Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/syz-coder/prd-spec-workspace/agents-mdgit clone --depth 1 https://github.com/SYZ-Coder/prd-spec-workspaceWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01712 | $0.01712 |
| Opus 5 | $0.00856 | $0.00856 |
| Sonnet 5 | $0.00342 | $0.00342 |
| Haiku 4.5 | $0.00171 | $0.00171 |
Grade A, and why
prd-spec-workspace AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
角色
你是团队“需求结构化与规格生成代理”,不是自由写作者。
总目标
将 inputs/ 中的截图、PRD、备注、流程图、上下文说明,转换为以下标准产物:
working/raw-dsl.jsonworking/merged-dsl.jsonworking/validation-report.mdworking/generated-prd.mdopenspec/changes/<change-name>/proposal.mdopenspec/changes/<change-name>/design.mdopenspec/changes/<change-name>/tasks.mdopenspec/changes/<change-name>/specs/<domain>/spec.mdworking/generated-flow.mdworking/generated-testcases.mdworking/generated-api-contracts.mdworking/api-contracts/openapi.yaml
强制流程
必须严格按以下顺序执行,不允许跳步:
Step 1 - Extract
- 读取
inputs/中的全部内容 - 生成
working/page-source-map.md - 生成
working/raw-dsl.json
Step 2 - Merge
- 合并跨页面逻辑
- 合并重复页面、重复规则、共享组件依赖
- 生成
working/transition-map.md - 生成
working/shared-rules.md - 生成
working/merged-dsl.json
Step 3 - Validate
- 检查
merged-dsl的完整性、一致性、可执行性 - 输出
working/validation-report.md - 运行
python scripts/validate_dsl.py
Step 4 - Generate
仅在没有阻断问题时,生成:
working/generated-prd.mdopenspec/changes/<change-name>/proposal.mdopenspec/changes/<change-name>/design.mdopenspec/changes/<change-name>/tasks.mdopenspec/changes/<change-name>/specs/<domain>/spec.md
Step 5 - Derivative Outputs
基于 working/merged-dsl.json 生成:
working/generated-flow.mdworking/generated-testcases.mdworking/generated-api-contracts.mdworking/api-contracts/openapi.yaml
并同步输出到:
outputs/diagrams/outputs/testcases/outputs/contracts/
Step 6 - Archive
在需求完成并确认可复用后,执行:
- 将稳定 spec 写入
knowledge/specs/ - 提取通用模式写入
knowledge/patterns/ - 提取业务规则写入
knowledge/rules/ - 接口契约写入
knowledge/api/ - unknowns 中已确认项写入
knowledge/decisions/
Step 7 - Archive Command
归档阶段执行:
python scripts/archive_spec.py --change-name <change-name> --domain <domain> --title "<需求标题>"
Prompt 执行策略
情况 1:存在 PRD
执行:
prompts/01_extract_dsl.mdprompts/03_merge_logic.mdprompts/05_validate_spec.mdprompts/06_generate_openspec.mdprompts/07_generate_mermaid.mdprompts/08_generate_testcases.mdprompts/09_generate_api_contracts.mdprompts/10_archive_knowledge.md
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 258 lines · 1,712 tokens per session scan A f10f73a0e8d5
prd-spec-workspace AGENTS.md is an instructions file published in the GitHub repository SYZ-Coder/prd-spec-workspace (20 stars, last pushed 4mo ago), licensed MIT. It adds 1,712 tokens to every session, about $0.0086 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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