fix: address code review issues from Claude and GPT-5.2
- report/SKILL.md: add AskUserQuestion to allowed-tools - validate_json.py: remove unused imports (List, Any) - validate_json.py: sort output lists for deterministic order - validate_json.py: limit recursion to category level only - validate_json.py: add list-of-dict handling - EN validate_json.py: remove Chinese from CATEGORY_MAPPING - EN deep/SKILL.md: change output language from Chinese to English - deep/SKILL.md: add slug handling for filenames - report/SKILL.md: fix uncertain_fields -> uncertain naming - README: add PyYAML dependency note 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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jilinchen
co-authored by
Claude Opus 4.5
Claude Opus 4.5
parent
98ddfce42d
commit
779c447670
@@ -28,7 +28,7 @@ Find `*/outline.yaml` file in current working directory, read items list, execut
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- `{item_related_info}`: item's complete yaml content (name + category + description etc.)
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- `{output_dir}`: execution.output_dir from outline.yaml (default: ./results)
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- `{fields_path}`: absolute path to {topic}/fields.yaml
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- `{output_path}`: absolute path to {output_dir}/{item_name}.json
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- `{output_path}`: absolute path to {output_dir}/{item_name_slug}.json (slugify item_name: replace spaces with _, remove special chars)
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**Hard Constraint**: The following prompt must be strictly reproduced, only replacing variables in {xxx}, do not modify structure or wording.
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@@ -44,7 +44,7 @@ Read {fields_path} to get all field definitions
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1. Output JSON according to fields defined in fields.yaml
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2. Mark uncertain field values with [uncertain]
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3. Add uncertain array at the end of JSON, listing all uncertain field names
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4. All field values must be in Chinese (research can be in English, but final JSON values in Chinese)
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4. All field values must be in English
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## Output Path
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{output_path}
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@@ -70,7 +70,7 @@ Read /home/weizhena/AIcoding/aicoding-history/fields.yaml to get all field defin
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1. Output JSON according to fields defined in fields.yaml
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2. Mark uncertain field values with [uncertain]
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3. Add uncertain array at the end of JSON, listing all uncertain field names
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4. All field values must be in Chinese (research can be in English, but final JSON values in Chinese)
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4. All field values must be in English
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## Output Path
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/home/weizhena/AIcoding/aicoding-history/results/GitHub_Copilot.json
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@@ -1,6 +1,6 @@
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---
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description: Summarize deep research results into markdown report, cover all fields, skip uncertain values.
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allowed-tools: Read, Write, Glob, Bash
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allowed-tools: Read, Write, Glob, Bash, AskUserQuestion
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---
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# Research Report - Summary Report
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@@ -75,7 +75,7 @@ CATEGORY_MAPPING = {
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Collect fields that exist in JSON but not defined in fields.yaml, put in "Other Info" category. Note to filter:
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- Internal fields: `_source_file`, `uncertain`
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- Nested structure top-level keys: `basic_info`, `technical_features` etc.
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- `uncertain_fields` list: Display each field name on separate line, don't compress into one line
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- `uncertain` array: Display each field name on separate line, don't compress into one line
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**5. Uncertain Value Skipping**
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Skip conditions:
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@@ -9,18 +9,18 @@ import json
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import yaml
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import sys
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from pathlib import Path
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from typing import Dict, List, Set, Any, Tuple
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from typing import Dict, Set, Tuple
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# Category mapping (supports both Chinese and English keys)
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# Category mapping (English keys only)
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CATEGORY_MAPPING = {
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"basic_info": ["basic_info", "基本信息"],
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"technical_features": ["technical_features", "technical_characteristics", "技术特性"],
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"performance_metrics": ["performance_metrics", "performance", "性能指标"],
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"milestone_significance": ["milestone_significance", "milestones", "里程碑意义"],
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"business_info": ["business_info", "commercial_info", "商业信息"],
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"competition_ecosystem": ["competition_ecosystem", "competition", "竞争与生态"],
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"history": ["history", "历史沿革"],
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"market_positioning": ["market_positioning", "market", "市场定位"],
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"basic_info": ["basic_info", "Basic Info"],
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"technical_features": ["technical_features", "technical_characteristics", "Technical Features"],
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"performance_metrics": ["performance_metrics", "performance", "Performance Metrics"],
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"milestone_significance": ["milestone_significance", "milestones", "Milestone Significance"],
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"business_info": ["business_info", "commercial_info", "Business Info"],
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"competition_ecosystem": ["competition_ecosystem", "competition", "Competition Ecosystem"],
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"history": ["history", "History"],
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"market_positioning": ["market_positioning", "market", "Market Positioning"],
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}
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@@ -53,30 +53,41 @@ def load_fields_yaml(fields_path: Path) -> Tuple[Set[str], Set[str], Dict[str, s
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def extract_json_fields(data: Dict, category_mapping: Dict = None) -> Set[str]:
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"""
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Extract all field names from JSON (supports both flat and nested structures)
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Only extracts field names at category level, not nested dict/list values
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"""
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if category_mapping is None:
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category_mapping = CATEGORY_MAPPING
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# Get all possible nested keys
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# Get all possible nested keys (category containers)
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nested_keys = set()
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for keys in category_mapping.values():
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nested_keys.update(keys)
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fields = set()
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def collect_fields(d: Dict, is_top_level: bool = True):
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for k, v in d.items():
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# Skip internal fields
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if k in {"_source_file", "uncertain"}:
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continue
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# If it's a top-level nested key, recurse into it
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if is_top_level and k in nested_keys:
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if isinstance(v, dict):
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collect_fields(v, is_top_level=False)
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else:
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fields.add(k)
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if isinstance(v, dict):
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collect_fields(v, is_top_level=False)
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def collect_fields(d, is_category_level: bool = True):
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"""
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Collect fields from dict or list structures
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is_category_level: True if we're at top level or inside a category container
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"""
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if isinstance(d, dict):
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for k, v in d.items():
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# Skip internal fields
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if k in {"_source_file", "uncertain"}:
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continue
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# If it's a category container key, recurse into it
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if is_category_level and k in nested_keys:
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if isinstance(v, dict):
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collect_fields(v, is_category_level=True)
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else:
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# This is a field name, add it
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fields.add(k)
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# Don't recurse into field values (avoid counting nested keys as fields)
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elif isinstance(d, list):
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# Handle list-of-dict structures at category level
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for item in d:
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if isinstance(item, dict):
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collect_fields(item, is_category_level=is_category_level)
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collect_fields(data)
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return fields
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@@ -110,6 +121,10 @@ def validate_json(json_path: Path, all_fields: Set[str], required_fields: Set[st
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missing_by_category[cat] = []
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missing_by_category[cat].append(field)
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# Sort lists within categories for deterministic output
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for cat in missing_by_category:
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missing_by_category[cat].sort()
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return {
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"file": json_path.name,
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"total_defined": len(all_fields),
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@@ -117,10 +132,10 @@ def validate_json(json_path: Path, all_fields: Set[str], required_fields: Set[st
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"missing": len(missing),
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"extra": len(extra),
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"coverage_rate": len(covered) / len(all_fields) * 100 if all_fields else 100,
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"missing_required": list(missing_required),
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"missing_optional": list(missing_optional),
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"missing_required": sorted(missing_required),
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"missing_optional": sorted(missing_optional),
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"missing_by_category": missing_by_category,
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"extra_fields": list(extra),
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"extra_fields": sorted(extra),
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"valid": len(missing_required) == 0, # Valid if all required fields are covered
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}
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@@ -140,7 +155,8 @@ def print_result(result: Dict, verbose: bool = True):
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if verbose and result["missing_optional"]:
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print(f"\n[WARN] Missing optional fields ({len(result['missing_optional'])}):")
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for cat, fields in result["missing_by_category"].items():
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for cat in sorted(result["missing_by_category"].keys()):
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fields = result["missing_by_category"][cat]
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optional_fields = [f for f in fields if f not in result["missing_required"]]
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if optional_fields:
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print(f" [{cat}]: {', '.join(optional_fields)}")
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