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 skills/alfredoperez/sdd/initnpx skills add alfredoperez/sdd --skill initgit clone --depth 1 https://github.com/alfredoperez/sddWrote 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/skills/alfredoperez/sdd/init)<a href="https://agentmods.dev/skills/alfredoperez/sdd/init"><img src="https://agentmods.dev/badge/skills/alfredoperez/sdd/init.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 | $0.00032 | $0.01761 |
| Opus 5 | $0.00016 | $0.00881 |
| Sonnet 5 | $0.00006 | $0.00352 |
| Haiku 4.5 | $0.00003 | $0.00176 |
Grade A, and why
sdd:init 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/sdd:init does two things, both optional and idempotent:
- Scaffold — create the
.sdd/folder for project-wide context (principles, ADR storage, config). - Adopt — incrementally bootstrap Layer 1 living specs from an existing codebase, one area at a time.
You can stop after the scaffold. Adoption is never forced, never whole-repo, and safe to re-run — it only adds new areas and never overwrites a spec you've reviewed.
Shared Instructions
- Layered Context — living-spec paths, the resolver script (
lib/scripts/resolve-spec-paths.py), tier files, and thedomainsregistry.
Steps
1. Detect state
Check the project root for: .sdd.json, .sdd/principles.md, .sdd/decisions/, and whether .sdd.json already has a domains map.
- If the scaffold artifacts are missing → first run: do Phase 1, then offer Phase 2.
- If they all already exist → already initialized: skip Phase 1 and go straight to Phase 2 (adopt another area).
Phase 1 — Scaffold (Layer 0)
Build a list of only the artifacts that don't exist (never overwrite). If the list is empty, say ✓ .sdd/ already initialized and go to Phase 2.
- Present via AskUserQuestion:
Create all/Customize/Cancel. (Customize→ ask per-artifact yes/no.) - Create the chosen artifacts:
.sdd.json(only if absent): minimal default{ "specsDir": "specs", "commitFormat": "conventional", "noAttribution": true }.sdd/decisions/.gitkeep(empty file so git tracks the folder)..sdd/principles.md— ask via AskUserQuestion how to seed it:- Blank template — copy
lib/templates/principles.mdverbatim. - Infer from codebase — spawn one subagent: read lint/format/test/build config (e.g. eslint, prettier, tsconfig,
package.jsonscripts, CI files),CLAUDE.md, and the folder layout, then draft 5–10 candidate project MUSTs in theprinciples.mdbullet shape. Ground every rule in something it actually saw; mark anything uncertain for the user. Show the draft; the user confirms/edits before you write the file.
- Blank template — copy
- Output the created files.
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 · 125 lines · 32 tokens per session scan A f67d0f96a366
sdd:init is a skill published in the GitHub repository alfredoperez/sdd (2 stars, last pushed 12d ago), licensed MIT. It adds 32 tokens to every session and 1,761 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…