Debugging Claude Code Skills: Loops, Bad Tool Calls, Fixes

The first time a Skill you wrote sends Claude into a tight loop — reading the same file, deciding it needs more context, reading it again — you stop trusting the magic and start wanting a debugger. Agentic systems fail differently than ordinary programs. There's no stack trace pointing at line 42. Instead you get a transcript: a sequence of model decisions, tool calls, and tool results, where the bug is usually a missing instruction rather than a broken function. This post is a practical guide to the failure modes we hit most when building Skills for Claude Code, and how to find and fix them.

What actually breaks in an agentic run

A Skill is a folder of instructions, scripts, and resources that Claude loads dynamically when a task looks relevant. That dynamic loading is what makes Skills powerful, and also where most bugs originate. The model isn't executing your code; it's reading your instructions and deciding what to do. So the bugs cluster into a handful of recognizable shapes.

The biggest three: loops (the agent repeats an action without making progress), wrong tool calls (it picks a tool that can't accomplish the step, or calls the right tool in the wrong order), and hallucinated arguments (it invents a file path, an ID, or a flag that doesn't exist). Underneath these sit two slower killers: context drift, where the model loses track of the original goal after many turns, and silent success, where a tool returns an error the model ignores and it confidently reports a job done that never happened.

None of these are random. Each maps to a concrete gap in how the Skill was written. Loops usually mean the success condition is undefined — the model has no way to know it's finished. Wrong tool calls usually mean two tools have overlapping descriptions. Hallucinated args almost always mean the model never had the real value and the Skill didn't tell it to go fetch one. Once you internalize that mapping, debugging becomes systematic instead of mystical.

Read the transcript like a stack trace

The transcript is your primary debugging surface. For any failed run, walk it turn by turn and ask one question at each tool call: did the model have everything it needed to make this decision correctly? If the answer is no, you've found your bug — and it lives in the Skill, not in the model.

Here is the decision flow we use when triaging a broken Skill run.

flowchart TD
  A["Run failed or stalled"] --> B{"Same action repeated?"}
  B -->|Yes| C["Loop: success condition undefined"]
  B -->|No| D{"Tool call rejected or errored?"}
  D -->|Yes| E{"Args invalid?"}
  E -->|Yes| F["Hallucinated arg: Skill never supplied real value"]
  E -->|No| G["Wrong tool: overlapping tool descriptions"]
  D -->|No| H{"Final answer wrong but no error?"}
  H -->|Yes| I["Silent success: model ignored a failing result"]
  H -->|No| J["Context drift: goal lost over long run"]

Two habits make this fast. First, log the full tool result, not a truncated version — many silent-success bugs are hidden in a stderr line the model technically saw but you can't, because your logs cut it off. Second, look at the last good decision before things went sideways. The failure is rarely where it becomes visible; it's a few turns upstream where a tool returned ambiguous data and the model guessed.

Breaking loops

Loops are the most demoralizing failure because the agent looks busy. The cause is almost always a missing or fuzzy stopping condition. The model keeps gathering information because nothing told it when enough is enough. The fix is to write the success criterion explicitly into the Skill: "You are done when the test suite passes and you have summarized the change. Do not re-read files you have already read in this run."

Add a concrete budget where it helps: "Attempt the fix at most twice. If it still fails, stop and report what you tried." This converts an open-ended loop into a bounded one with a defined exit. For loops driven by a flaky tool — say, a flapping API that returns transient errors — instruct the model to treat a repeated identical error as terminal rather than retryable. A surprising number of "infinite" loops are really three retries the model is too polite to give up on.

When a loop survives those fixes, it's usually structural: the task is too big for one agent to hold in working memory, and it keeps re-deriving the same plan. That's a signal to split the work into subagents with narrower, self-contained jobs, each with its own clear finish line.

Wrong tool calls and tool description hygiene

When Claude reaches for the wrong tool, the model is rarely the problem — your tool surface is. If two tools have descriptions that both sound right for the step, the model is guessing, and it will guess wrong some fraction of the time. The cure is description hygiene: every tool gets a one-line statement of exactly when to use it and when not to. "Use search_orders to find an order by customer email. Do not use it to fetch a known order ID; use get_order for that."

Ordering bugs are subtler. The model calls a valid tool, but before a prerequisite is satisfied — querying a record before authenticating, editing a file before reading it. Encode the sequence in the Skill as an explicit recipe: "Always read a file before editing it." Claude Code itself enforces some of these as hard preconditions, and that pattern is worth copying in your own MCP tools — make the tool refuse and return a helpful message instead of failing obscurely.

Hallucinated arguments

A hallucinated argument is the model supplying a value it never actually obtained — a plausible-looking file path, a record ID, a config key. It happens because the model would rather produce a confident guess than admit a gap. The structural fix is to never let a required value be guessable: the Skill should instruct the model to obtain the value from a tool first, then pass it through.

Concretely, replace "update the user's record" with "first call find_user to get the user ID, then pass that exact ID to update_user; never construct an ID yourself." On the tool side, validate aggressively and return errors the model can act on: "No order found with ID 'abc'. Did you mean to search by email first?" A good error message turns a hallucination into a self-correcting next step, which is exactly what you want an agent to do.

Building a debugging loop into the Skill itself

The most resilient Skills assume they will sometimes be wrong and tell the model how to recover. Bake in a verification step: after making a change, run the check that proves it worked, and read the result before declaring success. This single instruction eliminates most silent-success failures, because the model is now forced to look at reality instead of its own optimism.

Pair that with a short "if you get stuck" clause — a fallback path for the common dead ends, so the model has somewhere to go besides looping or guessing. Over a few iterations, your Skill stops being a list of instructions and becomes a small, robust control loop: act, observe, verify, recover. That is what separates a demo that works once from a Skill you can trust on a Monday morning.

Frequently asked questions

Why does Claude keep calling the same tool in a loop?

Almost always because the Skill never defined when the task is finished, so the model keeps gathering more context. Write an explicit success condition and a retry budget into the Skill, and instruct it not to repeat an action that already returned the same result.

How do I stop the model from inventing file paths or IDs?

Don't let required values be guessable. Tell the Skill to fetch each value from a tool before using it, and have your tools return actionable errors when an argument looks invented, so the model corrects itself instead of pushing forward on a hallucination.

What is the best way to debug an agentic failure?

Read the transcript like a stack trace. At each tool call, ask whether the model had what it needed to decide correctly. The real bug is usually a few turns upstream of where the failure became visible, in an ambiguous tool result the model guessed past.

Why does Claude report success when nothing actually changed?

This is silent success: a tool returned an error the model glossed over. Fix it by adding a mandatory verification step — run the check that proves the work happened and read the result before reporting done.

Bringing agentic AI to your phone lines

The same debugging discipline — clear stopping conditions, clean tool surfaces, and a verify-before-you-claim loop — is what keeps a live phone agent from talking in circles. CallSphere applies these agentic patterns to voice and chat, so every call is answered, every tool is used at the right moment, and work gets booked correctly. See it live at callsphere.ai.

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Source & attribution: This is an independent, original explainer inspired by Anthropic's coverage on the Claude blog. Claude, Claude Code, Claude Cowork, Claude Opus, and the Model Context Protocol are products and trademarks of Anthropic. CallSphere is not affiliated with or endorsed by Anthropic.