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Hollow Knight Silksong: Mastering the Shaman Crest in 2026 Hollow Knight: Silksong Shaman Crest guide: unlock it with Silk Soar in Act 3 and unleash three devastating Silk Skills. Among the seven crests in Hollow Knight: Silksong, six can be acquired during the first two acts. The seventh—the Shaman Crest—is a late-game treasure that only appears in Act 3, and only after players have obtained the Silk Soar ability from the depths of The Abyss. This crest is widely regarded as the most powerful of the bunch, and for good reason: it allows Hornet to equip three different Silk Skills simultaneously, turning her into a veritable tornado of steel and silk. To put it in perspective, it's like giving a concert pianist three extra hands, each playing a different melody that somehow harmonizes into pure devastation. Or, if one prefers, imagine a chess grandmaster who can make three moves at once—bosses never stand a chance. When paired with blue tools like the Volt Filament, the Shaman Crest becomes even more deadly, as the added electric damage synergizes beautifully with rapid-fire Silk Skills. Before players can claim the Shaman Crest, they must have unlocked Hornet's Silk Soar ability. Assuming that prerequisite is met, the journey begins at the Bone Bottom Bellway. From there, players should head left until they come to a hole with a ray of light shining down on it. Once there, they can drop down and head inside the doorway to the Ruined Chapel on their left. Inside the Ruined Chapel, players should head to the far right and stand in front of the lore marker. Then, they need to use Silk Soar by holding down together with the harpoon button (S on PC, L2 on PlayStation, LT on Xbox, or L on Switch). This action propels Hornet through the ceiling above her, allowing her to carry on to the right. From here, it's a simple case of following the path, while being sure to destroy the three breakable walls using slash attacks along the way. Eventually, players will drop down into an area with a destroyed statue. Standing in front of this will allow them to hold down to claim, after which they'll just need to tap the Bind button a few times to unlock the Shaman Crest. The moment of acquisition is shown below. To begin with, the Shaman Crest only has three white slots unlocked. However, by using a couple of Memory Lockets, players can unlock two blue slots. If they haven't already done so, they can add a yellow slot and one additional blue slot by getting the two Vesticrests from Eva in Weavenest Atla. Here's a quick breakdown of the slot progression: Slot Type Initial After Memory Lockets After Vesticrests White 3 3 3 Blue 0 2 3 🟡 Yellow 0 0 1 With the full array of slots unlocked, the Shaman Crest transforms Hornet into a one-woman army. The ability to wield three Silk Skills at once means players can chain devastating attacks, control crowds, and melt boss health bars. It's the difference between bringing a spoon to a sword fight and bringing a Swiss Army knife that also happens to be a flamethrower—except even that analogy undersells it. Think of the Shaman Crest as a skeleton key that unlocks a door behind which sits a personal battalion of silk-wielding warriors, all answering to Hornet's command. In 2026, the Shaman Crest remains a game-changer for Silksong enthusiasts. Whether players are veterans of the first game or newcomers to the sequel, mastering this crest is essential for tackling the toughest challenges that Act 3 and beyond have to offer. So go forth, claim that crest, and watch as Hornet becomes an unstoppable force of nature. ️ Maxing out the Shaman Crest means farming Memory Lockets and tracking down the Vesticrests from Eva, and that kind of prep work often pushes players to look for ways to stretch their gaming budget a little further. If you're planning to pick up Hollow Knight: Silksong on another platform, grab the original game for a fresh playthrough, or stock up on similar Metroidvania titles to fill the wait between Acts, it's worth keeping an eye on where prices drop before committing. Comparing storefronts and watching for limited-time discounts can save you enough to justify that extra DLC or soundtracks you've been eyeing. That's where a deal-tracking site like DealNest comes in handy, rounding up the [best game deals]( across platforms so you don't have to refresh a dozen store pages yourself. Whether you're chasing a discount on the game itself or hunting for the next great adventure to sink dozens of hours into, having a curated list of current offers makes the search far less tedious—and leaves more time for perfecting your Hornet combos. https://dealnesthq.com

This question comes up constantly once organizations complete an accessibility audit. Someone runs a scan, finds out they have 30,000+ PDFs sitting in a document library or LMS, does the math on manual remediation, and realizes that finishing everything one document at a time could take years. So how are organizations actually getting through this without a multi-year backlog? A few patterns show up consistently in the ones that pull it off. 1. They stop treating every PDF the same Not all 30,000 documents are equal. The teams that move fast triage first: High traffic, high complexity (course materials, public forms, financial reports) → gets full attention, often human-reviewed High traffic, low complexity (simple flyers, single-column text docs) → near-fully automatable Low traffic, low complexity → automate and batch, don't overthink it Low traffic, rarely accessed archival content → may legitimately qualify for an archive exception depending on the applicable regulation, instead of full remediation Doing this sorting up front is what turns "30,000 documents" into "actually, 22,000 of these are simple and can go through automation almost untouched." 2. AI handles the mechanical 80%, humans handle the judgment 20% This is the actual unlock. Today’s modern document remediation platforms use AI to automate: Structural tagging (headings, lists, tables of contents, reading order) Alt text generation for images at scale, including specialized content like STEM diagrams, charts, and math notation Table structure detection and tagging Form field tagging Some platforms report automating roughly 90% of the tagging work this way. That's the difference between "a person opens each file and manually tags every element" and "a person reviews and approves what the system already did." One is measured in minutes per document; the other in hours. Humans still matter, reviewing AI-generated alt text for accuracy, handling genuinely irregular layouts, and doing final QA before anything ships. But they're reviewing, not building from scratch. 3. They stop outsourcing 1:1 and start batching Sending every file to a vendor for individual manual remediation doesn't scale any better than doing it in-house manually, you're still paying for human hours per document, just someone else's. Orgs that get through large backlogs faster typically use a hybrid model: self-service automated remediation for the easy majority, with outsourced expert remediation reserved for the genuinely complex minority (scanned documents, dense multilingual content, heavily irregular tables). 4. They build it into the pipeline instead of doing a one-time sprint The backlog only gets bigger if new inaccessible documents keep getting added while you're working through the old ones. Orgs that actually close the gap add automated remediation/tagging at publish time going forward, so the "thousands of PDFs" problem doesn't regenerate itself every semester or fiscal year. What this looks like at scale, in practice Publishers dealing with legacy content libraries in the millions of documents have reported turnaround times cut significantly and throughput increased by an order of magnitude when shifting from manual, post-production fixes to AI-driven workflows integrated earlier in the content pipeline, this is showing up right now with publishers racing to get ahead of the ADA Title II vendor requirements. Similarly, government and municipal document libraries, think meeting minutes, budget reports, public forms, are increasingly being processed this way rather than farmed out page-by-page. We've seen more organizations moving toward AI-assisted remediation because manual workflows simply aren't keeping up with document volume. Is that what you're seeing too?