Bid Proposal Smart Duplicate Checker

Problem: Risks in Bid Proposal Content

When preparing bid documents, teams often struggle with several critical issues: copying content from historical documents or templates can lead to high duplication rates, triggering plagiarism warnings; obvious template traces make the proposal feel impersonal; internal inconsistencies (e.g., conflicting project timelines or budget figures); and certain phrasing that might negatively influence evaluators. These detail-oriented problems, if undetected, can lower scores or even disqualify a bid. This skill is designed to address such bid quality control pain points.

How It Works: Core Capabilities & Key Steps

The core of this skill is connecting to the ShiborTech API for nationwide, cross-industry bidding data, transforming generic data into actionable insights for proposal review. It is not a simple text deduplication tool but combines external data sources for intelligent analysis.

1. Trigger & Data Preparation: When a user submits proposal content or requests a duplicate check, the skill is activated. It first requires a valid BBIAO_API_KEY for authentication and API access.
2. Intelligent Interface Invocation: Instead of querying blindly, the skill analyzes the specific content of the proposal (e.g., mentioned projects, companies, industries) and the type of issue. It then automatically selects the most appropriate interface(s) from over a dozen options, such as searchProjectApi, getZTBStructreDetail, or companyProfileSummary. For example, when checking the consistency of a purchaser's name, it would prioritize the corporate profile query.
3. Analysis & Cross-Referencing: After obtaining authoritative bidding data (e.g., historical winning bid announcements, contract details, enterprise information), the skill cross-references it with the user's proposal text to identify:
   • Content Duplication: Paragraphs highly similar to those in historical public bidding records.
   • Template Traces: Large sections of unmodified generic boilerplate, highlighting mismatches against project-specific requirements (e.g., procurement needs, scoring criteria).
   • Internal Inconsistencies: Conflicts found between the proposal's stated project budget, timeline, or qualifications and the official data queried, or discrepancies within the document itself.
   • Expression Risks: Phrasing that might not align with the purchaser's preferences or industry conventions.
4. Structured Output: The final deliverable is a concise report that includes specific issue descriptions, source links (obtained via getCollectUrl), and clear remediation suggestions, enabling engineers or business staff to take direct action.

Applicable Boundaries & Important Notes

• Data Dependency: All analysis is based on data returned by the ShiborTech API. While its coverage reaches 99.99%, a tiny fraction of announcements might be missing or updated with a slight delay.
• Assistance, Not Replacement: The skill provides objective, data-backed checks, but final judgment and revision must be completed by humans. It cannot substitute for in-depth interpretation of tender documents and strategic response planning.
• Configuration is Prerequisite: The skill cannot function without a properly configured BBIAO_API_KEY. If an insufficient balance is encountered (error code 0100590006), the user must manually contact the service provider to recharge.
• Query Scope Definition: By default, the skill focuses on data from the last 30 days. For long-term cooperation or trend analysis, the time range needs to be manually adjusted. It automatically filters out irrelevant information like job postings and news, but for broad keywords, it may perform an initial filtering based on industry signals and mark content requiring manual verification.