インストラクション・スキル・フックの個人的な構成と作り方The article explains how to structure instruction files, skills, and hooks to…

AI coding agents have grown markedly more capable over the past year, yet they still struggle with the parts of software work that depend on context rather than general knowledge: environment setup, build quirks, and the project-specific conventions a team accumulates over time. A recent Qiita post aimed at GitHub Copilot users argues that these gaps are not something a model will simply outgrow. Instead, they have to be taught explicitly, and the article walks through how to do that using three complementary mechanisms — instruction files, skills, and hooks.

The core premise is that leaving an AI agent on its default settings and expecting it to follow house rules is optimistic. A model can write idiomatic code in a general sense, but it cannot infer that your repository formats commit messages a certain way, pins a particular toolchain version, or expects tests to run through a bespoke script. The author frames instruction files, skills, and hooks as the layers that carry this missing knowledge into the agent's working context, and presents a personal configuration approach for combining them.

Instruction files are the most familiar of the three. In the Copilot ecosystem these are typically Markdown documents — often placed under a .github directory or scoped to particular file patterns — that describe conventions the agent should follow on every task. They are well suited to stable, always-relevant guidance: coding style, naming rules, preferred libraries, directory structure, and the kind of do-and-don't notes a new team member would receive. Because they are loaded into context automatically, they set a baseline of behavior without the user having to restate expectations in each prompt.

Skills, as the article describes them, are a more modular way to package know-how. Rather than one large document that applies universally, skills bundle focused, reusable capabilities or procedures that the agent can draw on when a task calls for it. This keeps context lean, since only the relevant material is pulled in, and it lets a team maintain discrete, testable units of guidance — for example, how to scaffold a new module or how to interact with a specific internal API — instead of one sprawling file that grows unwieldy.

Hooks address a different problem: timing and enforcement. Where instructions and skills shape what the agent knows, hooks act at defined points in the workflow, running logic in response to events such as a file edit or a completed step. That makes them useful for deterministic checks the model should not be trusted to remember on its own — formatting, linting, or validation that runs automatically rather than relying on the agent to invoke it. Used together, the three form a layered system: instructions supply the persistent rules, skills supply on-demand procedures, and hooks supply automated guardrails.

The practical value the piece emphasizes is accuracy and reliability. By encoding project-specific practices explicitly, teams reduce the variance in what an agent produces and cut down on the corrections a developer has to make after the fact. This mirrors a broader industry direction, as agentic coding tools converge on similar configuration surfaces. Adjacent systems such as Cursor's rules files, Anthropic's Claude with its project instructions and emerging skills concept, and various agent frameworks all offer comparable ways to inject repository context, which suggests the pattern of teaching an agent your conventions is becoming a standard part of the workflow rather than a niche optimization.

For readers new to these features, a reasonable starting point is to treat instruction files as the first investment, since they require the least setup and deliver immediate consistency. Skills and hooks tend to pay off more as a project scales or as repetitive, error-prone steps become apparent. As with any configuration that lives in a repository, keeping these files in version control, reviewing them like code, and revising them as conventions change appears to be the sensible way to keep the guidance accurate over time. The overall message is pragmatic: capable agents still need to be told the local rules, and a small amount of deliberate structure is what turns a generic assistant into one that fits a specific project.