Matt Van Horn
4e91f4e754
* feat(resolve): category-peer subreddit map for Step 0.55 Introduces scripts/lib/categories.py with a curated category->peer-subs map and wires scripts/lib/resolve.py auto_resolve() to merge peers into the WebSearch-extracted subreddit list. Named 2026-04-22 failure mode: a "Prompting GPT Image 2" run resolved only r/OpenAI + r/ChatGPT and missed r/StableDiffusion, r/midjourney, r/dalle2, r/aiArt where prompting techniques actually live. Map is static, curated, ~11 categories (ai_image_generation, ai_video_generation, ai_music_generation, ai_coding_agent, ai_agent_framework, ai_chat_model, saas_screen_recording, saas_productivity, prediction_markets, crypto_defi, dev_tool_cli). First-match-wins ordering from most-specific to least-specific. Compound-term patterns only (no bare common nouns like "image", "ai"). auto_resolve now: - calls detect_category(topic) after _extract_subreddits - merges peer_subs case-insensitively, caps at MAX_SUBS (10) - preserves every WebSearch-returned sub (freshest signal) - emits [Resolve] Matched category=<id>, adding peers: <list> on stderr only when peers were actually added - returns new "category" key in the result dict for observability - wraps classifier in try/except so failures degrade to unwidened list Includes drive-by: test_full_resolve / test_partial_failure searches_run expectations bumped from 3->4 / 2->3 to match the current queries dict (subreddit + news + x_handle + github). * feat(skill): Step 0.55 category-peer expansion and self-check Adds Section 2a (category-peer expansion, MANDATORY for product topics) and the Step 0.55 self-check checkpoint that fires immediately before the Resolved block displays. Structural mirror of the engine-side categories.py map: same categories, same peer subs, same priority order. The model-side path now: - Applies category-peer expansion to the WebSearch-resolved subs on every product-in-a-known-category run. - Emits the (+ <category_id> peers) annotation on the Reddit line of the Resolved block as the observable contract. Absence on a product-in-a-known-category topic is a Step 0.55 regression. - Runs a self-check before emitting Resolved: "does the resolved list include at least 2 peer subs for the matched category? if not, widen NOW and do not run the engine yet." Mirror of the Python map lives inside Step 0.55 as a table for the model to pattern-match against; extrapolation to unlisted categories is explicitly allowed. Worked example (the exact failing query) appears below the table so reviewers can see before/after at a glance. Both changes land inside the existing Step 0.55 block. No new top-level section, no new LAW. LAWs 1-6 wording unchanged. * test: end-to-end regression for GPT Image 2 failure mode Stubs grounding.web_search to return the OpenAI-only subs that caused the 2026-04-22 failure, then asserts that auto_resolve widens to include the image-gen peers and emits the [Resolve] Matched category=ai_image_generation stderr line. Covers the cap boundary and the uncategorized-topic no-op path. Fixture tests/fixtures/prompting-gpt-image-2-resolved-block.md is documentation-grade (not parsed by tests) and shows the pre-fix vs post-fix Resolved block shape so reviewers can evaluate future categories.py edits against the original bug. --------- Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
284 lines
7.5 KiB
Python
284 lines
7.5 KiB
Python
"""Category-peer subreddit map for Step 0.55 community resolution.
|
|
|
|
When a topic is a product in a known category (AI image generation, AI coding
|
|
agents, SaaS screen recording, etc.), brand-specific subreddits returned by
|
|
WebSearch are insufficient: cross-product technique discussion lives in
|
|
category-peer subs. This module classifies a topic into a category by matching
|
|
compound-term patterns against the lowercased topic string, then returns the
|
|
priority-ordered peer subreddit list for that category.
|
|
|
|
The map is intentionally small, curated, and code-reviewed. Adding a new
|
|
category is a code change; there is no user-editable override surface.
|
|
|
|
False-positive guard: every pattern is either a multi-word compound (e.g.
|
|
"image generation", "text to image") or a domain-specific single word
|
|
(e.g. "midjourney", "stablediffusion"). Bare common nouns like "image",
|
|
"ai", or "model" are never used as patterns.
|
|
|
|
First-match-wins: categories are evaluated in declared order. Entries are
|
|
sorted from most-specific to least-specific so narrower categories claim a
|
|
topic before broader ones. For example, `ai_image_generation` appears
|
|
before `ai_chat_model` so "gpt image 2" matches the image-gen category.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import List, Optional, TypedDict
|
|
|
|
|
|
class _CategoryEntry(TypedDict):
|
|
patterns: List[str]
|
|
peer_subs: List[str]
|
|
|
|
|
|
CATEGORY_PEERS: dict[str, _CategoryEntry] = {
|
|
"ai_image_generation": {
|
|
"patterns": [
|
|
"image generation",
|
|
"image gen",
|
|
"text to image",
|
|
"text-to-image",
|
|
"gpt image",
|
|
"gpt-image",
|
|
"nano banana",
|
|
"midjourney",
|
|
"stable diffusion",
|
|
"stablediffusion",
|
|
"dall-e",
|
|
"dalle",
|
|
"flux.1",
|
|
"flux schnell",
|
|
"imagen",
|
|
"seedance",
|
|
"ideogram",
|
|
"recraft",
|
|
],
|
|
"peer_subs": [
|
|
"StableDiffusion",
|
|
"midjourney",
|
|
"dalle2",
|
|
"aiArt",
|
|
"PromptEngineering",
|
|
"MediaSynthesis",
|
|
],
|
|
},
|
|
"ai_video_generation": {
|
|
"patterns": [
|
|
"video generation",
|
|
"text to video",
|
|
"text-to-video",
|
|
"sora",
|
|
"veo 3",
|
|
"veo3",
|
|
"runway gen",
|
|
"kling",
|
|
"pika labs",
|
|
"luma dream machine",
|
|
"hailuo",
|
|
],
|
|
"peer_subs": [
|
|
"aivideo",
|
|
"StableDiffusion",
|
|
"runwayml",
|
|
"singularity",
|
|
"MediaSynthesis",
|
|
],
|
|
},
|
|
"ai_music_generation": {
|
|
"patterns": [
|
|
"music generation",
|
|
"ai music",
|
|
"suno",
|
|
"udio",
|
|
"riffusion",
|
|
"stable audio",
|
|
],
|
|
"peer_subs": [
|
|
"SunoAI",
|
|
"udiomusic",
|
|
"aimusic",
|
|
"artificial",
|
|
],
|
|
},
|
|
"ai_coding_agent": {
|
|
"patterns": [
|
|
"claude code",
|
|
"cursor ide",
|
|
"github copilot",
|
|
"windsurf",
|
|
"aider",
|
|
"cline",
|
|
"openclaw",
|
|
"hermes agent",
|
|
"continue.dev",
|
|
"codeium",
|
|
"sweep ai",
|
|
"devin ai",
|
|
"coding agent",
|
|
"coding assistant",
|
|
],
|
|
"peer_subs": [
|
|
"ChatGPTCoding",
|
|
"LocalLLaMA",
|
|
"singularity",
|
|
"PromptEngineering",
|
|
],
|
|
},
|
|
"ai_agent_framework": {
|
|
"patterns": [
|
|
"agent framework",
|
|
"agentic framework",
|
|
"langchain",
|
|
"langgraph",
|
|
"crewai",
|
|
"autogen",
|
|
"llamaindex",
|
|
"dspy",
|
|
"smolagents",
|
|
],
|
|
"peer_subs": [
|
|
"LangChain",
|
|
"LocalLLaMA",
|
|
"AI_Agents",
|
|
"MachineLearning",
|
|
],
|
|
},
|
|
"ai_chat_model": {
|
|
"patterns": [
|
|
"gpt-5",
|
|
"gpt-4",
|
|
"claude opus",
|
|
"claude sonnet",
|
|
"claude haiku",
|
|
"gemini pro",
|
|
"gemini flash",
|
|
"llama 3",
|
|
"llama 4",
|
|
"deepseek",
|
|
"qwen",
|
|
"mistral large",
|
|
"grok",
|
|
],
|
|
"peer_subs": [
|
|
"LocalLLaMA",
|
|
"ChatGPT",
|
|
"ClaudeAI",
|
|
"singularity",
|
|
"artificial",
|
|
],
|
|
},
|
|
"saas_screen_recording": {
|
|
"patterns": [
|
|
"screen recording",
|
|
"screen recorder",
|
|
"loom video",
|
|
"tella screen",
|
|
"vidyard",
|
|
"screen capture tool",
|
|
],
|
|
"peer_subs": [
|
|
"SaaS",
|
|
"screenrecording",
|
|
"productivity",
|
|
"Entrepreneur",
|
|
],
|
|
},
|
|
"saas_productivity": {
|
|
"patterns": [
|
|
"notion app",
|
|
"obsidian plugin",
|
|
"obsidian app",
|
|
"linear app",
|
|
"asana",
|
|
"clickup",
|
|
"productivity app",
|
|
],
|
|
"peer_subs": [
|
|
"productivity",
|
|
"SaaS",
|
|
"ObsidianMD",
|
|
"Notion",
|
|
],
|
|
},
|
|
"prediction_markets": {
|
|
"patterns": [
|
|
"polymarket",
|
|
"kalshi",
|
|
"prediction market",
|
|
"event contracts",
|
|
"manifold markets",
|
|
],
|
|
"peer_subs": [
|
|
"Polymarket",
|
|
"Kalshi",
|
|
"predictionmarkets",
|
|
],
|
|
},
|
|
"crypto_defi": {
|
|
"patterns": [
|
|
"defi protocol",
|
|
"yield farming",
|
|
"liquidity pool",
|
|
"stablecoin",
|
|
"ethereum layer",
|
|
"layer 2",
|
|
"l2 rollup",
|
|
],
|
|
"peer_subs": [
|
|
"defi",
|
|
"ethfinance",
|
|
"CryptoCurrency",
|
|
"ethereum",
|
|
],
|
|
},
|
|
"dev_tool_cli": {
|
|
"patterns": [
|
|
"cli tool",
|
|
"command line tool",
|
|
"terminal app",
|
|
"dev tool",
|
|
],
|
|
"peer_subs": [
|
|
"commandline",
|
|
"programming",
|
|
"webdev",
|
|
],
|
|
},
|
|
}
|
|
|
|
|
|
def detect_category(topic: Optional[str]) -> Optional[str]:
|
|
"""Classify a topic into a known category by compound-term match.
|
|
|
|
Returns the category id (e.g. "ai_image_generation") or None if no
|
|
category's patterns match. Matching is case-insensitive substring over
|
|
the lowercased topic. Declaration order wins (first-match-wins), so the
|
|
map is ordered from most-specific to least-specific.
|
|
|
|
A None or empty topic returns None. Classification never raises on
|
|
normal string inputs; callers do not need to wrap in try/except for
|
|
typical paths, though defensive callers may.
|
|
"""
|
|
if not topic:
|
|
return None
|
|
lowered = topic.lower()
|
|
for category_id, entry in CATEGORY_PEERS.items():
|
|
for pattern in entry["patterns"]:
|
|
if pattern in lowered:
|
|
return category_id
|
|
return None
|
|
|
|
|
|
def peer_subs_for(category_id: Optional[str]) -> List[str]:
|
|
"""Return the priority-ordered peer subreddit list for a category.
|
|
|
|
Returns an empty list for None or unknown category ids. The returned
|
|
list is a fresh copy; callers may safely mutate it.
|
|
"""
|
|
if not category_id:
|
|
return []
|
|
entry = CATEGORY_PEERS.get(category_id)
|
|
if not entry:
|
|
return []
|
|
return list(entry["peer_subs"])
|