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Shinsuke Kagawa says a short coding-agent session exposed three defects in his repository’s instructions: obsolete mandatory steps, a misspelled tool identifier, and conflicting rules about what should control task startup. The file at the center of his example was actually CLAUDE.md, not AGENTS.md. His account offers a practical lesson for maintaining agent workflows: when a run goes wrong—or succeeds despite broken rules—inspect the setup that shaped it, rather than treating the prompt as the only thing to fix.
What Kagawa says happened in the session
Kagawa reports using Session Doctor to examine a small, two-turn session in one of his repositories. He describes three problems in the repository’s legacy CLAUDE.md. These are his account of that session, not an independent audit of the repository or a controlled test.
1. Mandatory steps had become obsolete
The file required a task breakdown, a date check, and a rule-advisor call before any work, describing them as “required for all work, no exceptions.” Kagawa says the date-retrieval step had been removed from his workflow in July and rule-advisor in September, but the instruction file remained unchanged. In the session he examined, none of the three steps ran and the task still completed correctly.
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The file called for mcp__local-rag__query_documents. Kagawa says the actual identifier was mcp__mcp-local-rag__query_documents. An agent following the documented name literally would be unable to call the intended tool.
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3. Two rules disagreed about what controlled startup
One direction said the file’s gate ran first without exceptions. Another routed the trigger to a skill that had no such gate. Kagawa says the skill won in the session and the decision was not recorded. That left a consequential routing outcome invisible in the workflow record.
Why a successful task can still expose a broken setup
In Kagawa’s interpretation, earlier models’ weaker instruction-following sometimes concealed contradictions: an agent might simply fail to carry out a rule. As instruction-following improves, he argues, an agent may instead execute an obsolete mandatory step or act on incompatible directions, making instruction quality more consequential. This is his explanation of the case, not an independently established finding about model behavior.
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The session mattered even though the task completed. It showed that the written procedure no longer described the active workflow, that one documented tool call could fail, and that the system had no clear, recorded resolution for competing startup rules. A completed task is not proof that its instructions are sound.
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How to diagnose a failed coding-agent run
Kagawa’s approach is to preserve the run, identify the behavior that went wrong, trace it to the workflow mechanism that produced it, and change that mechanism. The goal is not just to coax a better answer from the next prompt; it is to prevent the same setup defect from recurring.
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- Keep the problematic session. The original run contains evidence of which instructions were available and how the agent responded. Kagawa writes: “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.”
- Trace behavior to its source. Check the instruction file, referenced tools, skills, routing rules, and other workflow artifacts that could have caused the observed behavior. Distinguish a stale instruction from a tool-name mismatch or an unresolved conflict; each needs a different correction.
- Change the mechanism that failed. Update or remove obsolete requirements, correct identifiers, and make precedence or routing explicit. A revised prompt alone will not repair a repository instruction that still directs later runs to repeat the same mistake.
- Check what the fix changes. After editing, verify that the documented procedure matches the current workflow and that the relevant route or tool can be used as intended. Avoid treating a single successful run as proof that the wider setup is correct.
What Session Doctor does, according to its author
Kagawa presents Session Doctor as a tool that reads a saved Claude Code or Codex session and reports potential changes in three separate passes. He says each finding identifies where an issue occurred, how it changed the run, and a small change that may prevent a repeat. The account does not provide independent performance testing or a comparison with other diagnosis tools.
To try it, the article gives these installation commands:
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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-clinicThen run /recipe-diagnose in Claude Code or $recipe-diagnose in Codex. With no argument, the tool selects the most recent session in the repository and asks for confirmation before starting. Installation steps and command availability can change, so check the project documentation for current instructions before installing.
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Kagawa describes his setup as something that changed alongside the models he used: he added scaffolding when he believed it was needed, then removed steps as behavior changed. He reports “133 releases since October 2025” in 2026. That is an author-reported project-history figure, not an independently checked release count or a measure of effectiveness.
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His examples include 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. The mismatch between these changes and the lingering CLAUDE.md requirements illustrates a maintenance risk: workflow components can change faster than the instructions that describe them.
What this case does—and does not—show
The useful takeaway is specific: inspect the instructions and routing behind an agent run, including runs that appear to succeed. Kagawa’s reported session illustrates three plausible failure mechanisms—stale requirements, an unusable identifier, and conflicting startup rules—and a way to investigate them by retaining the original run and tracing behavior to its cause.
One author’s case does not establish how often these problems occur, prove that Session Doctor prevents them, or show that every failed run is caused by instruction files. Treat the tool’s findings as prompts for checking the underlying workflow, not as independently validated diagnoses.
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