How do I install the Earnings Orchestrator skill?

Run npx skillmds@latest add majiayu000/earnings-orchestrator-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Earnings Orchestrator skill do?

Master orchestrator for batch earnings analysis It is listed under Coding & Dev Tools on SkillMD.

Is Earnings Orchestrator safe to use?

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Which AI agents work with Earnings Orchestrator?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Earnings Orchestrator free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Earnings Orchestrator?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Earnings Orchestrator by majiayu000

Name: Earnings Orchestrator
Rating: 4.5 (4 reviews)
Author: majiayu000

Earnings Orchestrator

Input

$ARGUMENTS = TICKER

TICKER: Company ticker (required)

Task - MUST COMPLETE ALL STEPS

Step 0: Record Start Time

echo "=== START: $(date '+%Y-%m-%d %H:%M:%S') ==="

Step 1: Get Earnings Data

source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_earnings.py {TICKER}

Parse: Extract E1 (first data row after header), E2 (second data row after header). The script returns data sorted oldest-to-newest, so E1 is the OLDEST quarter, E2 is the second oldest. Only process these two quarters. Note trailing_vol and fye_month for each. The fye_month (1-12) indicates the company's fiscal year end month (e.g., 9=September for Apple, 12=December for most companies).

If ERROR returned: Stop and report error to user.

Step 1b: Check Processing Cache

Check both earnings-analysis/news_processed.csv and earnings-analysis/guidance_processed.csv for {TICKER}.

News cache: (format: ticker|quarter|fiscal_year|processed_date)

Find row where ticker={TICKER} AND quarter={E1.fiscal_quarter} AND fiscal_year=FY{E1.fiscal_year}
If row exists → Q1_NEWS_CACHED=true
Repeat check for Q2 → Q2_NEWS_CACHED

Guidance cache: (format: ticker|quarter|fiscal_year|processed_date)

Find row where ticker={TICKER} AND quarter={E1.fiscal_quarter} AND fiscal_year=FY{E1.fiscal_year}
If row exists → Q1_GUIDANCE_CACHED=true
Repeat check for Q2 → Q2_GUIDANCE_CACHED

Skip logic for each quarter:

Q1_NEWS_CACHED	Q1_GUIDANCE_CACHED	Action
false	false	Run full Steps 2, 3, 3b for Q1 (both tracks)
true	false	Skip news in Steps 2, 3, 3b; run guidance only
false	true	Skip guidance in Steps 2, 3, 3b; run news only
true	true	Skip Steps 2, 3, 3b entirely for Q1 → go to Step 4

Same logic applies to Q2 with Steps 4, 4b.

Step 2: Discovery for Q1 (News + Guidance in parallel)

Calculate:

START = E1 date minus 3 months (or earliest available data)
END = E1 date (just the date part, e.g., 2024-02-01)

Run discovery scripts in parallel (only for non-cached tracks):

# News discovery (SKIP if Q1 news cached)
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_significant_moves.py {TICKER} {START} {END} {E1.trailing_vol}

# Guidance discovery - all 5 in parallel (SKIP ALL if Q1 guidance cached)
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_8k_filings_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_10k_filings_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_10q_filings_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_transcript_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_guidance_news_range.py {TICKER} {START} {END}

Cache-aware execution: (based on Step 1b flags)

If Q1_NEWS_CACHED=true → skip get_significant_moves.py
If Q1_GUIDANCE_CACHED=true → skip all 5 guidance discovery scripts
If both cached → skip Steps 2, 3, 3b entirely for Q1

Parse news results:

get_significant_moves.py output columns: date|daily_stock|daily_macro|daily_adj
Parse: List of dates with significant moves

Parse guidance results (content-level sources): All 7 scripts return the SAME format: report_id|date|source_type|source_key

get_8k_filings_range.py → 8-K content (source_type: exhibit, section, filing_text)
get_10k_filings_range.py → 10-K content (source_type: exhibit, section, filing_text, financial_stmt, xbrl)
get_10q_filings_range.py → 10-Q content (source_type: exhibit, section, filing_text, financial_stmt, xbrl)
get_transcript_range.py → Transcripts (source_type: transcript, source_key: full)
get_guidance_news_range.py → News (source_type: news, source_key: full)

Combine all guidance sources into a single list. Each line is one content source to process. Parse uniformly - no special handling needed.

If OK|NO_MOVES returned: No significant moves for Q1 news, skip news tasks but still process guidance if sources found.

Step 3: Concurrent Analysis for Q1 (News + Guidance)

Phase 1: Create tasks upfront (only for non-cached tracks)

NEWS TASKS (with blockedBy dependencies) - SKIP if Q1_NEWS_CACHED=true: For EACH significant date from Step 2, create all 4 tasks with dependency chain:

Create BZ task via TaskCreate:
- subject: "BZ-{QUARTER} {TICKER} {DATE}" (e.g., "BZ-Q4_FY2022 NOG 2023-01-03")
- description: "pending"
- activeForm: "Analyzing {TICKER} {DATE}"
- Note the task ID as BZ_ID
Create WEB task via TaskCreate:
- subject: "WEB-{QUARTER} {TICKER} {DATE}"
- description: "{TICKER} {DATE} {DAILY_STOCK} {DAILY_ADJ}"
- activeForm: "Web research {TICKER} {DATE}"
- Then call TaskUpdate with addBlockedBy: ["{BZ_ID}"]
- Note the task ID as WEB_ID
Create PPX task via TaskCreate:
- subject: "PPX-{QUARTER} {TICKER} {DATE}"
- description: "{TICKER} {DATE} {DAILY_STOCK} {DAILY_ADJ}"
- activeForm: "Perplexity research {TICKER} {DATE}"
- Then call TaskUpdate with addBlockedBy: ["{WEB_ID}"]
- Note the task ID as PPX_ID
Create JUDGE task via TaskCreate:
- subject: "JUDGE-{QUARTER} {TICKER} {DATE}"
- description: "pending"
- activeForm: "Validating {TICKER} {DATE}"
- Then call TaskUpdate with addBlockedBy: ["{PPX_ID}"]
- Note the task ID as JUDGE_ID

GUIDANCE TASKS (NO dependencies - all run in parallel) - SKIP if Q1_GUIDANCE_CACHED=true:

For EACH content source line from guidance discovery (format: report_id|date|source_type|source_key), create a task:

subject: "GX-{QUARTER} {TICKER} {REPORT_ID} {SOURCE_TYPE}"
- Pattern: GX-{quarter} {ticker} {id} {type} where id format varies by source type
description: "{REPORT_ID}|{SOURCE_TYPE}|{SOURCE_KEY}" (store for agent prompt)
activeForm: "Extracting guidance from {SOURCE_TYPE}"

All 7 source types use the same format - no special handling needed for transcripts or news.

Phase 2: Spawn agents (only for non-cached tracks, all in parallel)

NEWS: Spawn BZ agents for each significant date - SKIP if Q1_NEWS_CACHED=true:

subagent_type: "news-driver-bz"
description: "BZ news {TICKER} {DATE}"
prompt: "{TICKER} {DATE} {DAILY_STOCK} {DAILY_ADJ} TASK_ID={BZ_ID} WEB_TASK_ID={WEB_ID} PPX_TASK_ID={PPX_ID} JUDGE_TASK_ID={JUDGE_ID} QUARTER={E1.fiscal_quarter}_FY{E1.fiscal_year}"

GUIDANCE: Spawn guidance-extract agents - SKIP if Q1_GUIDANCE_CACHED=true (one per content source, all in parallel):

For EACH guidance task, read the description to get {REPORT_ID}|{SOURCE_TYPE}|{SOURCE_KEY}, then spawn:

subagent_type: "guidance-extract"
description: "Guidance {TICKER} {SOURCE_TYPE}"
prompt: "{TICKER} {REPORT_ID} {SOURCE_TYPE} {SOURCE_KEY} {QUARTER} FYE={fye_month} TASK_ID={TASK_ID}"

Key variations in prompt format:

Filing sources: {TICKER} {accession} {source_type} {source_key} {QUARTER} FYE={fye_month} TASK_ID={id}
Transcript: {TICKER} {transcript_id} transcript full {QUARTER} FYE={fye_month} TASK_ID={id}
News: {TICKER} {news_id} news full {QUARTER} FYE={fye_month} TASK_ID={id}

The fye_month comes from E1/E2 data (get_earnings.py output). Pass the same value for all guidance tasks of that company.

IMPORTANT:

Create tasks only for non-cached tracks, THEN spawn agents for those tracks in parallel
If only news cached → create + spawn guidance agents only
If only guidance cached → create + spawn news agents only
BZ agents mark WEB+PPX as SKIPPED if they find answer (external_research=false)
Guidance agents have NO dependencies - they complete independently
DO NOT WAIT for any agents - proceed immediately to Phase 3

Phase 3: Concurrent escalation loop - SKIP if Q1_NEWS_CACHED=true

If Q1_NEWS_CACHED=false, immediately after spawning BZ agents, enter this loop:

WHILE any Q1 tasks (BZ-*, WEB-*, PPX-*, JUDGE-*) are pending or in_progress:
  1. Check TaskList for WEB-{QUARTER} {TICKER} tasks that are:
     - status = "pending" AND blockedBy is empty (auto-unblocked when BZ completed)
     - NOT already spawned
     → For each such WEB task:
       - Get task via TaskGet to read description: "{TICKER} {DATE} {DAILY_STOCK} {DAILY_ADJ}"
       - Extract QUARTER from task subject
       - Find corresponding PPX and JUDGE task IDs from TaskList
       - Spawn:
         subagent_type: "news-driver-web"
         prompt: "{TICKER} {DATE} {DAILY_STOCK} {DAILY_ADJ} TASK_ID={WEB_ID} PPX_TASK_ID={PPX_ID} JUDGE_TASK_ID={JUDGE_ID} QUARTER={QUARTER}"
     → WEB agents mark PPX as SKIPPED if confidence >= 50

  2. Check TaskList for PPX-{QUARTER} {TICKER} tasks that are:
     - status = "pending" AND blockedBy is empty (auto-unblocked when WEB completed)
     - NOT already spawned
     → For each such PPX task:
       - Get task via TaskGet to read description
       - Extract QUARTER from task subject
       - Find corresponding JUDGE task ID from TaskList
       - Spawn:
         subagent_type: "news-driver-ppx"
         prompt: "{TICKER} {DATE} {DAILY_STOCK} {DAILY_ADJ} TASK_ID={PPX_ID} JUDGE_TASK_ID={JUDGE_ID} QUARTER={QUARTER}"
     → PPX agents always update JUDGE with result (final tier)

  3. Check TaskList for JUDGE-{QUARTER} {TICKER} tasks that are:
     - status = "pending" AND blockedBy is empty (auto-unblocked when PPX completed or skipped)
     - NOT already spawned
     - description starts with "READY:" (has result to validate)
     → For each such JUDGE task:
       - Spawn:
         subagent_type: "news-driver-judge"
         prompt: "TASK_ID={JUDGE_ID}"
     → JUDGE agents validate and update task with final confidence

  4. Brief pause (2-3 seconds), then repeat
END WHILE

Track which task IDs you've already spawned agents for to avoid duplicates.

Note on SKIPPED tasks: When BZ or WEB finds a confident answer, they mark downstream tasks as "completed" with description="SKIPPED: {tier} found answer". This auto-unblocks the next task in chain (JUDGE for BZ skip, JUDGE for WEB skip).

Phase 4: Collect all results

NEWS RESULTS: When all Q1 news tasks (BZ-, WEB-, PPX-, JUDGE-) are completed, collect results from JUDGE-* tasks via TaskGet. Read the description field — it contains the validated 12-field pipe-delimited result line (with attr_confidence, pred_confidence, and judge_notes).

Note: Each date has exactly one JUDGE task with the final validated result. BZ/WEB/PPX tasks contain intermediate results.

GUIDANCE RESULTS: When all Q1 guidance tasks (GX-*) are completed, collect results via TaskGet. Read the description field — it contains pipe-delimited guidance entries (18 fields per line):

period_type|fiscal_year|fiscal_quarter|segment|metric|low|mid|high|unit|basis|derivation|qualitative|source_type|source_id|source_key|given_date|section|quote

Field definitions:

period_type: quarter, annual, half, or long-range
fiscal_year: e.g., 2025
fiscal_quarter: 1, 2, 3, 4, or . for annual
segment: Total (default), or specific segment like Services, iPhone, AWS
metric: Normalized name like Revenue, EPS, Gross Margin
low, mid, high: Numbers or . for qualitative guidance
unit: %, USD, B USD, % YoY, etc.
basis: GAAP, non-GAAP, as-reported, etc.
derivation: explicit, calculated (mid derived), point, or implied (qualitative only)
qualitative: Non-numeric guidance text (e.g., "double digits") or .
source_type, source_id, source_key: Source identification
given_date: When guidance was issued (YYYY-MM-DD)
section: Location in source (e.g., "CFO prepared remarks", "Outlook section")
quote: Exact text with pipes replaced by ¦

Or NO_GUIDANCE|{source_type}|{source_key} if no guidance found in that source.

Note: Guidance tasks have no dependencies and complete independently of news tasks.

Step 3b: Save Q1 Results

NEWS RESULTS:

Create directory if needed: earnings-analysis/Companies/{TICKER}/
Append Q1 results to earnings-analysis/Companies/{TICKER}/news.csv:
- Add quarter column with value {E1.fiscal_quarter}_FY{E1.fiscal_year} (e.g., Q1_FY2024)
- Format: quarter|date|news_id|driver|attr_confidence|pred_confidence|daily_stock|daily_adj|market_session|source|external_research|source_pub_date|judge_notes
- Create file with header if it doesn't exist
Update earnings-analysis/news_processed.csv:
- Format: ticker|quarter|fiscal_year|processed_date
- Append row: {TICKER}|{E1.fiscal_quarter}|FY{E1.fiscal_year}|{today YYYY-MM-DD}
- Create file with header if it doesn't exist

GUIDANCE RESULTS: 4. Append Q1 guidance to earnings-analysis/Companies/{TICKER}/guidance.csv:

Add quarter column with value {E1.fiscal_quarter}_FY{E1.fiscal_year}
Format (19 fields): quarter|period_type|fiscal_year|fiscal_quarter|segment|metric|low|mid|high|unit|basis|derivation|qualitative|source_type|source_id|source_key|given_date|section|quote
Skip lines that start with NO_GUIDANCE
Create file with header if it doesn't exist

Update earnings-analysis/guidance_processed.csv:
- Format: ticker|quarter|fiscal_year|processed_date
- Append row: {TICKER}|{E1.fiscal_quarter}|FY{E1.fiscal_year}|{today YYYY-MM-DD}
- Create file with header if it doesn't exist

Step 4: Concurrent Analysis for Q2 (News + Guidance)

Calculate:

START = E1 date + 1 day (exclude E1 earnings reaction)
END = E2 date (exclusive, excludes E2 earnings reaction)

Run discovery scripts in parallel (only for non-cached tracks):

# News discovery (SKIP if Q2 news cached)
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_significant_moves.py {TICKER} {START} {END} {E2.trailing_vol}

# Guidance discovery - all 5 in parallel (SKIP ALL if Q2 guidance cached)
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_8k_filings_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_10k_filings_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_10q_filings_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_transcript_range.py {TICKER} {START} {END}
source /home/faisal/EventMarketDB/venv/bin/activate && python /home/faisal/EventMarketDB/scripts/earnings/get_guidance_news_range.py {TICKER} {START} {END}

Cache-aware execution: (based on Step 1b flags)

If Q2_NEWS_CACHED=true → skip get_significant_moves.py
If Q2_GUIDANCE_CACHED=true → skip all 5 guidance discovery scripts
If both cached → skip Steps 4, 4b entirely for Q2

Then follow the same concurrent pattern as Step 3:

Phase 1: Create ALL tasks (news with blockedBy + guidance without dependencies)
Phase 2: Spawn BZ agents AND guidance agents in parallel
Phase 3: Concurrent escalation loop - spawn WEB/PPX/JUDGE as they auto-unblock
Phase 4: Collect all Q2 results (news from JUDGE-, guidance from GX-)

Use QUARTER={E2.fiscal_quarter}_FY{E2.fiscal_year} for all Q2 tasks.

Step 4b: Save Q2 Results

Same as Step 3b but for Q2:

NEWS RESULTS:

Append to earnings-analysis/Companies/{TICKER}/news.csv with quarter={E2.fiscal_quarter}_FY{E2.fiscal_year}
Append to news_processed.csv: {TICKER}|{E2.fiscal_quarter}|FY{E2.fiscal_year}|{today YYYY-MM-DD}

GUIDANCE RESULTS: 3. Append to earnings-analysis/Companies/{TICKER}/guidance.csv with quarter={E2.fiscal_quarter}_FY{E2.fiscal_year} 4. Append to guidance_processed.csv: {TICKER}|{E2.fiscal_quarter}|FY{E2.fiscal_year}|{today YYYY-MM-DD}

Step 5: Return Combined Results

=== EARNINGS ORCHESTRATOR: {TICKER} ===

--- EARNINGS DATA ---
E1: {accession} | {date} | FY{fiscal_year} {fiscal_quarter} | {daily_adj}% adj | vol={trailing_vol}% ({vol_days}d) {vol_status}
E2: {accession} | {date} | FY{fiscal_year} {fiscal_quarter} | {daily_adj}% adj | vol={trailing_vol}% ({vol_days}d) {vol_status}
...

--- Q1 NEWS ANALYSIS ({START} to {E1}) ---
Filter: |stock|>=4%, |adj|>=max(2×{trailing_vol}%,3%)
Significant dates: {count}

date|news_id|driver|attr_confidence|pred_confidence|daily_stock|daily_adj|market_session|source|external_research|source_pub_date|judge_notes
...

--- Q1 GUIDANCE EXTRACTION ---
Content sources processed: {total_sources}
  - exhibits: {exhibit_count}
  - sections: {section_count}
  - financial_stmt: {financial_stmt_count}
  - xbrl: {xbrl_count}
  - transcripts: {transcript_count}
  - news: {news_count}
Guidance entries found: {guidance_count}

quarter|period_type|fiscal_year|fiscal_quarter|segment|metric|low|mid|high|unit|basis|derivation|qualitative|source_type|source_id|source_key|given_date|section|quote
...

--- Q2 NEWS ANALYSIS ({E1} to {E2}) ---
Filter: |stock|>=4%, |adj|>=max(2×{trailing_vol}%,3%)
Significant dates: {count}

date|news_id|driver|attr_confidence|pred_confidence|daily_stock|daily_adj|market_session|source|external_research|source_pub_date|judge_notes
...

--- Q2 GUIDANCE EXTRACTION ---
Content sources processed: {total_sources}
  - exhibits: {exhibit_count}
  - sections: {section_count}
  - financial_stmt: {financial_stmt_count}
  - xbrl: {xbrl_count}
  - transcripts: {transcript_count}
  - news: {news_count}
Guidance entries found: {guidance_count}

quarter|period_type|fiscal_year|fiscal_quarter|segment|metric|low|mid|high|unit|basis|derivation|qualitative|source_type|source_id|source_key|given_date|section|quote
...

--- NEWS SUMMARY ---
Total dates analyzed: {N}
Explained by Benzinga: {B}
Explained by WebSearch: {W}
Explained by Perplexity: {P}
Still unknown (confidence=0): {U}
Validated by Judge: {J}

--- GUIDANCE SUMMARY ---
Total content sources processed: {total_sources}
  - exhibits: {exhibit_count}
  - sections: {section_count}
  - financial_stmt: {financial_stmt_count}
  - xbrl: {xbrl_count}
  - transcripts: {transcript_count}
  - news: {news_count}
Total guidance entries found: {total_guidance}

=== COMPLETE ===

Step 6: Signal Completion (Auto-Triggers Thinking Build)

echo "=== ORCHESTRATOR_COMPLETE {TICKER} $(date '+%Y-%m-%d %H:%M:%S') ==="

This command triggers the PostToolUse hook which automatically runs:

build-news-thinking.py --ticker {TICKER}
build-guidance-thinking.py --ticker {TICKER}

Thinking files appear in Obsidian at Companies/{TICKER}/thinking/{QUARTER}/ without explicit script calls.

Rules

Full row replacement - When a later tier returns a result, use its COMPLETE output. PPX replaces WEB, WEB replaces BZ. Never mix fields across tiers. Judge outputs 12-field line.
Always run get_earnings.py first - provides trailing_vol for each quarter
Skip if done - check news_processed.csv, skip quarters already processed
All sub-agents in parallel - spawn one per date, no cap
Q1 complete before Q2 - finish all 4 tiers (BZ → WEB → PPX → JUDGE) + save for Q1, then Q2
Extract date only - E1 date "2024-02-01T16:30:33-05:00" → use "2024-02-01"
Preserve news_id EXACTLY - Copy URLs verbatim. NEVER shorten, summarize, or create short IDs. If sub-agent returns a URL, save the full URL exactly as returned.
Pass through raw output - don't summarize or lose data
Always save results - append to news.csv and mark done in news_processed.csv

Error Handling

Script errors return structured format: ERROR|CODE|MESSAGE|HINT

If any script returns ERROR:

Log the error in output
Try to continue with remaining steps if possible
Report all errors in summary

Example

Input: AAPL

Flow:

get_earnings.py AAPL → E1=2024-02-01 (Q1_FY2024, vol=0.90), E2=2024-05-02 (Q2_FY2024, vol=0.99)
Check news_processed.csv → no row for AAPL|Q1|FY2024 → process Q1
get_significant_moves.py AAPL 2023-11-01 2024-02-01 0.90 → internally: |stock|>=4%, |adj|>=max(2×0.90,3)=3%
Spawn news-driver-bz for each significant date → some explained (create JUDGE), some create WEB tasks
Spawn news-driver-web for each WEB task → some explained (create JUDGE), some create PPX tasks
Spawn news-driver-ppx for each PPX task → all create JUDGE tasks
Spawn news-driver-judge for each JUDGE task → validates and returns final confidence
Save Q1 to Companies/AAPL/news.csv (from JUDGE results), mark Q1_FY2024 done
Check news_processed.csv → row exists for AAPL|Q2|FY2024 → skip Q2
Return results (Q1 only, Q2 was cached)

Earnings Orchestrator

File contents

Earnings Orchestrator

Input

Task - MUST COMPLETE ALL STEPS

Step 0: Record Start Time

Step 1: Get Earnings Data

Step 1b: Check Processing Cache

Step 2: Discovery for Q1 (News + Guidance in parallel)

Step 3: Concurrent Analysis for Q1 (News + Guidance)

Step 3b: Save Q1 Results

Step 4: Concurrent Analysis for Q2 (News + Guidance)

Step 4b: Save Q2 Results

Step 5: Return Combined Results

Step 6: Signal Completion (Auto-Triggers Thinking Build)

Rules

Error Handling

Example

Frequently asked questions

Earnings Orchestrator

File contents

Earnings Orchestrator

Input

Task - MUST COMPLETE ALL STEPS

Step 0: Record Start Time

Step 1: Get Earnings Data

Step 1b: Check Processing Cache

Step 2: Discovery for Q1 (News + Guidance in parallel)

Step 3: Concurrent Analysis for Q1 (News + Guidance)

Step 3b: Save Q1 Results

Step 4: Concurrent Analysis for Q2 (News + Guidance)

Step 4b: Save Q2 Results

Step 5: Return Combined Results

Step 6: Signal Completion (Auto-Triggers Thinking Build)

Rules

Error Handling

Example

Related skills

Frequently asked questions

How do I install the Earnings Orchestrator skill?

What does the Earnings Orchestrator skill do?

Is Earnings Orchestrator safe to use?

Which AI agents work with Earnings Orchestrator?

Is Earnings Orchestrator free to use?

Who published Earnings Orchestrator?