.
Earnings Report Analysis
byteseek/Mira
Analyze earnings releases, filings, transcripts, guidance, peer comparisons, market reaction, and thesis impact for a company reporting event.
Healthcare Equity
hh-health-AI/healthcare-equity
A skill your agent uses for healthcare company initiation, earnings analysis, valuation framing, thesis development, screens, portfolio research, management meetings, sell discipline, international…
Longbridge Earnings
helsome/folio
Earnings analysis — pre- and post-earnings. An agent skill from helsome/folio.
Financial Analyzing
huangjia2019/claude-code-engineering
Analyze financial data, calculate financial ratios, and generate analysis reports.
More from ginlix-ai/LangAlpha
All 37 skills in this repoInvestment Deck Check
ginlix-ai/LangAlpha
Quality-checks an investment deck in .pptx form before it goes out: number consistency, chart and narrative alignment, source coverage, language and a circulation verdict.
Equity Initiation Report
ginlix-ai/LangAlpha
Produces a first-time equity research initiation report in five tasks: company research, financial model, valuation, charts and a DOCX report.
Three-Statement Model Builder
ginlix-ai/LangAlpha
Builds or repairs an integrated income statement, balance sheet and cash flow model in Excel with live formulas, supporting schedules, scenarios and a Checks sheet.
Financial Model Checker
ginlix-ai/LangAlpha
Audits an existing Excel financial model without editing it, checking structure, formulas, integrity identities and source tie-out, and ends in a prioritized issue log.
DCF Model Builder
ginlix-ai/LangAlpha
Builds a live Excel DCF valuation workbook with free cash flow projections, WACC, terminal value, three scenarios, sensitivity grids and a reverse DCF.
Word Documents for Human Review
ginlix-ai/LangAlpha
Builds Word files with python-docx, edits existing ones in place with tracked changes and comments, then renders and validates the result.
Related
Works with
Categories
Questions about Earnings Analysis
What does Earnings Analysis do?
Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact. Earnings Analysis is an agent skill from ginlix-ai/LangAlpha. Post-print earnings update for a covered name: beat/miss decomposition, EPS quality, transcript debate map, estimate revisions, thesis impact.
When should I use Earnings Analysis?
Earnings Analysis fits situations like: earnings update; post-earnings report; analyze quarterly results; what management said on the call.
How do I install Earnings Analysis in Claude Code?
Run `npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a claude-code`. Or copy the skill folder (plugins/langalpha_research/skills/earnings-analysis in ginlix-ai/LangAlpha) into .claude/skills/earnings-analysis in your project. Claude Code loads it when a task matches its description.
How do I install Earnings Analysis in Codex?
Run `npx skills add ginlix-ai/LangAlpha --skill earnings-analysis -a codex`. Or copy the skill folder (plugins/langalpha_research/skills/earnings-analysis in ginlix-ai/LangAlpha) into .agents/skills/earnings-analysis in your project. Codex loads it when a task matches its description.
Can I use Earnings Analysis 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-analysis -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-analysis, .gemini/skills/earnings-analysis, .github/skills/earnings-analysis and .opencode/skills/earnings-analysis in your project.
What does Earnings Analysis need to run?
SKILL.md names no scripts, command-line tools or credentials: Earnings Analysis is instructions for the agent only. Our summary lists: Python 3.
Does Earnings Analysis 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 Analysis 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 Analysis use?
Earnings Analysis 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 Analysis use?
About 2k tokens (SKILL.md is roughly 8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.3k tokens, read only when the agent opens those files.
What are the alternatives to Earnings Analysis?
Skills that share tags, products or a category with Earnings Analysis: Financial Report (monarchjuno/vibe-investing, 299 stars), Earnings Analysis (Wind-Alice/AliceMarket, 134 stars), Earnings Report Analysis (byteseek/Mira, 275 stars) and Healthcare Equity (hh-health-AI/healthcare-equity, 101 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Earnings Analysis?
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.