In Shinsuke Kagawa’s account, a short coding-agent session exposed three defects in his repository’s instructions: mandatory steps that were no longer part of the workflow, a nonexistent tool name, and competing rules for deciding what should run first. The example is a useful reminder that a failed run can reveal problems in the setup itself—not just in the prompt.
What the session revealed
Kagawa says he used Session Doctor to examine a small, two-turn session in one of his repositories. The file at the center of the case was a legacy CLAUDE.md, despite the broader topic being agent instruction files such as AGENTS.md. His account identifies three distinct problems.
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Mandatory steps that had gone stale
The file required a task breakdown, a date check, and a call to rule-advisor before any work, using the wording “required for all work, no exceptions.” Kagawa says date retrieval had been removed from his workflow in July and rule-advisor in September, but the file still required both. In the session he examined, none of the specified steps ran, and the task nevertheless completed correctly.
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A tool name that could not work as written
The same file named mcp__local-rag__query_documents. Kagawa says the actual tool identifier was mcp__mcp-local-rag__query_documents. An agent following the written name literally would call a nonexistent tool rather than the configured one.
Two rules competing to control startup
The file said its startup gate ran first, without exceptions. But its routing rule directed the trigger to a skill that did not contain that gate. Kagawa reports that the skill won in the session and that the decision was not recorded. The issue was not simply a missing step: two sources disagreed about which instructions governed the start of the task, leaving the outcome difficult to inspect afterward.
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Why Kagawa says better instruction following raises the stakes
Kagawa’s explanation is that earlier models often failed to follow instructions, and those failures could conceal contradictions in the surrounding setup. As instruction following improves, he argues, agents may be more likely to act on conflicting directions or carry out obsolete mandatory steps. In that view, stronger compliance makes the quality and consistency of workflow instructions more consequential.
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That is Kagawa’s interpretation of his experience, not a general finding established by a controlled study. The case shows how these defects appeared in one repository and one session; it does not establish how often they occur elsewhere.
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How the workflow changed over time
Kagawa describes building scaffolding when models needed it, then removing some of it as model behavior changed. He reports removing date retrieval from six agents in v0.23.0, deleting the rule-advisor agent and task-analyzer skill in v0.26.0, and removing a recipe while reducing planning templates in v0.24.0.
He also reports 133 releases since October 2025. That is a project-history figure attributed to Kagawa in 2026, not an independently checked release count or a measure of effectiveness. The three defects in the session are consistent with a maintenance risk in that kind of evolving setup: workflow behavior can change while older instructions remain in place.
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Diagnose the bad run, not a replacement prompt
Kagawa’s advice is to keep the failed session and trace what happened back to the artifact or mechanism that produced it. In his words, “The session worth diagnosing is the one that went badly, and the instinct is to close the tab and start over with a better prompt.” He adds: “That throws away the best record you have of how the thing actually failed.”
A useful diagnosis should connect an observed event to the instruction or routing behavior behind it, then recommend a small change to that mechanism. For this case, that means checking the current instruction file against the steps actually used, verifying tool identifiers against the configured names, and resolving which rule controls task startup. Rewriting the prompt alone would not correct a stale file or an ambiguous routing rule.
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Using Session Doctor on a saved session
Kagawa describes Session Doctor as a tool for reading a saved Claude Code or Codex session and reporting potential changes across three separate passes. He says each finding identifies where it occurred, how it changed the run, and a small change that might prevent a repeat. These are the maintainer’s descriptions; the account does not provide independent performance testing or comparisons with other diagnostic tools.
Install it
The commands below are the installation instructions reported in Kagawa’s article. Plugin commands and availability can change, so check the project’s current documentation before using them.
Claude Code
/plugin marketplace add shinpr/agent-clinic
/plugin install session-doctor@agent-clinicCodex
codex plugin marketplace add shinpr/agent-clinic
codex plugin add session-doctor@agent-clinicRun the diagnosis
Use /recipe-diagnose in Claude Code or $recipe-diagnose in Codex. Without an argument, Kagawa says the tool selects the most recent session in the repository and asks for confirmation before starting. If that is not the failed run you want to inspect, select the intended saved session using the tool’s current instructions rather than diagnosing a fresh replacement session.
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- It can show how stale mandatory steps, a mistyped tool identifier, and conflicting startup rules appeared together in one author-reported repository session.
- It cannot establish that the same defects are common across coding-agent workflows, that Session Doctor reliably prevents recurrence, or that one diagnostic tool performs better than another.
- The practical lesson is to preserve the original run and use it to identify which instruction, routing rule, or workflow artifact needs correction.
Source for Kagawa’s account and project details: Shinsuke Kagawa’s article.
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