Skip to content
Agentmart
Submit a tool

agent-experience

Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills…

Skill by wopal-cn

IndexedMIT★ 3
Write a review

Chalk Labs

Need help deploying agent-experience?

Chalk Labs sets up agents like this inside real teams: connected to your tools, tested on your data, with a human in the loop where it matters.

Book a call

Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/wopal-cn-agent-experience

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

Get the weekly diff

The weekly diff: one email a week with new agent tools, notable updates and grade changes, plus security alerts for tools you follow. Double opt-in, one-click unsubscribe.

We use your email only for this newsletter. Privacy

About

Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills, context loading, or multi-step workflows, to make information discoverable and use context efficiently.

Use cases: writing, agent-building

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/agent-experience

What's inside

Skill name
agent-experience
Description
Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills, context loading, or multi-step workflows, to make information discoverable and use context efficiently.
License
MIT
Instructions
16 lines in SKILL.md · view on GitHub

Files (1)

  • SKILL.md

SKILL.md (first lines)

- **Start with minimal context:** expose purpose, available actions, and constraints first; load detailed instructions when needed.
- **Make discovery explicit:** every deferred resource needs a clear description and a reliable way to retrieve it.
- **Keep critical constraints visible:** permissions, destructive effects, and required validation should appear before the relevant action.
- **Prefer bounded outputs:** return concise results with identifiers or paths for retrieving details; avoid dumping entire logs or documents.
- **Use locality:** include a bounded amount of likely needed adjacent context with an operation’s result. For example, deliver a thread reply with a few preceding messages. Mark omissions and truncation explicitly, and provide a way to retrieve more.
- **Evaluate total work:** saving context is useful only if it does not cause more searches, repeated reads, or mistakes.

## Tool definitions

- **Delete obvious constraints.** Omit rules the model learns from the call result, such as a missing file failing to read or a failed send not being delivered.
- **Describe behavior, not implementation.** State what the tool does and returns; omit internal mechanisms, output markers the model sees in results, and enforcement details.
- **Put parameter rules on the parameter.** Defaults, ranges, pairing, and when-to-set rules belong in that parameter's description or schema; a description that varies with configuration usually means the varying part belongs to a parameter.
- **Say each fact once.** Do not repeat the tool definition in its system-prompt section, a parameter description in the tool description, or one tool's rules in another tool.
- **Measure the change.** Compare first-turn prompt tokens before and after a tool-definition change.

From wopal-cn/wopal-space-ontology, MIT.

More from wopal-cn/wopal-space-ontology

Signals

Stars
3
Forks
1
Open issues
0
Last commit (this folder)
Oct 22, 2026 (9 days ago)
Last release
Oct 24, 2026 (7 days ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

More from wopal-cn

Done for you

Rather not set it up yourself?

Chalk Labs scopes, builds and runs agents for your team: the task audit, the eval harness, the integrations and the weekly ops report. Fixed scope, priced before we start.

See how we build AI agents →Read the case studies

Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.

launches shipped
50+
launches shipped
media placements
500+
media placements
founders on every account
2
founders on every account

Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block

Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.

Get agent-experience running in your business

Tell us what you want to automate. A founder replies within one working day with a plan, not a pitch deck.

Goes to the Chalk Labs founders, not the newsletter. Privacy

Need help deploying agent-experience?

CallGet help
Cite this page

agent-experience skill. Agent Mart. https://agentmart.dev/skills/wopal-cn-agent-experience (last updated 2026-10-24)