Whoa! I was in the weeds last week, staring at a web of SPL transfers and memos, trying to untangle a messy NFT drop. It felt like following a trail of breadcrumbs through a busy interstate. My instinct said: start at the transaction level and work upward. Hmm… that gut reaction usually gets me 70% of the way there, fast.
Initially I thought the best route was to check a wallet balance and call it a day, but then I realized how often balances lie about recent activity. Actually, wait—let me rephrase that: balances tell you the result, not the how or why. On one hand you see lamports shifted; on the other hand, there might be inner instructions, CPI calls, or program logs hidden in plain sight. So you have to open a transaction and read the narrative that the chain left behind.
Start with the basics: transactions, accounts, and instruction traces
Here’s the thing. A Solana transaction is more than just “from” and “to.” There are multiple instructions, possibly multiple programs, and sometimes a bunch of token transfers bundled together. Seriously? Yes. The details live in the transaction JSON: signatures, recent blockhash, account keys, and instruction arrays. Those instruction arrays are where you find program IDs like the Token Program, Metaplex, or a custom marketplace contract.
Check the “inner instructions” and “log messages.” Those reveal CPIs (cross-program invocations) that matter for NFTs—like when a marketplace escrow moves a token then releases payment. When you see compute units spike or repeated failed logs, something felt off about that transaction. You can also spot retries, and sometimes very subtle reentrancy-like patterns (not reentrancy in the EVM sense, but nested calls that change state unexpectedly).
For practical day-to-day: copy the tx signature and paste it into an explorer. I usually use solscan because it surfaces token transfers, token mints, and decoded instructions cleanly. Check the block time, slot, and confirmation status. If the tx is unconfirmed or stuck, you get a different set of signals than a finalized one.
Why NFT explorers are different — metadata matters
NFTs are quirky. The token mint tells one story; metadata tells another. The on-chain mint points to metadata accounts (sometimes via Metaplex), and those metadata JSON blobs often contain off-chain URIs. Some projects put real metadata on-chain; many don’t. I’m biased, but I trust on-chain references more. When you trace a sale, follow the mint → metadata → creators flow. It makes provenance obvious.
Also, token accounts are ephemeral. A buyer’s token account can be empty after a transfer even though ownership changed. So look for recent token account history. Check royalties: are creators still set as recipients? On Solana, royalty enforcement is mostly marketplace-driven, so you’ll see revenue split instructions in some transactions but not others. This part bugs me—royalties are messy and inconsistent across marketplaces.
Filters, program IDs, and the detective work
In practice, I run filters: program ID equals Token Program, or equals Metaplex Metadata. Then I filter by mint address or by a wallet address. That narrows the noise. It’s surprisingly effective. You can also watch for specific instruction types—InitializeAccount, TransferChecked, or ApproveChecked—because they indicate different behaviors.
One trick: follow the memo instructions. People and programs stick notes there. Developers leave debug memos. Marketplaces tag payments with references. A memo can point you to an off-chain order ID or a Discord user. It’s not official, but it’s human. And humans make mistakes—double posts, typos, somethin’ like that—so memos often reveal intent.
Oh, and check rent-exemption events. When someone creates multiple token accounts in one tx, I raise an eyebrow. It might be batch minting. Or bots. Bots leave trails: rapid consecutive transfers, similar compute unit footprints, and identical instruction shapes. When I see that, I start thinking bot farm, not organic collector activity.
Tools and UI features that actually help
Explorers that decode instruction data for you save hours. Another useful feature is the timeline view—seeing the sequence of events as they happened, not just a flat list. Also, token holder lists with enrichment (profile names, linked marketplaces) speed up research. Filters for date ranges and slot ranges are very very important when you’re reconstructing an incident.
Check the transaction logs for errors and “Program log:” lines. Those are gold. They tell you what the program attempted, and where it bombed (if it did). When a marketplace tries to move an NFT but fails midway, you’ll often find a partial state change or a failed refund path—these are subtle, but show up in logs. If you want to see all that rendered nicely in an explorer, try opening a decoded view.
For hands-on tracking I recommend using a reliable explorer like solscan. It highlights token transfers, decodes instructions, and surfaces metadata links in a tidy way, which means you spend less time squinting at raw JSON. It’s not perfect, though—sometimes it misses custom program decoders—but it’s a solid starting point.
Advanced signals: inner transactions, compute units, and program logs
Watch compute unit consumption. High compute usage paired with many inner instructions usually means complex logic—marketplace escrow, royalty splits, multiple CPIs. If compute spikes unexpectedly, the tx might be doing a lot off-chain calls translated into on-chain steps, or it might be a clever gas-optimizing vector you don’t expect.
Also: look for failed preludes. Occasionally a transaction will include a no-op preliminary call that probes a state. That sign, combined with a follow-up successful call, suggests probing or reconnaissance by a bot. I’m not 100% sure every time, but pattern matching across many txs sharpens that intuition.
Common mistakes and how to avoid them
People assume “token transferred = sale.” Not always. Transfers can be internal reorgs, gifts, or deposits to a marketplace’s custody account. Also, wallet labels can mislead—someone might reuse a wallet name for marketing. So cross-verify with signature-level data.
Another mistake: forgetting to check the mint’s freeze authority or supply. Some “NFTs” are actually fungible or have mutable supply. If the mint authority still exists, the project can mint more. That changes valuation and provenance, fast.
FAQ
Q: How do I confirm a sale happened and not just a transfer?
A: Look for two things together—payment transfer (usually SOL or stablecoin) and a token transfer tied to the same transaction (or tightly linked consecutive transactions), plus marketplace program instructions or memo tags. If the payment is to a marketplace escrow account, and then an instruction releases the token, that’s a sale pattern.
Q: Can I rely solely on explorers for forensic work?
A: Nope. Explorers are great, but for deep forensic work you should pull the raw transaction JSON from an RPC node and inspect inner instructions and logs yourself. Explorers decode a lot, but they can miss custom programs or obfuscations. Use both: explorer for speed, raw data for depth.
Okay — final thought. Tracking Solana activity is part forensics, part pattern recognition, and part walking through a story left by programs and people. It’s satisfying when the pieces click. I’m biased toward explorers that surface instruction-level details quickly, but I’m also pragmatic: sometimes a quick wallet search and a memo check answers the question. Keep your tools sharp, your instincts engaged, and expect surprises… because they arrive often, and they teach you more than success does.
Reporter. She loves to discover new technology.
