Can You Reuse Saved Instructions in a Fresh AI Conversation?

If every request to organize meeting notes starts with “Don't guess missing owners, and keep canceled tasks separate,” could you save that explanation in a file and reuse it?

We tested this with short, fictional meeting notes. After copying the identical instruction file into each fresh workspace, we could load it in a new conversation. Every answer passed our predefined factual checks, so this test could not distinguish accuracy between the conditions. That does not establish equal reliability. Ordinary requests without the extra instructions also passed.

What does “teach” mean here?

An Agent Skill is a package of instructions an AI can read while doing a task. Its name, description, and procedure go in a text file called SKILL.md. This is different from retraining the AI model. Think of it as a procedure the tool can retrieve when needed. Agent Skills specification

Codex and Claude Code, tools for assigning file-based work to AI, support these instructions, though the locations for saving the files and ways of invoking them differ. Sharing a file format does not guarantee identical tools, permissions, or results.

Comparing how the same instructions were delivered

On September 5, 2026, we used Codex and Claude Code on a Mac to organize two sets of Korean meeting notes. One covered an ordinary workshop plan. The other covered a newsletter, including canceled printing and conflicting review deadlines. These were fictional inputs, not real meetings or accounts of a beginner's experience. AI agents running in Codex carried out and assessed the experiment.

Each tool received three conditions:

The instructions specified four sections, references to the numbered source lines, explicit unknowns for missing owners or deadlines, and preservation of cancellations and conflicts. The pasted and saved versions contained the same instructions. Pasted versus saved instructions was the main delivery comparison. The ordinary request was a reference condition for performance without the extra procedure, outside the main pasted-versus-saved comparison. The tools still differed in how and where they loaded instructions; this was not an identical-context test.

Every run started in a new conversation and workspace. Saved-skill runs received a copy of the identical file. We checked invocation with separate development notes, then froze the inputs and scoring criteria. Two tools × three conditions × two cases produced 12 initial answers, all retained.

Even the ordinary requests passed every factual check

Each case had its own nine predefined factual checks for decisions, actions, owners, deadlines, and unknowns. The second case also tested cancellations and conflicts. Separate AI agents graded answers with model and condition labels hidden; the Codex coordinator, which was not blinded, cross-checked their findings. Answer formatting remained visible to the graders. This was not independent human grading, and a tool under examination also participated in assessment. We allowed up to two correction requests for substantive omissions or errors, excluding formatting differences from the factual score.

For example, where the notes listed conflicting review deadlines of September 14 and September 16, no answer arbitrarily chose one. None restored the canceled printing-quote request as an active task. Some ordinary answers added recommendations beyond the notes. In one case, we interpreted “A should set the deadline,” placed under items needing clarification, as advice rather than a fabricated meeting commitment. One Claude saved-skill answer included an English preamble in its Korean output. These judgments and formatting differences were recorded separately. Passing every factual check did not establish equal overall quality.

We did not infer skill use from the appearance of the answers. All four explicit-invocation runs had records showing the file being read or its instructions inserted by the tool. In a separate diagnostic, we installed the file but did not name it in the request, making one attempt per tool. Unlike the ordinary-request condition, the file was present. Both tools loaded the skill. Because this request text differed from the main test, we treated it as an invocation check, not another accuracy comparison. One successful attempt per tool cannot establish reliable automatic activation.

Request length is not the same as effort

These character counts follow from how we assembled the inputs. Saved-skill requests were 700 characters shorter than pasted instructions, but the ordinary requests were shortest. The requests were assembled the same way for both tools.

These Unicode character counts include spaces and source notes, and the requests were assembled automatically. We did not measure how much less a person would type or how much working time they would save. The AI still reads the file's instructions, so a shorter request does not establish token or cost savings.

In these observations, explicit skill invocation took longer than pasting in each of the four comparisons: two tools × two cases. With only one run per condition and case, this is not a reliable general speed comparison.

The separate development stage hit a setup failure: an older version of the Codex command-line tool did not support the selected model. The development check succeeded with a newer version already bundled in the installed app. Having an instruction file did not prevent a tool-version problem from blocking execution.

The experiment used existing subscriptions. The Codex tool version was 0.153.0-alpha.5, with gpt-6-astra selected. Claude Code was 2.1.261, with Fable 5.1 producing the main answers. The retained Codex records did not identify the model that actually answered, and some Claude usage records included an auxiliary model. These results should not be read as a pure model-performance contest.

Reusing a procedure and improving accuracy are different claims

What this experiment established was that the same procedure could be retrieved from a file. With every answer passing, these short cases and criteria could not distinguish performance. Only the second case tested cancellations and conflicts. We did not test long-term consistency or time savings for beginners. Fresh workspaces did not eliminate every tool default setting, either. This English article describes the same Korean-input experiment; we did not run an English benchmark.

The ordinary requests preserved the facts in these cases, so this experiment provides no reason to say a skill was necessary for accuracy. For a recurring task, start with “Did it load the intended procedure?” and “Did it leave missing information marked as unknown?” Reusing instructions that fit does not remove the need to check the resulting work.

AI assisted with research and drafting. The author reviewed the final manuscript and directed the revisions.