engram-sdd-flow

A Spec-Driven Development workflow, meaning a process that explores the existing system, agrees on a written change plan, implements it, tests it, and records the result.

In plain words
What is it for?
Use it for multi-phase implementation planning or when a user requests SDD. It organizes work into explore, propose, apply, verify, and archive phases.
Why use it?
It makes non-trivial changes easier to scope and review by keeping decisions, risks, tests, and verification evidence visible.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/gentleman-programming/engram/sdd-flow
Any agent
npx skills add Gentleman-Programming/engram --skill sdd-flow
Clone the repo
git clone --depth 1 https://github.com/Gentleman-Programming/engram

Made for: Claude Code, Codex.

Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 267 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00028 $0.00267
Opus 5 $0.00014 $0.00133
Sonnet 5 $0.00006 $0.00053
Haiku 4.5 $0.00003 $0.00027

Measured 3d ago against content hash e815910e2c30, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

engram-sdd-flow 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.

skills/sdd-flow/SKILL.md · 49 lines

What it actually says

When to Use

Use this skill when:

  • Starting non-trivial changes
  • Coordinating spec, design, implementation, and validation
  • Running command-based SDD flow

Canonical Phase Order

  1. explore - understand existing behavior and constraints
  2. propose - define intent and scope
  3. apply - implement tasks from approved plan
  4. verify - validate behavior against spec and regressions
  5. archive - capture completion and close loop

Never skip a phase without explicit rationale.


Artifacts per Phase

  • Explore: findings and risks
  • Propose: change proposal with scope boundaries
  • Apply: code + tests
  • Verify: evidence of validation
  • Archive: finalized summary and follow-ups

Exit Criteria

  • Scope and risks understood before implementation
  • Tests prove expected behavior
  • Verification covers regressions
  • Session summary captures learnings for next work
Changes

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.

  1. 3d ago First seen · 49 lines · 28 tokens per session scan A e815910e2c30

Subscribe to this mod's changes

engram-sdd-flow is a skill published in the GitHub repository Gentleman-Programming/engram (6,266 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 267 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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.

obra/superpowers · 37 tokens

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.

microsoft/vscode · 62 tokens

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.

microsoft/vscode · 51 tokens

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.

microsoft/vscode · 53 tokens

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…

microsoft/vscode · 71 tokens