reverse-skills-max: initial public release (restored, redacted, hardened)
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baradok609hecl1pq6qi
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---
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name: research
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description: Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.
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---
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# Research Skill - Preliminary Research
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## Trigger
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`/research <topic>`
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## Workflow
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### Step 1: Generate Initial Framework from Model Knowledge
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Based on topic, use model's existing knowledge to generate:
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- Main research objects/items list in this domain
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- Suggested research field framework
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Output {step1_output}, use request_user_input to confirm:
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- Need to add/remove items?
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- Does field framework meet requirements?
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### Step 2: Web Search Supplement
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Use request_user_input to ask for time range (e.g., last 6 months, since 2024, unlimited).
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**Parameter Retrieval**:
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- `{topic}`: User input research topic
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- `{YYYY-MM-DD}`: Current date
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- `{step1_output}`: Complete output from Step 1
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- `{time_range}`: User specified time range
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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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Launch 1 web-search-agent (background), **Prompt Template**:
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```python
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prompt = f"""## Task
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Research topic: {topic}
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Current date: {YYYY-MM-DD}
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Based on the following initial framework, supplement latest items and recommended research fields.
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## Existing Framework
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{step1_output}
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## Goals
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1. Verify if existing items are missing important objects
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2. Supplement items based on missing objects
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3. Continue searching for {topic} related items within {time_range} and supplement
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4. Supplement new fields
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## Output Requirements
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Return structured results directly (do not write files):
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### Supplementary Items
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- item_name: Brief explanation (why it should be added)
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...
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### Recommended Supplementary Fields
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- field_name: Field description (why this dimension is needed)
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...
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### Sources
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- [Source1](url1)
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- [Source2](url2)
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"""
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```
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**One-shot Example** (assuming researching AI Coding History):
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```
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## Task
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Research topic: AI Coding History
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Current date: 2025-12-30
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Based on the following initial framework, supplement latest items and recommended research fields.
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## Existing Framework
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### Items List
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1. GitHub Copilot: Developed by Microsoft/GitHub, first mainstream AI coding assistant
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2. Cursor: AI-first IDE, based on VSCode
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...
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### Field Framework
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- Basic Info: name, release_date, company
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- Technical Features: underlying_model, context_window
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...
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## Goals
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1. Verify if existing items are missing important objects
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2. Supplement items based on missing objects
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3. Continue searching for AI Coding History related items within since 2024 and supplement
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4. Supplement new fields
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## Output Requirements
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Return structured results directly (do not write files):
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### Supplementary Items
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- item_name: Brief explanation (why it should be added)
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...
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### Recommended Supplementary Fields
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- field_name: Field description (why this dimension is needed)
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...
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### Sources
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- [Source1](url1)
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- [Source2](url2)
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```
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### Step 3: Ask User for Existing Fields
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Use request_user_input to ask if user has existing field definition file, if so read and merge.
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### Step 4: Generate Outline (Separate Files)
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Merge {step1_output}, {step2_output} and user's existing fields, generate two files:
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**outline.yaml** (items + config):
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- topic: Research topic
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- items: Research objects list
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- execution:
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- batch_size: Number of parallel agents (confirm with request_user_input)
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- items_per_agent: Items per agent (confirm with request_user_input)
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- output_dir: Results output directory (default: ./results)
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**fields.yaml** (field definitions):
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- Field categories and definitions
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- Each field's name, description, detail_level
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- detail_level hierarchy: brief -> moderate -> detailed
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- uncertain: Uncertain fields list (reserved field, auto-filled in deep phase)
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### Step 5: Output and Confirm
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- Create directory: `./{topic_slug}/`
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- Save: `outline.yaml` and `fields.yaml`
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- Show to user for confirmation
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## Output Path
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```
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{current_working_directory}/{topic_slug}/
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├── outline.yaml # items list + execution config
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└── fields.yaml # field definitions
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```
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## Follow-up Commands
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- `/research-add-items` - Supplement items
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- `/research-add-fields` - Supplement fields
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- `/research-deep` - Start deep research
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@@ -0,0 +1,198 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import json
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import sys
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from collections import defaultdict
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from pathlib import Path
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import yaml
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CATEGORY_MAPPING = {
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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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_SKIP_KEYS = {"_source_file", "uncertain"}
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def load_fields_yaml(fields_path):
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"""Parse fields.yaml in the single schema the research skills emit:
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fields:
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<category>:
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- {name: ..., description: ..., detail_level: ...}
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...
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uncertain: []
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This is the ONLY accepted shape. A fields.yaml that does not match fails
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loudly instead of silently passing with zero fields.
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Required semantics (so the validator can never pass vacuously / "lie"):
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- if ANY field carries an explicit `required:` key -> opt-in, preserve it
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- else (detail_level-style, no markers) -> ALL fields required, because the
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script's stated purpose is COMPLETE field coverage.
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"""
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with fields_path.open(encoding="utf-8") as f:
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data = yaml.safe_load(f) or {}
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defs = [] # (name, category, required_or_None)
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fn = data.get("fields")
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if not isinstance(fn, dict):
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print(f"[ERROR] fields.yaml must use the `fields: {{<category>: [{{name, ...}}]}}` shape; got {type(fn).__name__ if fn is not None else 'None'}.")
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sys.exit(1)
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for cname, flist in fn.items():
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if cname in _SKIP_KEYS:
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continue
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if not isinstance(flist, list):
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print(f"[ERROR] category `{cname}` must map to a list of field dicts; got {type(flist).__name__}.")
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sys.exit(1)
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for field in flist:
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if isinstance(field, dict) and "name" in field:
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defs.append((str(field["name"]), str(cname), field.get("required", None)))
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else:
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print(f"[ERROR] field entry under `{cname}` must be a dict with a `name` key; got {field!r}.")
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sys.exit(1)
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if not defs:
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print("[ERROR] fields.yaml parsed zero fields. Ensure at least one category with field dicts.")
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sys.exit(1)
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all_fields = {n for n, _, _ in defs}
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if any(r is not None for _, _, r in defs):
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required_fields = {n for n, _, r in defs if r}
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else:
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required_fields = set(all_fields)
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field_categories = {n: c for n, c, _ in defs}
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return all_fields, required_fields, field_categories
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def extract_json_fields(data, category_mapping=None):
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category_mapping = CATEGORY_MAPPING if category_mapping is None else category_mapping
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nested_keys = {k for keys in category_mapping.values() for k in keys}
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fields = set()
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stack = [(data, True)]
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while stack:
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obj, is_category_level = stack.pop()
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if isinstance(obj, dict):
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for k, v in obj.items():
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if k in _SKIP_KEYS:
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continue
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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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stack.append((v, True))
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continue
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fields.add(k)
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elif isinstance(obj, list):
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stack.extend((item, is_category_level) for item in obj if isinstance(item, dict))
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return fields
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def validate_json(json_path, all_fields, required_fields, field_categories):
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with json_path.open(encoding="utf-8") as f:
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data = json.load(f)
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json_fields = extract_json_fields(data)
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covered = all_fields & json_fields
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missing = all_fields - json_fields
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extra = json_fields - all_fields
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missing_required = missing & required_fields
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missing_by_category = defaultdict(list)
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for field in missing:
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missing_by_category[field_categories.get(field, "Unknown")].append(field)
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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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"covered": len(covered),
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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": sorted(missing_required),
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"missing_optional": sorted(missing - required_fields),
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"missing_by_category": {k: sorted(v) for k, v in missing_by_category.items()},
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"extra_fields": sorted(extra),
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"valid": len(missing_required) == 0,
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}
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def print_result(result, verbose=True):
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status = "PASS" if result["valid"] else "FAIL"
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line = "=" * 60
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print(f"\n{line}")
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print(f"[{status}] {result['file']}")
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print(line)
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print(f"Coverage: {result['coverage_rate']:.1f}% ({result['covered']}/{result['total_defined']})")
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if result["missing_required"]:
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print(f"\n[ERROR] Missing required fields ({len(result['missing_required'])}):")
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print("\n".join(f" - {f}" for f in result["missing_required"]))
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if verbose and result["missing_optional"]:
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missing_required = set(result["missing_required"])
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print(f"\n[WARN] Missing optional fields ({len(result['missing_optional'])}):")
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for cat in sorted(result["missing_by_category"]):
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optional = [f for f in result["missing_by_category"][cat] if f not in missing_required]
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if optional:
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print(f" [{cat}]: {', '.join(optional)}")
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if verbose and result["extra_fields"]:
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extra = result["extra_fields"]
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print(f"\n[INFO] Extra fields ({len(extra)}):")
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print(f" {', '.join(extra[:10])}")
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if len(extra) > 10:
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print(f" ... and {len(extra) - 10} more")
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def main():
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import argparse
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parser = argparse.ArgumentParser(description="Validate whether JSON files cover all fields defined in fields.yaml")
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parser.add_argument("--fields", "-f", type=str, help="Path to fields.yaml", default="fields.yaml")
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parser.add_argument("--json", "-j", type=str, nargs="*", help="JSON file paths to validate")
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parser.add_argument("--dir", "-d", type=str, help="Directory containing JSON files", default="results")
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parser.add_argument("--quiet", "-q", action="store_true", help="Show summary only")
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args = parser.parse_args()
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fields_path = Path(args.fields)
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if not fields_path.exists():
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for p in (Path.cwd() / "fields.yaml", Path.cwd().parent / "fields.yaml"):
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if p.exists():
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fields_path = p
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break
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if not fields_path.exists():
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print(f"[ERROR] fields.yaml not found: {fields_path}")
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sys.exit(1)
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print(f"Field definition file: {fields_path}")
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all_fields, required_fields, field_categories = load_fields_yaml(fields_path)
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print(f"Total fields: {len(all_fields)} (required: {len(required_fields)}, optional: {len(all_fields) - len(required_fields)})")
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json_files = (
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[Path(p) for p in args.json]
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if args.json
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else sorted(Path(args.dir).glob("*.json")) if Path(args.dir).exists() else []
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)
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if not json_files:
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print("[WARN] No JSON files found")
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sys.exit(0)
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results = []
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for json_path in json_files:
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if not json_path.exists():
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print(f"[WARN] File not found: {json_path}")
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continue
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result = validate_json(json_path, all_fields, required_fields, field_categories)
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results.append(result)
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print_result(result, verbose=not args.quiet)
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line = "=" * 60
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print(f"\n{line}")
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print("Summary")
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print(line)
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passed = sum(1 for r in results if r["valid"])
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avg_coverage = sum(r["coverage_rate"] for r in results) / len(results) if results else 0
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print(f"Validation passed: {passed}/{len(results)}")
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print(f"Average coverage: {avg_coverage:.1f}%")
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if passed < len(results):
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sys.exit(1)
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if __name__ == "__main__":
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main()
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