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CR-730/agent-system-prompt-architect-skill
Agent Skill for generating deployable system prompts for AI agents
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Individual skills in this repository
- agent-system-prompt-architect
skills/agent-system-prompt-architect
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 tokensAlways available to the agent
SKILL.md
3,020 tokensWhen the skill is activated
Reference documents
7,256 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Goal
- Read order
- Non-default delivery modes
- Prompt construction standard
- Capability modules
- Reasoning control policy
- Few-shot calibration
- Tool integration standard
- Safety and compliance standard
- Multi-turn revision protocol
- Output quality bar
- Special rule for cited structured outputs
- Reference files
Files
SKILL.mdcore14,716 bytesSKILL.mdcore14,716 bytesagents/openai.yamlasset246 bytesreferences/code_agent_template.mddoc3,058 bytesreferences/domain-adaptation.mddoc2,937 bytesreferences/evaluation.mddoc5,871 bytesreferences/prompt-engineering-principles.mddoc5,757 bytesreferences/prompt-techniques.mddoc3,600 bytesreferences/rag_template.mddoc3,482 bytesreferences/research_agent_template.mddoc3,244 bytesreferences/snippets.mddoc3,611 bytesreferences/support_agent_template.mddoc3,539 bytesreferences/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 GitHubSkills 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 onlyAutomated 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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- 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 | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/agent-system-prompt-architect/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Native | Path .codex/INSTALL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/agent-system-prompt-architect/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/agent-system-prompt-architect/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/agent-system-prompt-architect/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard 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.