News intel

News Intel

Gather ranked news intelligence for: $ARGUMENTS

Overview

• Implementation status: code-backed
• Local entry script: <bundle-root>/news-intel/run.py
• Primary purpose: turn provided headline evidence into a ranked catalyst, risk, and narrative block for downstream research use
• Research layer: event-driven context (Stage 2: Data Collection & Quality Assurance, Stage 4: Feature Engineering & Signal Construction - Alternative data subset)
• Workflow stages: stage 2 Data Collection & Quality Assurance and stage 4 Feature Engineering & Signal Construction for event-oriented context
• Local executor guarantee: parse manual or file-backed headlines, rank them, tag catalysts and risks, and emit a compact report plus query-plan scaffolding

Use When

• The user wants symbol-relevant catalyst and risk context.
• The caller already has headlines and wants structured ranking rather than raw browsing.
• The user wants a compact narrative block before analysis, stock-data, or strategy-chat.
• The user wants to understand how news events may impact technical or fundamental analysis.
• The user wants to identify potential catalysts for thesis formation or invalidation triggers.

Do Not Use When

• The user expects this skill to crawl the web on its own. The current local executor ranks provided headlines rather than performing broad autonomous search.
• The user wants a full one-symbol report. Use analysis or market-brief.
• The user wants historical artifact review rather than fresh headline ingestion. Use reports or analysis-history.
• The user wants real-time news monitoring or alerting. This skill processes provided headlines, not live feeds.

Inputs

• Normal case: one stock symbol.
• Optional --headline "title | summary" repeated multiple times.
• Optional --headline-file PATH for local text, JSON, or JSONL headline input.
• Optional --company-name, --alias, --max-items.
• If symbol is omitted, the skill may reuse last_symbol from the same execution context.
• Evidence contract:
  • headline items are caller-supplied or file-supplied evidence
  • source quality, timeliness, and completeness are not guaranteed by the local executor
  • if no headlines are provided, the skill returns a minimal query-plan-oriented scaffold rather than pretending current news coverage exists

Execution

Step 1: Define news intelligence requirements

Before processing headlines, clarify the intelligence needs:

News intelligence objectives:

• Identify potential catalysts (positive events that could drive price up)
• Identify potential risks (negative events that could drive price down)
• Extract narrative themes (market sentiment, sector trends, company positioning)
• Assess event materiality (which events are likely to impact price)
• Determine event timing (when events occurred, when they may impact price)
• Evaluate information quality (source credibility, freshness, completeness)

Event categories to identify:

• Earnings and financial: Results, guidance, analyst ratings, forecasts
• Corporate actions: M&A, restructuring, capital raising, dividends, buybacks
• Regulatory and policy: Approvals, investigations, policy changes, compliance
• Product and business: New products, contracts, partnerships, market expansion
• Management and governance: Leadership changes, strategy shifts, governance issues
• Market and sector: Industry trends, competitive dynamics, macro factors
• Technical and trading: Price moves, volume spikes, institutional activity

A-share specific events:

• Regulatory: CSRC approvals, delisting risk, ST/*ST designation changes
• Policy: Industry policy changes, subsidy programs, environmental regulations
• State ownership: SOE reforms, state asset transfers, mixed ownership reforms
• Capital markets: IPO/refinancing approvals, index inclusion/exclusion, Stock Connect changes
• Corporate governance: Shareholder disputes, related-party transactions, pledge releases

Step 2: Collect and validate headline evidence

Process provided headlines with quality assessment:

Headline collection:

• Accept manual headlines via --headline flag
• Accept headline file via --headline-file flag
• Parse headline format: "title | summary" or structured JSON/JSONL
• Extract metadata: timestamp, source, relevance score (if provided)

Headline validation:

• Timestamp present and parseable (if available)
• Source identified (official, media, social, user-supplied)
• Title and summary distinguishable
• Symbol relevance (mentions company name, ticker, or aliases)
• Language consistency (Chinese for A-share news)
• Duplicate detection (same event reported multiple times)

Evidence quality assessment:

• Freshness: How old is the headline?
  • Fresh: < 1 day old
  • Recent: 1-7 days old
  • Stale: > 7 days old
  • Undated: Timestamp missing (treat as low quality)
• Source credibility:
  • Official: Company announcements, exchange filings (highest credibility)
  • Established media: Major financial news outlets (high credibility)
  • Social media: Weibo, forums, blogs (medium credibility)
  • User-supplied: Unknown provenance (low credibility)
• Completeness:
  • Complete: Title, summary, timestamp, source all present
  • Partial: Missing some metadata
  • Minimal: Only title present
• Duplication:
  • Unique: First report of this event
  • Duplicate: Same event as another headline
  • Update: New information on previously reported event

Step 3: Rank and categorize headlines

Apply systematic ranking and categorization:

Relevance scoring (0-100):

• Direct mention (80-100): Company name or ticker in title
• Indirect mention (50-79): Company in summary, or sector/industry mention
• Tangential (20-49): Related company, supply chain, or macro factor
• Irrelevant (0-19): No clear connection to symbol

Materiality scoring (0-100):

• High materiality (80-100): Likely to significantly impact price
  • Earnings surprise, major M&A, regulatory approval/rejection
  • Management change, fraud allegation, delisting risk
• Medium materiality (50-79): May impact price moderately
  • Analyst rating change, contract win/loss, product launch
  • Industry policy change, competitor news
• Low materiality (20-49): Minor impact expected
  • Routine announcements, minor partnerships, general commentary
• Negligible (0-19): Unlikely to impact price
  • Historical information, general industry trends

Sentiment classification:

• Bullish: Positive catalyst, likely to drive price up
• Bearish: Negative risk, likely to drive price down
• Neutral: Informational, no clear directional impact
• Mixed: Contains both positive and negative elements

Event timing classification:

• Immediate: Event just occurred, market may not have fully reacted
• Recent: Event occurred 1-7 days ago, likely already priced in
• Historical: Event occurred > 7 days ago, definitely priced in
• Forward-looking: Event expected in future (guidance, scheduled events)

Confidence scoring (0-100):

• High confidence (80-100): Official source, complete information, clear impact
• Medium confidence (50-79): Established media, mostly complete, probable impact
• Low confidence (20-49): Unverified source, incomplete information, unclear impact
• Speculative (0-19): Rumor, no source, highly uncertain

Step 4: Extract catalysts and risks

Identify specific catalysts and risks from ranked headlines:

Catalyst extraction: For each bullish headline with materiality > 50:

• Catalyst type: Earnings beat, M&A, approval, contract, etc.
• Catalyst description: Brief summary of the positive event
• Expected impact: How this could drive price up
• Timing: When impact may materialize
• Confidence: How certain is this catalyst
• Evidence: Which headline(s) support this catalyst

Risk extraction: For each bearish headline with materiality > 50:

• Risk type: Earnings miss, investigation, competition, regulation, etc.
• Risk description: Brief summary of the negative event
• Expected impact: How this could drive price down
• Timing: When impact may materialize
• Confidence: How certain is this risk
• Evidence: Which headline(s) support this risk

Narrative theme extraction: Identify recurring themes across headlines:

• Growth narrative: Expansion, market share gains, new products
• Quality narrative: Margin improvement, efficiency gains, competitive moats
• Value narrative: Undervaluation, asset sales, shareholder returns
• Risk narrative: Regulatory pressure, competition, macro headwinds
• Sentiment narrative: Market enthusiasm, skepticism, controversy

Step 5: Assess evidence quality and gaps

Evaluate the overall quality of news intelligence:

Evidence quality summary:

• Total headlines processed
• Freshness distribution (fresh/recent/stale/undated)
• Source distribution (official/media/social/user-supplied)
• Completeness distribution (complete/partial/minimal)
• Duplication rate (% of headlines that are duplicates)

Evidence gaps:

• Missing event types: Which important event categories have no coverage?
• One-sided coverage: Are headlines predominantly bullish or bearish?
• Stale information: Is most information > 7 days old?
• Low credibility: Are most headlines from unverified sources?
• Sparse coverage: Are there too few headlines to draw conclusions?

Quality flags:

• Insufficient evidence: < 3 relevant headlines (cannot draw strong conclusions)
• Stale evidence: > 50% of headlines > 7 days old (may be outdated)
• Low credibility: > 50% of headlines from unverified sources (unreliable)
• One-sided: > 80% bullish or bearish (missing counterarguments)
• Duplicate-heavy: > 30% duplicates (less information than it appears)

Step 6: Synthesize news intelligence report

Organize findings into structured report:

Part 1: Intelligence Summary

• Symbol and company name
• Total headlines processed
• Date range of headlines
• Overall sentiment (bullish/bearish/neutral/mixed)
• Evidence quality score (0-100)

Part 2: Key Catalysts For each catalyst (ranked by materiality × confidence):

• Catalyst type and description
• Expected impact and timing
• Confidence level
• Supporting evidence (headline titles)

Part 3: Key Risks For each risk (ranked by materiality × confidence):

• Risk type and description
• Expected impact and timing
• Confidence level
• Supporting evidence (headline titles)

Part 4: Narrative Themes

• Dominant narratives across headlines
• Sentiment trends (improving/deteriorating/stable)
• Market positioning (how company is perceived)
• Sector context (industry trends affecting company)

Part 5: Ranked Headlines Top 10-20 headlines by relevance × materiality:

• Title and summary
• Timestamp and source
• Relevance, materiality, sentiment scores
• Confidence level

Part 6: Evidence Quality Assessment

• Freshness summary
• Source credibility summary
• Completeness summary
• Duplication summary
• Quality flags (if any)

Part 7: Evidence Gaps and Limitations

• Missing event types
• One-sided coverage concerns
• Stale information concerns
• Low credibility concerns
• Sparse coverage concerns
• Recommended additional research

Part 8: Query Plan (Optional) If evidence is insufficient, suggest queries for additional research:

• Specific events to investigate
• Sources to check (exchange filings, company website, etc.)
• Time periods to focus on
• Related companies or sectors to monitor

Step 7: Run the local executor

python3 <bundle-root>/news-intel/run.py <symbol> [--headline "标题 | 摘要"] [--headline-file PATH]

Step 8: Deliver bounded intelligence block

When presenting results, maintain strict boundaries:

Evidence provenance:

• State that headlines are user-supplied or file-supplied evidence
• Identify source for each major catalyst or risk claim
• Distinguish official sources from media reports from speculation

Ranking transparency:

• Explain that scores are heuristic rankings, not truth scores
• State that relevance/materiality/confidence are model outputs, not guarantees
• Acknowledge that ranking cannot assess information not provided

Freshness and duplication:

• Highlight when most relevant items are stale (> 7 days old)
• Flag when multiple headlines report the same event
• Note when evidence is sparse or one-sided

Inference boundaries:

• Separate directly supplied evidence from derived insights
• Label narrative themes as interpretive synthesis, not facts
• Qualify catalyst/risk claims based on evidence quality

Integration with analysis:

• Catalysts can support thesis formation in analysis skill
• Risks can inform disconfirming evidence in analysis skill
• Narrative themes can inform variant view in analysis skill
• Evidence gaps should narrow claim strength in downstream memos

Output Contract

• Minimum local executor output: human-readable text beginning with # <symbol> News Intel.
• Possible sections: Catalysts, Risks, Narratives, Ranked Items, and Query Plan.
• Side effects: updates session memory for the current execution context.
• Caller-facing delivery standard:
  • Eight-part structure: Intelligence summary, key catalysts, key risks, narrative themes, ranked headlines, evidence quality, evidence gaps, query plan (if needed)
  • Evidence provenance: Label headline basis as supplied evidence, not independently verified truth
  • Ranking transparency: Treat confidence and relevance scores as heuristic ranking outputs, not completeness or truth scores
  • Freshness disclosure: Surface freshness and duplication profile when it materially affects conclusions
  • Quality assessment: Provide evidence quality score and flag quality concerns
  • Inference boundaries: Separate supplied evidence, ranking output, and narrative interpretation
  • Narrow claims when evidence is thin: If evidence is sparse, stale, or one-sided, qualify conclusions accordingly
  • Integration guidance: Explain how catalysts/risks feed into thesis formation, disconfirming evidence, and invalidation triggers
  • No fabricated certainty: Incomplete evidence should degrade conclusion strength, not be hidden
• Non-guarantees:
  • No web crawling guarantee (processes provided headlines only)
  • No durable report artifact unless another skill persists the result
  • No real-time monitoring or alerting capability

Failure Handling

• Parse and argument errors: non-zero exit with a readable 命令错误 message.
• Missing or unreadable headline files: readable 执行失败: text.
• Missing headlines: returns a reduced report with query plan suggestions.
• If evidence is incomplete, that should degrade the conclusion rather than cause fabricated certainty.
• Malformed headline format: skip malformed entries and report count of skipped items.
• Duplicate detection failures: proceed with ranking but flag potential duplication issues.

Key Rules

• Distinguish provided evidence from inferred narrative.
• Treat ranking as deterministic scoring over supplied items, not as a guarantee of external completeness.
• Prefer concise, source-labeled headline payloads over noisy dumps.
• When this skill feeds analysis, preserve provenance for each major catalyst or risk claim.
• Evidence quality assessment is mandatory. Always evaluate freshness, source credibility, completeness, duplication.
• Quality flags must be surfaced. If evidence is insufficient, stale, low-credibility, one-sided, or duplicate-heavy, state it explicitly.
• Confidence scores must reflect evidence quality. Low-quality evidence = low confidence, regardless of headline content.
• Narrative themes are interpretive synthesis. Label them as such, not as facts.
• Catalyst and risk claims must be qualified. State expected impact, timing, and confidence level.
• Evidence gaps must be acknowledged. Missing event types, one-sided coverage, sparse information.
• Integration with downstream skills must be explicit. How do catalysts/risks inform thesis, disconfirming evidence, invalidation?
• No web crawling or autonomous search. This skill processes provided headlines only.

Composition

• Often feeds analysis, stock-data, or strategy-chat.
• Shares research helpers with the broader packet-building logic, but it is not a substitute for full research verification.
• Catalysts feed into thesis formation and key drivers in analysis skill.
• Risks feed into disconfirming evidence and invalidation conditions in analysis skill.
• Narrative themes feed into variant view and market positioning in analysis skill.
• Evidence gaps inform evidence sufficiency assessment in analysis skill.
• Can be combined with fundamental-context for comprehensive non-price context.
• Should be rerun when new material events occur to update catalyst/risk assessment.