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# R.E.P.O. Survival Guide: All 29 Monsters, How Value Damage Actually Works & the Quota System

lianlele168 — Sat, 19 Sep 2026 05:30:28 +0000

R.E.P.O. Survival Guide: All 29 Monsters, How Value Damage Actually Works & the Quota System

A field manual for semiwork's co-op extraction horror — verified against the live game (Early Access v0.4.x, September 2026).

R.E.P.O. looks like a simple "grab loot, reach quota" game. Then the Huntsman hears you breathing through a wall and your roguelike run ends at the Taxman.

This guide compiles the full monster roster, how the value-damage system really works (it is the core economy of the game), and the quota rules — every entry checked against the live game and community trackers. Where the exact numbers aren't published by the developer, I say so instead of inventing them, because half the "guides" out there describe monsters and items that simply don't exist in the game.

1. The golden rule: your loot takes damage

Every valuable in R.E.P.O. — around 165 of them, each spawning only in specific locations — has a price cap. Every collision permanently knocks value off it: walls, door frames, drops, monster hits. The extraction truck pays whatever is left.

The rules of thumb that follow from this:

Fragile materials (glass, ceramic) lose far more value per hit than sturdy metal. A Diamond survives almost nothing; a Trophy shrugs off bumps.
Doors, stairs and corners are carry dead-zones — most value damage happens there, not in open rooms.
Small items are light but fragile; medium items can moonlight as weapons (use cheap ones — swinging a valuable damages it too); large items are the big money but need two players or Strength upgrades.
Some monsters (Reaper, Rugrat) attack your loot itself. Secure valuables before fighting them.

2. The quota system

Each level has a money quota split across extraction points — you'll see a 0/2-style counter from level two onward. Deposit loot into the extraction cart until the counter fills.
Quota climbs steeply with your level: community data puts levels 9-10 around $17k total across 3 extraction points.
Total loot on a map is usually 2-4× the quota, so route planning beats greed.
Extracting revives downed teammates — carry the body (head attached) to the truck.
Fail the quota or wipe, and the Taxman sends your run to Disposal. Roguelike save gone.

3. The monster roster (29 entities)

☠️ Extreme threat

Monster	HP	Damage	How to survive
Headman	250	50 + stun	Fast, but poor eyesight — crouch and break line of sight
Huntsman	250	100	Completely blind, hearing ~5× normal. Stay silent, toss objects to redirect him
Clown	250	100	Kill-beam in high/low arcs — jump the low beam, crouch the high one
Robe	250	100	Turns you to stone if you face it. Navigate by peripheral vision
Loom	500	100+25	Marks one player and hunts relentlessly — keep distance until it gives up
Trudge	500	100	Highest HP, one-swing decapitation, extremely slow — avoid its sightline

🔥 High danger

Monster	HP	Damage	How to survive
Cleanup Crew	350	10-75	Throws radiation-bombing heads — focus the body from range
Heart Hugger	300	30	The pink mist is the trap — avoid the cloud
Hidden	100	0	Invisible kidnapper — track breathing and footsteps
Mentalist	150	50	Telekinetic slaps + teleportation — don't brawl it

⚠️ Mid-tier

Monster	HP	Damage	How to survive
Chef	150	10/hit	Side-step the leap startup
Headgrab	150	5-15	Speeds up wearing a stolen player head — break the grab early
Oogly	200	4/hit	Flying searchlight — teammates can pull you free
Reaper	150	10/hit	Scythe scarecrow; its real target is your loot's value
Rugrat	150	varies	Grabs and throws your valuables — kill it before it reaches your haul
Banger	50	30 + knockback	Exploding skeletons — keep range
Gambit	150	varies	Roulette: healing, $5k, or 1 HP left. Good luck
Birthday Boy	150	10/0.25s	Do not poke the balloon
Bella	200	5/touch	Contact-triggered, not sound — touch it once, never twice
Bowtie	100	5 (pin)	Wind pressure pins you to walls
Tick	10-100	10/hit	Drains Grabber users; killed at full HP it drops a healing orb

🐤 Low threat / nuisance

Monster	HP	Damage	How to survive
Apex Predator	150	10	A duck. Do not click the duck
Elsa	600	5+20	Clingy dog — petting it actually calms it down
Peeper	50	2/sec	Ceiling eye locks onto your gaze — break eye contact within ~3s
Animal	150	2	Burrows onto you, stuns, damages items
Gnome	20	10	Swarms and attacks your loot — pick them up and yeet them
Shadow Child	150	30	Fades if you look away for 4 seconds
Upscream	50	10 + stun	Stun-lock screamer — 50 HP, it dies fast
Spewer	65	10	Face-pouncer; its vomit also hurts other monsters

4. New-player tips that actually matter

Energy is life. You start around 40 energy; sprinting, crouch-walking and heavy carries all drain it. Plan one clean loop per floor instead of backtracking.
First upgrades: Stamina, then Max Health. The community-agreed core buys. Strength comes next if you want to solo S-tier hauls.
Your microphone is positional — and monsters hear it. Proximity voice is great until the Huntsman hears you planning. Mute near threats, crouch to reduce noise.
Grab small high-value items first. Leave the Grandfather Clock for after Strength upgrades or a second pair of hands.
Active items make noise on pickup (Scream Doll screams, Music Box turns the camera). Clear the room first, warn your team.
A note on console commands: the internet is full of R.E.P.O. "console command" lists describing commands that don't exist. The real set is tiny — chat commands on the T key, a dev console behind tilde after enabling developer mode, and no built-in FPS counter (use Steam's overlay).

5. Tools

If you want planners instead of mental math, I built a few for our fan wiki — a quota split planner, a value damage planner, and the full monster roster. Unofficial, not affiliated with semiwork.

Safe runs — and remember: the duck is never worth it.

Data based on Early Access v0.4.x (September 2026). semiwork adds entities regularly; the exact per-collision decay rates are not published by the developer, so treat any precise decay math you see anywhere (including calculators) as estimates.

I Built 30 AI-Generated Game Wiki Sites in 45 Days. Google Rejected Every Single One. Here's the Post-Mortem.

lianlele168 — Sat, 19 Sep 2026 02:50:44 +0000

I Built 30 AI-Generated Game Wiki Sites in 45 Days. Google Rejected Every Single One. Here's the Post-Mortem.

The numbers, the three layers of failure, and the 78% hallucination rate I found when I actually audited my own data.

Between August 12 and September 10, I used AI coding agents to build a network of game wiki sites — Roblox game guides with interactive calculators, code lists, and tier lists — deployed on Next.js and Vercel, all hanging off one domain with subdomains.

The plan was simple: pick games with real search demand, ship useful tools faster than legacy wikis, let SEO do the rest.

Google indexed approximately zero pages from the entire network. Not "ranked poorly." Not "sandboxed for a few weeks." The indexing report read like a rejection letter.

This is the honest post-mortem: what the data said, the three separate layers of failure I found, and the uncomfortable discovery that scared me most — that when I finally audited my own content, most of it was fabricated.

The numbers

Here's what Google Search Console showed for the whole network (302 known URLs):

Status	Count	What it means
Crawled — currently not indexed	116	Google came, read the page, and said no
Discovered — not crawled	186	Google knows the URL exists and hasn't bothered to visit
Redirect error / duplicate clusters	~25	Various technical junk

Zero pages ranking. Meanwhile, Bing had already indexed at least 7 of the sites organically — same pages, same content. That contrast mattered later, because it ruled out "the content is unindexable" and pointed at something else.

The timeline was the most revealing part. The "crawled, not indexed" count sat at 16–19 pages through late August, then jumped from 19 to 107 in a single day (Aug 29), then flatlined at 116 for the next three weeks. I hadn't launched 88 sites that day — Google had simply finished sampling the network, decided what it thought of it, and applied that verdict wholesale. Sites it never sampled just stayed frozen in "discovered, never crawled," with crawl dates showing the epoch default: January 1, 1970.

Lesson 1: Google evaluates site networks as networks, not as pages. A sample of your pages becomes a verdict on all of them. If your first impression is a bad one, the other 80% of your pages don't even get a crawl.

Layer 1: Technical self-sabotage

Before blaming algorithms, I ran a full technical audit across every domain. What I found was embarrassing:

A canonical tag pointing at a domain that doesn't exist. One site's layout declared its canonical URL as a .com domain I had apparently planned to buy and never did. DNS said NXDOMAIN. Every page on that site was telling Google "the real version of this page lives at an unregistered domain." That's not a quality issue — that's a self-destruction switch.
Three sites with no canonical tags at all, because an earlier fix script had injected metadata into client components, which broke their builds. The builds failed silently for weeks while everyone assumed Vercel was just slow.
64 URLs inside sitemaps that redirected (trailing-slash mismatches between the sitemap generator and the server config). Every one burned scarce crawl budget on a 308 dance.
Pages marked noindex still listed in sitemaps. "Please index me" and "don't index me" in the same envelope.
Broken builds freezing deployments. Three sites served stale content for days because a regex-based cleanup script had mangled JSX syntax, and a static validation script couldn't catch what only a build would catch.

All of this is fixable in a sprint, and we fixed it: canonicals everywhere, sitemaps byte-identical to final URLs, noindex pages pulled from sitemaps, builds gated before every push. But I want to be honest about what it changed: nothing immediately. Technical health is a threshold, not a ranking factor. Cleaning it up removes self-inflicted rejection reasons; it doesn't create a reason to rank you.

Lesson 2: "It builds on my machine" is not enough. If your deployment pipeline doesn't fail loudly on build errors, you will ship a site that serves a stale snapshot for weeks and nobody notices.

Layer 2: The 78% hallucination rate

This is the part that should scare anyone shipping AI-generated content sites at scale.

Our code-list pages listed "working redeem codes" for each game. When I finally verified them against actual public code trackers — instead of trusting that the generated content looked plausible — 18 out of 23 sites had fabricated code tables. Roughly 63 invented code strings across the network.

The fabrication wasn't random. It fell into seven repeatable patterns, which I now use as an audit checklist:

Developer names as codes — "SUPERGAMING", "ASSEMBLERX"
Game title prefixes as codes — "SKYASSASSIN" for a game titled [SKY ASSASSIN] Jujutsu Shenanigans
Game mechanics as codes — "MONARCH", a trait name from the game, listed as a redeem code
Version numbers as codes — "UPDATE14", "FALLPREVIEW"
Real codes rewritten — "Origins" became "ORIGINS2026", "Release!" became "RELEASE"
Template filler — "XXX2026", "BETA_TEST_2025", "SUB2XXX"
Entire code systems invented for games that have none — including full "how to redeem" tutorials for games with no redemption feature. One game had never even launched.

Worst of all: a joke code in one game actually deducts 300 units of in-game currency, and our page told players it gives 300. Anyone who followed our advice lost money.

The tells were invisible from the outside. The pages looked confident, structured, complete. LLM-generated content doesn't look wrong — it looks exactly right until you check it against reality.

Lesson 3: You cannot audit generated content by reading it. The only reliable method is external verification: pull the entity, search for it in independent sources, and require agreement before publishing. We rebuilt every table this way — every code cross-checked against at least two independent trackers, and where no source documented a reward, the entry now literally says "reward not documented by the publisher" instead of guessing.

We also found that some of our "verified" data sources were inflating their own numbers — major outlets listing dead codes as working, with duplicate entries. Freshness of the source mattered more than the brand of the source.

Layer 3: Thirty thin subdomains, zero external trust

Even with perfect technicals and honest data, the network had a structural problem: 30 subdomains, each with 5–20 pages, sharing one root domain, with zero backlinks between them and the rest of the web. No community presence, no creators linking in, no directory listings. Nothing.

From Google's perspective, this is the textbook footprint of scaled content abuse — exactly what the March 2024 spam policies target. And no matter how good each page is, a site with no incoming trust gets a much higher bar for "worth indexing" than an established domain.

We hadn't earned a single external signal. We were asking Google to trust 300 pages from a domain nobody had ever linked to, powered by content we hadn't verified. The rejection was, in hindsight, reasonable.

Where it stands

The technical layer is now clean across all 29 domains. Every code table has been rebuilt from verified sources — and we open-sourced the result as a CC-licensed dataset of 527 verified codes across 19 games, including the verification methodology, so anyone building similar tools can skip the fabrication phase.

Has Google noticed? Too early — the re-crawl window is weeks, not days, and trust rebuilds slower than it collapses. The honest summary of where we are: we removed every reason for the rejection, but we haven't yet added a reason for the acceptance. That next layer is real external signals — community participation, creator links, content people actually cite — and it can't be generated by an agent. It has to be earned at human speed.

Five takeaways if you're doing this

Sample-based judgment is network-wide. Don't ship ten sites to "test" — the ten sites are the test, and a bad verdict spreads to everything else on the domain.
Verify generated data against external sources before shipping. "Looks right" is exactly what a confident hallucination looks like. An 80% fabrication rate is invisible from inside the document.
Canonical tags are load-bearing. One wrong canonical can hide an entire site from the index while every page returns 200.
Gate deploys on builds. A static check that doesn't compile your code will let a broken site serve stale content for weeks.
AI can generate the site, but not the reputation. The moat in a post-LLM-content world is precisely the stuff agents can't fake: first-hand data, real community presence, and links you earned.

The open dataset is here: github.com/lianlele168/roblox-codes-dataset — 527 verified codes, per-code source notes, CC BY 4.0. Examples of the rebuilt pages: Pressure's codes page (including the joke codes that deduct currency, which most trackers get backwards) and Fisch's codes page (135-entry expiry archive; that game kills weekly codes within 24 hours by design).

Building an Ad-Free, Interactive Wiki & Calculator Hub for Roblox Games with Next.js 14

lianlele168 — Thu, 10 Sep 2026 13:32:53 +0000

If you have ever searched for a game guide or item tier list for popular Roblox games, chances are you have encountered the official Fandom wikis.

While Fandom has vast amounts of community data, the modern browsing experience is often plagued with intrusive auto-playing video ads, massive layout shifts, and heavy page weights. More importantly, most game mechanics in RPGs and tower defense games require dynamic mathematical computation (such as luck formulas, drop rates, and DPS calculations), which static wiki tables simply cannot solve.

To solve this, I built Roblox Wiki Hub — an open, high-performance, ad-free ecosystem of interactive game guides and formula-driven web calculators built with Next.js 14, TypeScript, and Tailwind CSS.

Here is how the project was architected and the engineering decisions behind it.

🏗️ Architecture & Tech Stack

The platform is designed around a multi-subdomain architecture, allowing each major game community to have its own specialized, ultra-fast web tool:

Framework: Next.js 14 (App Router)
Rendering: Static Site Generation (SSG) with React Server Components for near-instant <100ms First Contentful Paint.
Styling: Tailwind CSS with custom dark-mode game UI themes.
Data Layer: TypeScript-typed game schemas with full JSON-LD structured data (FAQPage & SoftwareApplication) for optimal SEO and rich snippets.
Hosting: Edge deployment via Vercel with automated global CDN caching.

🧮 From Static Tables to Interactive Web Calculators

The biggest pain point in gaming wikis is calculating dynamic odds. For example, in fishing simulators or combat games, catching a rare secret item is not just a flat percentage — it depends on rod luck multipliers, bait enhancements, and weather conditions:

Mathematical Engine: Catch Rate = Base Chance × (1 + Rod Luck Multiplier × Bait Modifier)

Instead of asking users to do mental math, each dedicated wiki features interactive client-side calculator widgets.

By pre-rendering the database server-side and hydrating lightweight client components only where mathematical interaction is needed, our Lighthouse performance scores consistently hit 98-100/100.

🌐 Live Interactive Wikis & Tools

The platform currently covers 7 major Roblox game communities with deep item catalogs, verified stats, and custom calculators:

🎣 Fisch Wiki & Fishing Catch Rate Calculator — Complete fishing rod tier lists, bait luck matrices, and secret fish location guides.
⚔️ Blade Ball Wiki & Clash Speed Calculator — Sword rarity mechanics, clash speed multipliers, and ability tier ratings.
👁️ Pressure Wiki & Survival Room Calculator — Entity behavior database, node timer calculators, and Angler variant guides.
👗 Dress To Impress (DTI) Wiki & Star Rating Calculator — Runway theme color palettes, VIP clothing combos, and score optimizers.
👻 Type Soul Wiki & Soul Stat Optimizer — Shikai/Bankai progression pathways, weapon damage scaling, and skill trees.
🌀 Jujutsu Infinite Wiki & Cursed Technique Calculator — Domain expansion stats, cursed energy drain curves, and combo simulators.
🛡️ Anime Defenders Wiki & Unit DPS Calculator — Mythic unit upgrade costs, optimal placement matrices, and trait bonuses.

You can also explore the complete central portal at robloxwikihub.com.

🚀 Open Source & What's Next

The core codebase and data schemas are maintained transparently. You can check out the repository, release notes, and contribution guidelines on GitHub:
👉 GitHub Repository: lianlele168/roblox-wiki-hub

Our roadmap includes:

Adding real-time trading value indexes driven by community feedback
Progressive Web App (PWA) offline support for mobile gamers
Expanding to upcoming trending titles like Rivals and Blox Fruits updates

I'd love to hear feedback from the Dev.to community on the UI/UX performance, data structuring, or calculator mathematics!