targeted-tech-research

Performs in-depth, vendor-specific technical research on solutions/products with standardized, fact-verified breakdowns of hardware, software, co-design, and...

定向技术方案深度拆解调研 Skill

This Skill performs comprehensive deep technical research on vendor-specific technical solutions/products, thoroughly solving the problems of AI generating shallow webpage summaries and regurgitating marketing language.

I. Core Principles (Harness Engineering Constraints)

• Scope Locking: All outputs must strictly correspond to "Vendor Full Name + Solution Full Model + Application Scenario". Generalized industry common knowledge is prohibited.
• Source Attribution: 100% distinction between "Publicly Verifiable Fact" and "Technically Derived Content". Annotate at line endings: [Public: Source] or [Derived].
• Granularity Compliance: Technical breakdowns must be precise down to "Module - Principle - Function - Interaction Logic".
• No Marketing Fluff: Prohibits copying vendor promotional language. Only structured principle breakdowns and objective analysis.
• Logical Consistency: Outputs across steps must be fully consistent and correspond with each other.
• Comprehensibility-Driven: Perform limited deep-diving for missing explanations of core principles, bounded by user-specified granularity and topic scope.

II. Pre-Flight Preparation: Interactive Onboarding Questionnaire

Before starting research, confirm the following information through dialogue. Users may answer "skip" to use defaults, or use the quick-start command to bypass the questionnaire.

1. Research Target Precision (Required): Vendor full official name, technical solution/product full model, core deployment scenario.
2. Research Priority: Hardware / Software / Hardware-Software Co-Design / Technical Features, ordered by importance. (Default: Hardware > Co-Design > Software > Technical Features)
3. Breakdown Granularity: Entry-level (module functions only) / Advanced (module working principles) / Extreme (component selection/algorithm logic). (Default: Advanced)
4. Terminology Explanation Preference:
   • A. Zero Explanation (assumes reader domain knowledge)
   • B. Minimal Contextual Explanation (≤15-word contextual note for first occurrence of non-generic terms)
   • C. Full Definition (2-3 sentence definition plus relevance to this solution) (Default: B)
5. Provided Sources (Optional): User may provide patent numbers, whitepaper links, paper DOIs, etc., to be prioritized as primary sources.

Quick-Start Mode

Users can directly send a command in the following format to skip the questionnaire and use all defaults:

text

research [Vendor Full Name] [Solution Full Model] [Application Scenario] --quick

Example: research Framatome "Reactor Pressure Vessel Bolt Tensioning Robot System" "Nuclear Refueling Outage" --quick

The Skill will then proceed directly to Step 0 Recon with default configurations.

Pre-Flight Hook Response Handling

After invoking hooks/pre_flight_check.py, determine next actions based on the returned JSON:

• If status is "passed": Proceed directly to Step 0 Recon.
• If status is "incomplete":
  • Use the returned prompt_for_user field to politely ask the user for missing information. Wait for user response.
  • Once user provides missing details, re-invoke the Skill with the complete information for re-validation.

III. Data Acquisition Strategy & Tool Selection Rules

This Skill does not directly execute network requests. All scraping tasks are delegated to sub-Skills and automatically selected according to the following rules.

3.1 Prioritize web-scraper (Lightweight Static Scraping)

Applicable for static HTML pages, RSS/Atom feeds, plain-text API responses.

3.2 Conditions to Trigger playwright-scraper (Any Match Triggers)

• Target URL matches any rule defined in references/dynamic_sites_whitelist.json.
• web-scraper returns content length < 200 characters, and contains keywords like loading, JavaScript, enable, please enable JavaScript.
• User instruction explicitly includes interaction verbs (e.g., "click", "switch tab", "scroll down").
• HTTP 403/406 is returned and response body contains no valid business data.

3.3 Low-Value Scenarios Prohibited from Triggering playwright-scraper (Blacklist)

• Comment sections or "related articles" widgets on news/blogs (always ignored).
• Pages requiring login or behind paywalls (immediately abandoned and marked as inaccessible).
• Purely visual showcase pages (3D showrooms, panoramas, interactive animations).
• PDF online preview pages (should extract direct PDF link and call pdf-reader or download directly).

3.4 Failure Handling & Degradation

• If playwright-scraper times out (>30s), abandon the URL and continue with available data.
• If 3 consecutive URLs time out, terminate the current scraping round and note the obstruction in the report.
• On scraping failure, do not block subsequent steps. Instead, insert a marker in the report at the corresponding position: [Info Missing: Manual extraction needed from [Source]] and aggregate all gaps at the end of the report.

IV. 5-Step Progressive Research Workflow

Step 0: Reconnaissance & Feasibility Assessment

Goal: Quickly assess the volume and usability of public information, generate a Research Feasibility Brief, and wait for user confirmation before proceeding to deep-dive steps.

1. Call web-scraper to search for [Vendor Full Name] [Solution Model] whitepaper and patent, obtaining titles, URLs, and snippets.
2. If PDF links exist, attempt to extract direct links; if PDF preview page, abandon and note.
3. If the source is a patent detail page or tech doc site (whitelisted), directly call playwright-scraper to extract key text.
4. Content Cleaning: Before feeding scraped text to LLM, invoke the cleaning script:

   bash

   python scripts/compress_content.py --max-length 3000 < raw_text.txt > cleaned_text.txt
   

5. Generate Research Feasibility Brief containing:
   • Estimated volume of obtainable public information (High/Medium/Low)
   • List of key information sources
   • Recommended breakdown granularity (dynamically based on info volume)
   • Prompt asking user whether to continue with deep-dive steps

After user confirmation, proceed to Steps 1-5.

Step 1: Overall Architecture Anchoring & Information Boundary Mapping

1. Context Preparation: Use the cleaned text from Step 0 as core context. If user provided private PDFs/patents, also process via scripts/compress_content.py and merge.
2. The Skill invokes internal LLM, strictly following the Step 1 Prompt template in references/prompts.md.
3. Output must include: Solution core positioning, layered architecture, public information boundary annotation, suggested priorities for subsequent research.
4. Annotate each module with source: [Public: URL/Patent#] or [Derived] or [Info Missing].

Step 2: Full-Dimensional Deep-Dive on Hardware System

1. If Step 1 reveals insufficient public info for specific hardware modules:
   • Call web-scraper for supplementary searches using module names (e.g., "controller", "sensor").
   • If target is a whitelisted dynamic page, call playwright-scraper.
2. Generate initial hardware breakdown report following Step 2 Prompt template in references/prompts.md.
3. Comprehensibility-Driven Deep-Dive Check:
   • From the user's specified granularity perspective, check for logical gaps or unexplained core principles in the draft.
   • Trigger limited supplementary research (max 2 search rounds, strictly within topic scope) for:
     • Core function implementation principles (how hardware achieves its role).
     • Scenario-linked component selection rationale (why this specific part).
   • If still unclear after supplementary research, annotate [Derived: based on similar solutions] or [Manual supplement needed: background knowledge on this tech point].
4. On scraping failure, insert [Info Missing] marker and log to gap list.

Step 3: Full-Dimensional Deep-Dive on Software System

1. Similar to Step 2, perform supplementary scraping for software layers.
2. Generate initial software breakdown following Step 3 Prompt template.
3. Comprehensibility-Driven Deep-Dive Check:
   • Focus on core algorithm principles: are inputs/outputs/core steps clear?
   • Supplementary research depth capped at "block diagram level" or "pseudocode logic level"; no deep mathematical derivations (unless user requested Extreme granularity).
   • If unclear, annotate [Derived].

Step 4: Hardware-Software Co-Design Full-Link Closed-Loop Principles

1. Based on prior hardware and software breakdowns, generate initial co-design analysis.
2. Follow Step 4 Prompt template, breaking down normal and abnormal operating conditions by time steps.
3. Comprehensibility-Driven Deep-Dive Check:
   • Check for logical gaps in timing descriptions (e.g., data sent but no receiver processing described).
   • Only supplement mainline flow gaps; do not expand all exception branches.
   • If gap info is completely missing, mark [Info Missing].

Step 5: Technical Features & Industry Benchmarking (with Credibility Scorecard)

1. Extract core technical features, barriers, quantitative performance benchmarks, and deployment suitability.
2. Follow Step 5 Prompt template.
3. Concurrently generate Credibility Scorecard (embedded in Step 5 output):
   • Based on preceding content, tally the ratio of facts/derivations/gaps per chapter.
   • Present as a Markdown table with star ratings (⭐) per chapter.
   • If table formatting fails, silently omit this card; do not block report generation.
4. Output a core conclusion summary under 100 words.

Step 5.6: Generate Auxiliary Enhancements

After the main report is generated:

1. Generate Researcher's Narrative:

   bash

   python scripts/generate_narrative.py --meta /path/to/execution_meta.json --output /tmp/narrative.txt
   

   Insert the output into the report as Appendix C: Researcher's Narrative.

2. Generate Reproducible Research Recipe: Extract input and config fields from execution_meta.json and format per Appendix B in assets/report_template.md.

3. Aggregate Information Gaps: Compile the gap list recorded during execution into a table per Appendix A format in the template.

V. Report Output & Auxiliary Enhancement Features

5.1 Main Report Structure (User-Facing)

markdown

# [Vendor] - [Solution Full Model] Technical Research Report

## Executive Summary (≤100 words)

## Chapter 1: Overall Architecture Anchoring & Information Boundaries

## Chapter 2: Hardware System Deep-Dive

## Chapter 3: Software System Deep-Dive

## Chapter 4: Hardware-Software Co-Design Principles

## Chapter 5: Technical Features & Industry Benchmarking (incl. Credibility Scorecard)

## Appendix A: Information Gaps & Manual Intervention Suggestions
| Gap ID | Target URL | Missing Description | Suggested Manual Action |

## Appendix B: Research Recipe (Reproducible Config)
(code block)

## Appendix C: Researcher's Narrative (First-Person Reflection)
(≤150 words)

## Appendix D: Source Attribution Summary (Optional)

5.2 Auxiliary Enhancement Features (Non-Core; Fail Silently)

Feature	Implementation	Degradation Strategy
Credibility Scorecard	Generated inline by LLM in Step 5. Zero extra calls.	Silently omit if formatting fails.
Researcher's Narrative	Generated by scripts/generate_narrative.py from meta JSON using templates. Zero LLM calls.	Omit appendix if script fails or meta missing.
Reproducible Recipe	Extracted from execution_meta.json fields. Zero LLM calls.	Omit if meta missing.
Silent Evidence Package	Async save of raw scraped text to evidence/ directory. Report links with [Evidence] anchors. Zero LLM calls.	Log only; omit links if save fails.

5.3 Audit Trail Metadata (Not User-Facing; For Audit & Evaluation)

Each run generates execution_meta.json in the output directory, containing:

• Input parameters
• Execution statistics
• Scraping details
• Information boundary counts
• Compliance check results

VI. Resource Policy & Token Efficiency Principles

This Skill adheres to the following efficiency principles to control usage costs:

1. Minimal LLM Calls: Only the 5 core analysis steps invoke LLM. All enhancements use deterministic scripts or templates with zero extra LLM overhead.
2. Context Compression: All scraped text is cleaned via scripts/compress_content.py before entering LLM context (tags stripped, single-source capped at 3000 chars).
3. Step-Wise Context Reuse: Subsequent steps only reference summary conclusions from prior steps, avoiding context bloat.
4. User-Controlled Verbosity: Users may select "Brief Mode" in the questionnaire (output compressed ~50%), or append --brief flag in quick-start.
5. Auxiliary Features Silent Degradation: All non-core enhancements fail silently without retries or remedial LLM calls.

Estimated Token Consumption (GPT-4o equivalent, medium-info scenario):

• Detailed report mode: ~8,000 – 15,000 tokens
• Brief mode: ~4,000 – 7,000 tokens

VII. Quick-Start Example

Simulated complete interaction:

User: research Framatome "Reactor Pressure Vessel Bolt Tensioning Robot System" "Nuclear Refueling Outage" --quick

Skill (Internal):

1. Adopts default config, enters Step 0 Recon.
2. Discovers Google Patents link, triggers Playwright.
3. Generates Feasibility Brief and automatically proceeds to deep steps (due to --quick).
4. Executes Steps 1-5; in hardware section, notes missing hydraulic valve specs, performs one supplementary search without success, marks gap.
5. Generates final report with scorecard, narrative, recipe, and evidence links.

Final Report Snippet:

2.3 Hydraulic Control Unit

Core Components: Proportional servo valve [Public: Patent USXXXX]. Specific Model & Key Parameters: [Info Missing: Manual check from equipment nameplate or supplier] (See Appendix A: Information Gap GAP-01)

VIII. Capability Boundary Statement

This Skill Accepts:

• Full-dimensional breakdown of targeted technical solutions (vendor+model+scenario clear).
• Automated research based on public internet information.
• User-provided patents/whitepapers as priority sources.

This Skill Rejects:

• Generalized industry analysis, market research, financial analysis.
• Bypassing login walls, paywalls, CAPTCHAs.
• Fabricating undisclosed component parameters or algorithm details.
• Infinite recursive deep-diving into a single technical detail (strict depth limits).

Boundary Behaviors:

• Vague input → Guide user to refine.
• Dynamic scraping failure → Mark gap, do not block workflow.
• Insufficient info for specified granularity → Degrade output and inform user.

IX. Execution Directives & Exception Handling

Detailed directives and checklists are in references/rules.md. Core requirements:

1. Scope Locking: Strict correspondence to target solution.
2. Source Attribution: Clear distinction between fact and derivation.
3. Granularity Compliance: No vague statements.
4. No Marketing: No copying promotional text.
5. Logical Consistency: Consistency across steps.
6. Comprehensibility: Core principles must be explained within topic boundaries.

Common exceptions:

Scenario	Handling
Playwright interaction failure (tab not found)	Record gap, insert [Info Missing], aggregate in Appendix A.
All scraping sub-Skills return empty	Pause workflow, request user to provide private sources or adjust keywords.
Output judged as vague or marketing fluff	Re-invoke step prompt with additional emphasis directive.

---

This Skill follows Harness Engineering design principles, ensuring reliability, controllability, and measurability.