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/marcusgoll/spec-flow/domain-memorynpx skills add marcusgoll/Spec-Flow --skill domain-memorygit clone --depth 1 https://github.com/marcusgoll/Spec-FlowWrote 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/marcusgoll/spec-flow/domain-memory)<a href="https://agentmods.dev/skills/marcusgoll/spec-flow/domain-memory"><img src="https://agentmods.dev/badge/skills/marcusgoll/spec-flow/domain-memory.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 | $0.00000 | $0.01740 |
| Opus 5 | $0.00000 | $0.00870 |
| Sonnet 5 | $0.00000 | $0.00348 |
| Haiku 4.5 | $0.00000 | $0.00174 |
Grade A, and why
domain-memory 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 — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Memory Skill
Standardized boot-up ritual and memory access patterns for stateless agent workflows.
Overview
The Domain Memory pattern enables agents to work effectively across sessions without shared context. All state lives on DISK in domain-memory.yaml. Agents read from disk, work, write to disk, and exit.
Core Principle
Disk is the only source of truth.
- Agents are STATELESS - no memory of previous runs
- State is PERSISTENT - survives agent restarts
- Progress is OBSERVABLE - logged to disk
- Work is ATOMIC - one feature at a time
Boot-Up Ritual
Every agent session MUST follow this sequence:
┌─────────────────────────────────────────┐
│ 1. READ domain-memory.yaml │
│ - Load current state from disk │
│ - Understand goals, features, status │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ 2. VERIFY test baseline │
│ - Run existing tests │
│ - Ensure no regressions │
│ - STOP if baseline fails │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ 3. SELECT one work item │
│ - Pick failing features first │
│ - Then untested by priority │
│ - Check dependencies are met │
│ - Lock the selected feature │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ 4. EXECUTE with logging │
│ - Implement the feature │
│ - Run feature-specific tests │
│ - Log approach taken │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ 5. UPDATE domain-memory.yaml │
│ - Mark feature status │
│ - Record what was tried │
│ - Add log entry │
└─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────┐
│ 6. EXIT cleanly │
│ - Unlock the feature │
│ - Commit changes │
│ - Return structured result │
│ - DO NOT continue to next feature │
└─────────────────────────────────────────┘
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 · 277 lines · 0 tokens per session scan A af0dc8077147
domain-memory is a skill published in the GitHub repository marcusgoll/Spec-Flow (92 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,740 tokens. 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.
Other skills, from other repositories
agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
agent-memory
A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
accint-solve
Route a goal through acc's scored-memory loop via accact(runtime="solve"); deliberate any returned brainframe and submit via continue.
claw-memory-management
Workspace memory management guide: memory layers, writing rules, entry format, and maintenance. Use when writing, updating, or maintaining memory files (memory/YYYY-MM-DD.md and MEMORY.md).
self-improving-agent
Low-noise self-improvement skill. Captures learnings, errors, and corrections into a local .learnings/ cache layer. Never auto-promotes to long-term memory files (SOUL.md, MEMORY.md, AGENTS.md, TOOLS.md). Promotion requires explicit user approval after repeated validation.