The Skill Management Guide: Make Cursor, Claude Code, and Doubao Reuse Your Working Methods

Skill management is the full lifecycle of creating, validating, publishing, installing, and versioning working methods that AI agents can load directly. A skill usually ships as a SKILL.md (metadata, trigger conditions, steps, constraints, examples) or as a ZIP / remote repo, and agents such as Cursor, Claude Code, and Doubao Desktop install it with npx skills add or an Install URL. Unlike a prompt, a skill captures a repeatable process rather than a one-off piece of text.

What a skill is

A skill is a repeatable working method written for an AI agent. It answers three questions: when to use it (trigger conditions), how to do it (steps and constraints), and what a good result looks like (examples and acceptance criteria). In coding agents such as Cursor and Claude Code, a skill is centred on a SKILL.md file, bundled with scripts or templates as a directory, ZIP, or remote repo, and loaded automatically when the agent recognises a matching task.

A concrete comparison: "write a PR description for me" is a prompt. A "PR description skill" specifies when to trigger (the user mentions a PR or commit), what to read (the diff, linked issues), which template to output (background / changes / testing / risks), and when to ask a follow-up question. The first is a sentence; the second is a process.

Skills vs. prompts vs. sub-agents vs. MCP

They are not mutually exclusive: a skill's steps can call MCP tools or delegate to a sub-agent, and every step inside a skill is itself a carefully written prompt. Skill management operates at the method layer.

The skill lifecycle

1. 1Draft: distil from one successful conversation — what you told the model, how many times you corrected it, and which output you finally accepted. Turn corrections into constraints and the accepted output into an example.
2. 2Validate: run it on at least two real tasks and record failure modes. Do not publish a skill that has never seen a real task.
3. 3Publish: give the skill a stable address (Install URL or repo) with a version number and target agents. Public skills install without login; private ones use short-lived links.
4. 4Install: run the install in the target agent (npx skills add <url> for Cursor / Claude Code; upload the manager pack for Doubao Desktop).
5. 5Iterate: whenever the skill mishandles a case, adjust constraints or add an example, bump the version, and note why.
6. 6Retire: if a skill has not triggered in a quarter or has been superseded, archive rather than delete so history survives.

What a good SKILL.md contains

• Frontmatter metadata: name, one-line description, version, author, target agents. Write the description as "use me when…", because many agents decide whether to load based on it.
• Trigger conditions: scenarios where the skill applies, and explicit scenarios where it does not.
• Preconditions: what to read or confirm before starting, e.g. "read CHANGELOG to determine the version" or "ask first if there is no test directory".
• Steps: numbered, executable, one action each. Avoid unverifiable steps like "fully understand the requirements".
• Constraints: what must not be done, required output format, situations that require stopping to ask.
• Examples: at least one complete input → output sample; add counter-examples for common mistakes.
• Supporting files: scripts, templates, and checklists in the same directory, referenced by relative path from SKILL.md.

Distribution: where skills go and how they get there

Skills across models: the Canonical Spec

A skill that is stable on Claude may break on GPT or a Chinese model, usually because of different format preferences and instruction-following behaviour. Instead of maintaining several SKILL.md files, maintain one model-agnostic Canonical Spec — role, goal, steps, constraints, examples — and render per-model versions with adapters. Edit the spec once; the renderings update.

Implementing skill management with PromptMan

1. 1Bring existing skills home: use the desktop app to sync the agent directories of ChatGPT, Claude, and Cursor so skills scattered across .cursor/skills and .claude folders land in one library.
2. 2Fill in metadata: add a one-line description, trigger conditions, and target agents to every skill. This directly determines whether an agent loads it.
3. 3Unify distribution: grab an Install URL for each skill in PromptMan; from then on a single npx skills add works on any machine or project.
4. 4Connect the Doubao manager: if you also use Doubao Desktop, install the manager pack so it picks from your library inside the conversation.
5. 5Set a review rhythm: when a skill underperforms, fix the constraint or add an example and bump the version on the spot; monthly, look at which skills never triggered.

Common mistakes

• Renaming a prompt to a skill: without triggers and steps, the agent does not know when or how to use it.
• Writing attitudes instead of steps: "check carefully" is not executable; "list every public function and check null handling for each" is.
• One skill that does everything: the bigger the skill, the less precise its trigger. Prefer several small skills.
• Publish and forget: skills go stale as models and projects evolve; without version history there is nothing to trace.
• A copy in every project: the same skill duplicated across ten repos means one fix and nine misses. Use Install URLs for a single source of truth.