targeted-tech-research

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

Security Scan

The skill's code, instructions, and requirements are coherent with its stated purpose (vendor-specific technical research); it delegates all scraping to sub-skills, requests no credentials, and has no install step or external endpoints baked in.

Purpose & Capability

Name/description match the implementation: SKILL.md defines a research workflow and explicitly delegates network scraping to sub-skills. Included helper scripts (content compression, narrative generation) and a pre-flight input validator are appropriate for a research orchestration skill.

Instruction Scope

Runtime instructions constrain actions to orchestrating research, enforcing source/derivation annotations, and delegating scraping. SKILL.md explicitly prohibits accessing paywalled/login-protected pages and claims not to perform direct network requests. No instructions request arbitrary system files, secrets, or unrelated data.

Install Mechanism

No install spec is provided (instruction-only with small local helper scripts). No downloads from external URLs or package installs are present in the manifest.

Credentials

The skill declares no required environment variables, credentials, or config paths. All included scripts operate on local input/output files and metadata; there are no secret-looking env var references.

Persistence & Privilege

Skill is not marked always:true and uses normal autonomous invocation defaults. It does not request system-wide configuration changes or persistent elevated privileges.

Assessment

This skill appears internally consistent and appropriate for public-source technical research. Before installing: (1) confirm you trust the platform sub-skills it will call (web-scraper, playwright-scraper) because those perform network IO, (2) avoid asking it to fetch private or behind-login material (the Skill states it will abandon paywalled pages), and (3) verify any high-impact or safety-sensitive technical derivations against original sources (the Skill will label derived content but you should cross-check). If you do not want the agent to run research autonomously, restrict the skill's invocation in your agent settings.

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:

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:

   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:

   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)

# [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.

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