fix(security): 添加VITE_PAYMENT_URL环境变量配置

.qoder/skills/understand-knowledge/SKILL.md

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+---
+name: understand-knowledge
+description: Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
+argument-hint: [wiki-directory]
+---
+
+# /understand-knowledge
+
+Analyzes a Karpathy-pattern LLM wiki — a three-layer knowledge base with raw sources, wiki markdown, and a schema file — and produces an interactive knowledge graph dashboard.
+
+## What It Detects
+
+The **Karpathy LLM wiki pattern** (see https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f):
+- **Raw sources** — immutable source documents (articles, papers, data files)
+- **Wiki** — LLM-generated markdown files with wikilinks (`[[target]]` syntax)
+- **Schema** — CLAUDE.md, AGENTS.md, or similar configuration file
+- **index.md** — content catalog organized by categories
+- **log.md** — chronological operation log
+
+Detection signals: has `index.md` + multiple `.md` files with wikilinks. May have `raw/` directory and schema file.
+
+## Instructions
+
+### Phase 1: DETECT
+
+1. Determine the target directory:
+   - If the user provided a path argument, use that
+   - Otherwise, use the current working directory
+
+2. Run the format detection script bundled with this skill:
+   ```
+   python3 <SKILL_DIR>/parse-knowledge-base.py <TARGET_DIR>
+   ```
+   - If the script exits with an error, tell the user this doesn't appear to be a Karpathy-pattern wiki and explain what was expected
+   - If successful, proceed. The script writes `scan-manifest.json` to `<TARGET_DIR>/.understand-anything/intermediate/`
+
+3. Read the scan-manifest.json and announce the results:
+   - "Detected Karpathy wiki: N articles, N sources, N topics, N wikilinks (N unresolved)"
+   - List the categories found from index.md
+
+### Phase 2: SCAN (already done)
+
+The parse script in Phase 1 already performed the deterministic scan. The scan-manifest.json contains:
+- Article nodes (one per wiki .md file) with extracted wikilinks, headings, frontmatter
+- Source nodes (one per raw/ file)
+- Topic nodes (from index.md section headings)
+- `related` edges (from wikilinks)
+- `categorized_under` edges (from index.md sections)
+
+No additional scanning is needed. Proceed to Phase 3.
+
+### Phase 3: ANALYZE
+
+Dispatch `article-analyzer` subagents to extract implicit knowledge:
+
+1. Read the scan-manifest.json to get the article list
+
+2. Prepare batches of 10-15 articles each, grouped by category when possible (articles in the same category are more likely to have implicit cross-references)
+
+3. For each batch, dispatch an `article-analyzer` subagent with:
+   - The batch of articles (id, name, summary, wikilinks, category, content from knowledgeMeta)
+   - The full list of existing node IDs (so the agent can reference them)
+   - The batch number for output file naming
+   - The intermediate directory path: `$INTERMEDIATE_DIR = <TARGET_DIR>/.understand-anything/intermediate`
+   
+   The agent will write `analysis-batch-{N}.json` to the intermediate directory.
+
+4. Run up to 3 batches concurrently. Wait for all batches to complete.
+
+5. If any batch fails, log a warning but continue — the scan-manifest provides a solid base graph even without LLM analysis.
+
+### Phase 4: MERGE
+
+1. Run the merge script bundled with this skill:
+   ```
+   python3 <SKILL_DIR>/merge-knowledge-graph.py <TARGET_DIR>
+   ```
+
+2. The script:
+   - Combines scan-manifest.json + all analysis-batch-*.json files
+   - Deduplicates entities (case-insensitive name matching)
+   - Normalizes node/edge types via alias maps
+   - Builds layers from index.md categories
+   - Builds a tour from index.md section ordering
+   - Writes `assembled-graph.json` to the intermediate directory
+
+3. Read the merge report from stderr and announce:
+   - Total nodes, edges, layers, tour steps
+   - How many entities/claims the LLM analysis added
+
+### Phase 5: SAVE
+
+1. Read the assembled-graph.json
+
+2. Run basic validation:
+   - Every edge source/target must reference an existing node
+   - Every node must have: id, type, name, summary, tags, complexity
+   - Remove any edges with dangling references
+
+3. Copy the validated graph to `<TARGET_DIR>/.understand-anything/knowledge-graph.json`
+
+4. Write metadata to `<TARGET_DIR>/.understand-anything/meta.json`:
+   ```json
+   {
+     "lastAnalyzedAt": "<ISO timestamp>",
+     "gitCommitHash": "<from git rev-parse HEAD or empty>",
+     "version": "1.0.0",
+     "analyzedFiles": <number of wiki articles>
+   }
+   ```
+
+5. Clean up intermediate files:
+   ```
+   rm -rf <TARGET_DIR>/.understand-anything/intermediate
+   ```
+
+6. Report summary to the user:
+   - "Knowledge graph saved: N articles, N entities, N topics, N claims, N sources"
+   - "N edges (N wikilink, N categorized, N implicit)"
+   - "N layers, N tour steps"
+
+7. Auto-trigger the dashboard:
+   ```
+   /understand-dashboard <TARGET_DIR>
+   ```
+
+## Notes
+
+- The parse script handles ALL deterministic extraction (wikilinks, headings, frontmatter, categories from index.md). The LLM agents only add implicit knowledge that requires inference.
+- Categories and taxonomy come from index.md section headings, NOT from filename prefixes. The Karpathy spec is intentionally abstract about naming conventions.
+- The graph uses `kind: "knowledge"` to signal the dashboard to use force-directed layout instead of hierarchical dagre.
+- Source nodes from raw/ are lightweight (filename + size only) — we don't parse PDFs or binary files.