Yes, with a clear limit. A finished agent session is evidence about the instructions and skills the agent used, and that evidence can be reviewed to find where those instructions caused friction. It is not proof that a skill file is broken. The method described by Mielony in a September 16, 2026 DEV Community article (originally published at mielony.com) turns that review into a routine: collect recent sessions, scan them for mechanical signs of trouble, check each lead against the actual instruction file, and give the surviving proposals to a person who accepts, defers, or rejects them.
What a transcript can and cannot show
A session transcript records what the agent tried, which commands failed, which tools it called repeatedly, and where a user stepped in to correct it. These moments are useful because they happen during real work rather than on invented test prompts. A rough session, however, does not automatically point at a bad skill. The agent may have misread the task, the environment may have been broken, or the request may have changed midway. The author treats each awkward moment as a lead to investigate, not a verdict.
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Scanning also has a blind spot. An instruction can be wrong while the agent still finishes the job by improvising, and that kind of failure leaves no failed command to count. Transcript review therefore cannot be reduced to tallying errors. It needs a person reading the quoted evidence against the file it cites.
The review cycle, step by step
- Collect and export. A collector locates the projects in scope and exports the agent sessions from the preceding 24 hours. The author’s sample schedule runs once a day.
- Scan for friction signals. A scanner looks for mechanical traces of trouble (listed below) and keeps each signal with a severity, a suspected skill, and quoted evidence.
- Run the precheck. Before the agent is invoked, the run is skipped if a prerequisite is missing, if the relevant skill directory has uncommitted changes, or if no session in the window used a skill. The clean-file check matters because proposals cite line locations. If the source changes during analysis, those references can point at the wrong text.
- Verify against the real file. A headless run of the agent checks each signal against the current skill file. Findings can be kept, regraded, or dropped.
- Write the digest. The author caps the number of sessions reviewed and proposals produced. An empty digest is an acceptable outcome, because the process is designed not to invent findings to fill a report.
- Review by a person. Each proposal is accepted, deferred, or dropped by a human reviewer. The reflection job stops at proposals and does not edit skill files.
The signals the scanner looks for
- Failed commands, which leave an explicit error in the transcript.
- Repeated tool calls, which can suggest the agent was looping on an instruction it could not follow.
- User corrections, where the person redirected the agent in the middle of a task.
- Skills that were loaded but apparently not used, which can indicate a skill description that does not match the work.
Each of these is a prompt to look closer. Only the verification step decides whether a signal reflects a flaw in the instruction itself.
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What a valid proposal must contain
The author expects every proposal to state four things, so a reviewer can judge it without rereading the whole session:
- The signal that triggered it, with the quoted evidence.
- The target file that would change.
- The specific change proposed.
- A command that checks whether the change works.
The last item is what separates a checkable edit from an opinion. If a proposal has no command to run afterward, it is harder to tell whether the change helped.
Keeping a human in the loop
The human review is the control point of the whole method. A reviewer can accept a proposal, defer it, or drop it. The author notes that accepted changes can then be routed according to their size, meaning a small wording fix and a structural rewrite need different levels of scrutiny. Nothing in the described process applies an edit to a skill file on its own.
The one reported run
In the implementation described, the author read 40 sessions and produced three verified, checkable changes. That is a single anecdotal report from one implementation, not a measured success rate. The article reports no controlled comparison, no independent reproduction, and no figure showing that the method improves agents across projects. Treat the result as an illustration of what the process can produce, not as evidence of how often it will.
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Setting up a minimal version
According to the author, a minimal setup needs three things:
- A place where agent conversations are stored and can be exported.
- A scheduler that runs the review daily.
- The agent’s headless mode, so the verification step can run without an interactive session.
The commands depend on the agent command-line tool you use, and not every tool exposes session export in the same way. Check what your tool supports before copying the author’s schedule.
Session data can contain sensitive material such as file contents, credentials pasted by mistake, or client details. The source concentrates on auditability and does not establish how any particular product stores or retains transcripts. Read the current vendor documentation on retention and access before storing transcripts in a review pipeline.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design questions to settle before adopting it
The article is not a comparison of competing tools, but its approach raises questions worth answering for any team that maintains agent instructions this way.
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| Question | What to decide | What the source establishes |
|---|---|---|
| Evidence source | Use real working sessions, synthetic tasks, or both | The author’s method is built on real sessions; comparison with synthetic tests is not established |
| Verification | Check each finding against the current instruction file | Required in the described process |
| Reproducible check | Require a command that tests each proposed change | Required of every proposal in the described process |
| Human approval | Decide who accepts, defers, or drops proposals | A person is the control point; no automatic edits |
| Privacy and retention | Decide what is kept, for how long, and who can read it | Not stated in the source; check current vendor documentation |
An adjacent example from Microsoft
A Microsoft DevBlogs account of an Aspire engineering workflow describes an enterprise remediation process organized into check, plan, fix, validate, and learn stages, across multiple repositories, with an existing cloud test gate. It shows that agent work can be broken into explicit stages with gates. It does not test or validate the transcript-review method described above, and it should be read only as a comparison of how staged work is organized.
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