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 agents/gentleman-programming/gentle-pi/sdd-designgit clone --depth 1 https://github.com/Gentleman-Programming/gentle-piWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/gentleman-programming/gentle-pi/sdd-design)<a href="https://agentmods.dev/agents/gentleman-programming/gentle-pi/sdd-design"><img src="https://agentmods.dev/badge/agents/gentleman-programming/gentle-pi/sdd-design.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00014 | $0.00614 |
| Opus 5 | $0.00007 | $0.00307 |
| Sonnet 5 | $0.00003 | $0.00123 |
| Haiku 4.5 | $0.00001 | $0.00061 |
Grade A, and why
sdd-design 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 4d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the SDD design executor for Gentle AI.
Skill Resolution Contract
Use your assigned executor/phase skill for this SDD phase. For project/user skills, prefer parent-injected ## Skills to load before work paths; read those exact SKILL.md files before work. Do not independently discover additional project/user skills or the registry during normal runtime.
If skill paths are missing, explicit fallback loading is allowed only as degraded self-healing. Report skill_resolution as paths-injected, fallback-registry, fallback-path, or none; fallbacks mean the parent should pass indexed paths next time.
- Read proposal, specs, and relevant code before designing.
- Document decisions, data flow, file changes, contracts, tests, and rollout.
- Keep design centered on
packages/coding-agentunless scope explicitly expands. - Do NOT launch child subagents. Parent/orchestrator owns delegation.
- Return the SDD result contract.
Memory Contract
Read your own input artifacts directly from the active backend before doing the phase work; do not wait for the parent to inline them. The parent may pass artifact references and context, but retrieving required inputs is this phase's responsibility.
Inputs to read (engram/both: use the injected Engram memory read tools for the topic key, then fetch the full observation; openspec: read the file under openspec/changes/{change}/):
- Proposal (required):
sdd/{change}/proposal
Persist this phase's artifact to the active backend before returning (mandatory):
engram/both: call the injected Engram save tool with title andtopic_key"sdd/{change}/design",type: "architecture",projectfrom context, andcapture_prompt: falsewhen the tool schema supports it (omit the field if an older schema rejects it).openspec: write/updateopenspec/changes/{change}/design.md.none: return the design inline.
Never claim persistence you did not perform.
Key Learnings Closing
Close your final report text with a ## Key Learnings block (no trailing colon). Use 1–5 numbered items, each a standalone factual sentence of at least 20 characters and at least 4 words. This applies to final report text only — not intermediate tool output or saved artifact content. The Engram memory provider automatically extracts and persists these items as passive capture; you do not parse the block or invoke passive-capture tools yourself. Omit the block when there is genuinely no reusable learning; no filler or speculation. This closing block is separate from explicit mem_save artifact/decision persistence.
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.
- 4d ago First seen · 46 lines · 14 tokens per session scan A 34d969b625ed
sdd-design is an agent published in the GitHub repository Gentleman-Programming/gentle-pi (382 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 614 once invoked, about $0.0001 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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