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CR-730/agent-system-prompt-architect-skill

Agent Skill for generating deployable system prompts for AI agents

History

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git clone --filter=blob:none --no-checkout https://github.com/CR-730/agent-system-prompt-architect-skill.git .hotskills/cr-730-agent-system-prompt-architect-skill && git -C .hotskills/cr-730-agent-system-prompt-architect-skill checkout --detach 966a13e846a4086dfa4bf164fc3f07d1966b5ddf # Copy each selected skill directory from .hotskills/cr-730-agent-system-prompt-architect-skill into .agents/skills/<skill-name>/
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1skill in this repository
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Heat

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Individual skills in this repository

How it works

agent-system-prompt-architect

When your agent uses it

Designs, reviews, revises, and templates deployable system prompts for agent projects. Use whenever the user asks to write, improve, review, or debug an agent's system prompt, system message, or role/boundary instructions — including Chinese requests such as 系统提示词、角色设定、角色和边界规范、提示词改写 — and schema-driven requests where an agent must output a given class or JSON structure. Especially for prompts needing clear roles, task boundaries, tool-use rules, evidence handling, output formats, safety behavior, or compact runtime-ready structure. Do NOT use for one-off chat prompts, marketing copy, or general prompt-engineering Q&A.

What it loads into context

Frontmatter

151 tokens

Always available to the agent

SKILL.md

3,020 tokens

When the skill is activated

Reference documents

7,256 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

  1. Goal
  2. Read order
  3. Non-default delivery modes
  4. Prompt construction standard
  5. Capability modules
  6. Reasoning control policy
  7. Few-shot calibration
  8. Tool integration standard
  9. Safety and compliance standard
  10. Multi-turn revision protocol
  11. Output quality bar
  12. Special rule for cited structured outputs
  13. Reference files

Files

  • SKILL.mdcore14,716 bytes
  • SKILL.mdcore14,716 bytes
  • agents/openai.yamlasset246 bytes
  • references/code_agent_template.mddoc3,058 bytes
  • references/domain-adaptation.mddoc2,937 bytes
  • references/evaluation.mddoc5,871 bytes
  • references/prompt-engineering-principles.mddoc5,757 bytes
  • references/prompt-techniques.mddoc3,600 bytes
  • references/rag_template.mddoc3,482 bytes
  • references/research_agent_template.mddoc3,244 bytes
  • references/snippets.mddoc3,611 bytes
  • references/support_agent_template.mddoc3,539 bytes
  • references/template.mddoc1,562 bytes
Read SKILL.md
# Agent System Prompt Architect

## Goal
Turn partial agent requirements into a deployable system prompt for another agent project.

**Your entire response is the deployable prompt by default.** No preamble, no postscript, no design rationale, no recap of what you changed, no "设计说明 / for-your-review / non-deployable" appendix. Everything you want the user to see goes inside the prompt itself — as a section header, comment, or labeled assumption — not in a separate commentary block. Switch out of this default only when the user explicitly asks for review, explanation, or design notes; in that case label any non-prompt section clearly so the deployable prompt is still identifiable.

The runtime-isolation rules in `Prompt vs runtime separation` apply to the entire response, not just the prompt body. Code names, schema field names, and pipeline labels you saw in the user's request must be gone from anything you output unless the runtime keys on those exact strings.

**Draft in the same turn.** Ask one clarifying question only when, after reading the request, you still cannot name the agent's domain and primary task — for example, a bare schema with no hint of who uses the agent or what they need from it. Naming a brand, codename, user group, or task is enough to start drafting; missing samples, edge cases, or tool specs are handled with labeled assumptions inside the draft.

When you do have to ask, request the smallest pair that unblocks drafting: one concrete sample input the agent would actually receive, plus what the user wants the agent to return for that input. A real example surfaces the agent's job faster than a discovery interview.

## Read order
1. Read `references/prompt-engineering-principles.md` first as the baseline writing checklist.
View the complete source on GitHub

Skills included

  • agent-system-prompt-architect

Repository facts

License
MIT
Created
May 19, 2026
Last pushed
Sep 16, 2026
Forks
2
Open issues
1
Project activity
Active

Before you install

Instructions only

Automated signals, not a guarantee

Checked Nov 7, 2026

Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.

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  • skills/agent-system-prompt-architect/SKILL.md:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
  • No broad tool permissions
  • No dangerous shell commands
  • No network access
  • No hidden or obfuscated text
  • No instruction overrides
  • No executable scripts
  • No access to secrets
  • No requests to vote in Hot

How the signals fit together

Facts from a static read of the files at the pinned commit. They describe what the code does, not why.

No combined signals at this commit.

Works with

Agent compatibility, from the files in this repository
AgentStatusEvidenceInstall to
Claude CodeCompatibleStandard SKILL.md skills/agent-system-prompt-architect/SKILL.md.claude/skills/<skill-name>/
CodexNativePath .codex/INSTALL.md.agents/skills/<skill-name>/
CursorCompatibleStandard SKILL.md skills/agent-system-prompt-architect/SKILL.md.cursor/skills/<skill-name>/
Gemini CLICompatibleStandard SKILL.md skills/agent-system-prompt-architect/SKILL.md.gemini/skills/<skill-name>/
GitHub CopilotCompatibleStandard SKILL.md skills/agent-system-prompt-architect/SKILL.md.github/skills/<skill-name>/
OpenCodeCompatibleStandard SKILL.md skills/agent-system-prompt-architect/SKILL.md.opencode/skills/<skill-name>/
  • Native The repository ships a manifest, folder or frontmatter field for this agent.
  • Compatible A standard SKILL.md that this agent can read.
  • Unknown No evidence either way.

Derived automatically from paths, manifests and frontmatter. Not tested on each agent.