Anthropic's 4 New Rules for Prompting Claude 5 Models

The video opens with a sharp claim: most people are using the Claude 5 generation the wrong way. Opus 5 and Fable 5.1 class models work fundamentally differently from their predecessors, so old prompting habits fail to unlock their real power. Eric Tech studied notes from Claude Code lead Boris Cherny plus Anthropic documentation and distilled everything into four practical rules.

Rule one is auditing existing instructions and skill files. The logic is straightforward: Fable 5.1 running on skills written for the Opus 4.6 era stays trapped in old behavior despite its higher intelligence scores. Since the model reads much of its operating procedure from the skill content, stale lines drag the new model down. The fix is keeping the useful context while removing the lines that hold performance back.

The audit runs in practice as a single command: you pass the target model name and the skills folder, and the tool scans everything and returns a report with proposed fixes. In the on-screen demo, 212 skills get scanned; most are fine, while 14 broken and 1 outdated file get flagged. Eleven of the broken ones are commands pointing at a folder that no longer exists, and four are stale contents that no longer fit the new model. After confirmation, 13 fixes get applied and the repo is clean.

Two companion tools get mentioned. The first is a setup doctor command that finds broken installs, duplicate setups, slow hooks, and repeated content in the system prompt. The second is output style control: asking the model for condensed output instead of long essays, which is both easier to read and cheaper because shorter responses consume fewer tokens.

Rule two is giving the complete task context up front. Asking for a page or a research piece with a half-written brief forces the model to guess: who is it for, what is being sold, what counts as done? The Anthropic guide stresses the same point: the best results come from a full task specification followed by room to execute. Cherny draws the same line: write down the outcome, the reason, the guardrails, and the exit criteria, then let the model run and step away for a while.

The rule compresses into a one-prompt formula: outcome, reason, and guardrails merged into a single instruction. Answering the same three questions before every task and leaving execution to the model is presented as the way to get the most out of it. The point is stressed that this approach would not have worked six months ago but performs surprisingly well on current models.

Rule three is closing the unknowns before implementation. A map analogy is used: traveling from point A to point B requires knowing the roadblocks in advance. The solution is a relentless interview skill that walks the plan branch by branch across decision trees until both sides share the same understanding. Once decisions are settled, implementation accuracy rises markedly.

Rule four is matching model effort to task complexity. The environment offers effort tiers such as low, medium, high, extra high, and max, with high as the default. The documentation warning is clear: never spend a high tier on a simple task, and never starve a complex task with a small tier. The suggested test is simple: run the same task at low, medium, and high effort, and if the results barely differ, stay at the lower tier. The payoff is faster responses and lower token spend.

The video is backed by official Anthropic guides: the general best-practices page, the Opus 5 specific page, and the business performance piece all confirm the same principles. On the community side, the official post on prompting Fable 5 and Opus 5 sparked heavy discussion. The full resource list in the description plus the related playlists offer a roadmap for viewers who want to go deeper.

The wrap-up lands in four lines: audit old instructions, give full context up front, let the model question you before building, and pick the effort tier that fits the job. All four share one insight: with the new generation, victory goes not to whoever writes the longest prompt, but to whoever prepares the cleanest setup and states the clearest goal.