{"id":25078,"plugin_id":"plugin_asdk_app_69491eceef3c8191beb70788b7840429","kind":"skill","collection_source":null,"comparison_source":null,"observed_at":"2026-09-30T23:19:11.700Z","digest":"5e660b0d4bccb5c0925befbdb92e987af565a5439c8a8cb13e669aa037966293","against":5579,"payload":{"name":"bigdata-earnings-reaction","description":"Write a tight post-earnings reaction note using Bigdata.com data — headline numbers versus consensus with beat/miss magnitude, what mattered on both sides, a prior-versus-new guidance table, an explicit thesis check (Intact / Strengthened / Weakened / Broken) with evidence, the estimate and price-target revisions the print forces, a pre-versus-post valuation update, quality signals for the quarter, and an action with the next key date. Shorter and more decision-focused than a full earnings digest. Triggers: \"earnings reaction for X\", \"how should I react to X's results\", \"does X's quarter change the thesis\", \"X print reaction\", \"revise my numbers after X earnings\", \"was X's quarter good enough\".","included_files":[{"relative_path":"README.md","size_in_bytes":1549},{"relative_path":"agents/openai.yaml","size_in_bytes":341},{"relative_path":"assets/bigdata-icon-black.svg","size_in_bytes":3278},{"relative_path":"assets/report-template.md","size_in_bytes":3307},{"relative_path":"references/quality-of-earnings.md","size_in_bytes":8126},{"relative_path":"scripts/scenario_probability.py","size_in_bytes":18087}],"skill_md_contents":"---\nname: bigdata-earnings-reaction\ndescription: >\n  Write a tight post-earnings reaction note using Bigdata.com data — headline numbers versus\n  consensus with beat/miss magnitude, what mattered on both sides, a prior-versus-new guidance\n  table, an explicit thesis check (Intact / Strengthened / Weakened / Broken) with evidence,\n  the estimate and price-target revisions the print forces, a pre-versus-post valuation update,\n  quality signals for the quarter, and an action with the next key date. Shorter and more\n  decision-focused than a full earnings digest. Triggers: \"earnings reaction for X\", \"how\n  should I react to X's results\", \"does X's quarter change the thesis\", \"X print reaction\",\n  \"revise my numbers after X earnings\", \"was X's quarter good enough\".\n---\n\n# Bigdata Earnings Reaction\n\nThe decision note after a print: what changed, what to revise, what to do. Use Bigdata.com plugin tools for every fact.\n\n**Use this skill when** results are out and the user needs a fast, thesis-anchored call. Not this skill when:\n\n| Request | Use instead |\n|---------|-------------|\n| Full breakdown of the quarter — segments, cash flow, guidance detail | Earnings digest |\n| Analysis ahead of the print | Earnings preview |\n| A view with no earnings event | Quick take |\n| Full thesis rebuild | Investment memo |\n\n**Digest vs reaction:** the digest explains the quarter; the reaction decides what to do about it. If the user has a position or a stated thesis, this is the right skill.\n\n## Data foundation (plugin tools)\n\n| Tool | Purpose | Prerequisite |\n|------|---------|--------------|\n| `find_securities` | Resolve company name → RavenPack `entity_id` | None |\n| `bigdata_company_tearsheet` | Reported numbers, consensus, surprise, multiples pre/post | `find_securities` |\n| `bigdata_events_calendar` | Confirm the report date and the next key date | `find_securities` |\n| `bigdata_search` | Release, transcript, guidance, analyst reactions | None |\n\nIf the company name is ambiguous after `find_securities`, ask:\n\n> \"I found multiple companies named [X]. Did you mean [Company A] in [Industry] or [Company B] in [Industry]?\"\n\n## Workflow\n\n### Step 1 — Anchor the print\n\nResolve the entity, confirm the reported date, and pull the tearsheet for actuals versus consensus, the surprise, and multiples before and after the move.\n\n### Step 2 — Ask for the thesis (or infer both sides)\n\nIf the user has stated a thesis, use it — the thesis check is the centerpiece of this note. If not, construct the prevailing bull and bear cases from the tearsheet and search, and check the quarter against **both**.\n\n### Step 3 — Headline numbers\n\nRevenue, EPS, and the key sector KPI: reported, consensus, beat/miss in % or bps, and YoY change. Quantify — \"solid quarter\" is not a number.\n\n### Step 4 — What mattered\n\nPositive and negative surprises, each with its magnitude and whether it is **sustainable or one-time** (revenue volume or price vs buyback, tax, timing). Run 3–5 searches:\n\n- \"[Company] earnings results Q[X] [Fiscal Year]\"\n- \"[Company] earnings call transcript guidance commentary\"\n- \"[Company] analyst reactions price target changes\"\n- \"[Company] earnings surprise beat miss reaction\"\n\n### Step 5 — Guidance update\n\nPrior guidance versus new guidance versus consensus, per metric. Guidance usually moves the stock more than the reported quarter — treat it as first-order.\n\n### Step 6 — Thesis check\n\nState the status explicitly: **Intact / Strengthened / Weakened / Broken**. Back it with the specific data points from the quarter that support the call — not a general impression.\n\n### Step 7 — Revisions and valuation\n\nWhat the print forces you to change: FY revenue, FY EPS, price target. Then the valuation update — stock price, NTM P/E, NTM EV/EBITDA, pre- versus post-earnings. Does the move fit the news?\n\n### Step 8 — Quality signals\n\nOCF vs net income, DSO trend, inventory build, and guidance credibility (met/beat versus missed). A beat on declining quality is a different result than a beat on improving quality. Depth: [references/quality-of-earnings.md](./references/quality-of-earnings.md).\n\n### Step 9 — Action\n\nGive the action and the rationale in one or two sentences, plus the next key date. If scenarios need re-weighting, run [scripts/scenario_probability.py](./scripts/scenario_probability.py) only when the user asks for scripted math.\n\n## Output\n\nFollow [assets/report-template.md](./assets/report-template.md) exactly — section order, tables, sources, and footer.\n\n- Add inline citations `[1]`, `[2]` immediately after claims, hyperlinked to the document URL.\n- Every deliverable ends with the **Powered by Bigdata.com** line and the **Disclaimer**, verbatim.\n- Default format is Markdown; offer a Word (.docx) version if useful.\n\n## Quality bar\n\nNon-negotiables:\n\n- Thesis status stated as one of the four words, with evidence — the whole note turns on this\n- Beat/miss quantified and labelled sustainable vs one-time\n- Guidance treated as first-order, with prior versus new side by side\n- Estimate and price-target revisions named explicitly, not left implied\n- An action given with a next key date\n- Facts separated from analysis and implications\n"},"changes":[{"path":"/included_files","type":"changed","before":[{"relative_path":"assets/bigdata-icon-black.svg","size_in_bytes":3278},{"relative_path":"assets/report-template.md","size_in_bytes":3307},{"relative_path":"references/quality-of-earnings.md","size_in_bytes":8126},{"relative_path":"scripts/scenario_probability.py","size_in_bytes":18087}],"after":[{"relative_path":"README.md","size_in_bytes":1549},{"relative_path":"agents/openai.yaml","size_in_bytes":341},{"relative_path":"assets/bigdata-icon-black.svg","size_in_bytes":3278},{"relative_path":"assets/report-template.md","size_in_bytes":3307},{"relative_path":"references/quality-of-earnings.md","size_in_bytes":8126},{"relative_path":"scripts/scenario_probability.py","size_in_bytes":18087}]}],"summary":"Fields changed: 1. /included_files.","summary_kind":"deterministic","summary_metadata":{}}