Detailed Analysis
A Reddit post to r/Anthropic captures a specific and recurring frustration among Claude power users: Opus, even after being explicitly instructed to build and use a reusable "skill" for a workflow, frequently reverts to solving the same problem from scratch on subsequent tasks. The post, formatted as a sarcastic dialogue transcript, walks through the full arc of the complaint—Opus completes a complex task by installing packages, writing Python scripts, and chaining tool calls; the user asks it to package that workflow into a reusable skill; Opus does so, testing it and confirming it works; and then, on a later and ostensibly similar task, Opus ignores the skill entirely and re-executes the original slow, token-heavy process from scratch. When confronted, the model reportedly acknowledges the mistake, burns additional tokens "formulating a plan" to fix its behavior, and then repeats the same error on the next task.
The complaint centers on Anthropic's "Skills" feature, a mechanism designed to let Claude persist and reuse learned workflows—scripts, tool sequences, and procedures—within a project so that repeated task types don't require the model to reconstruct a solution from first principles every time. In principle, this should make Claude both faster and cheaper to run: a purpose-built skill executing in seconds is dramatically more efficient than an agent re-deriving a multi-step tool chain, complete with package installations and exploratory scripting, on every invocation. The post's frustration is precisely that this efficiency promise is being undermined by inconsistent adherence—Claude appears to "know" the skill exists, will even affirm it should have used it when asked, but doesn't reliably invoke it proactively during actual task execution.
This matters because it exposes a gap between Claude's stated agentic capabilities and its practical reliability in long-running, stateful, multi-session projects. Users are increasingly using Claude not as a single-shot chatbot but as a persistent coding and automation collaborator across sessions, expecting it to accumulate and apply institutional knowledge the way a competent engineer would reuse a utility library rather than rewriting it each time. When the model fails to consult its own prior work despite explicit instructions to "use skill for all new sessions," it directly undercuts the economic case for agentic AI: token costs balloon, latency increases, and users are forced into an awkward supervisory role of constantly reminding the model of its own tools—work that defeats the purpose of automation.
The episode also reflects a broader tension in current large language model agents: the difference between a model's declarative knowledge (it can articulate that it should use the skill) and its procedural execution (it doesn't reliably invoke it during autonomous operation). This "knowing-doing gap" is a known challenge in agentic AI generally, not unique to Anthropic, and it's a central obstacle as companies push toward more autonomous, long-horizon agents that manage their own memory, tools, and workflows. The complaint fits into a wider pattern of community feedback around Opus and Claude models regarding tool-use consistency, instruction-following degradation in extended contexts, and the perceived mismatch between marketed "agentic" capabilities and observed behavior—an ongoing pressure point as Anthropic and competitors race to make AI agents dependable enough for unsupervised, production-grade use rather than requiring constant human correction.
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