session-memory

A memory layer that saves and recalls findings from design-system skill runs. A design system is the shared set of visual rules and reusable interface parts used across a product.

In plain words
What is it for?
Use it to store findings with dates and sources, recall earlier results, compare runs, and connect recurring issues across design-system reviews.
Why use it?
It prevents each review from starting with no history and makes it possible to compare changes over time and across different checks.

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/murphytrueman/design-system-ops/session-memory
Any agent
npx skills add murphytrueman/design-system-ops --skill session-memory
Clone the repo
git clone --depth 1 https://github.com/murphytrueman/design-system-ops

Made for: Claude Code, Codex.

Per session 161 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,934 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 $0.00161 $0.02934
Opus 5 $0.00081 $0.01467
Sonnet 5 $0.00032 $0.00587
Haiku 4.5 $0.00016 $0.00293

Measured 2d ago against content hash 1b68ea0668d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

session-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 2d 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/session-memory/SKILL.md · 337 lines

How it starts

The opening of the file, as written. The whole thing — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Session Memory

A skill for persisting findings across skill runs so that future skills can compare, correlate, and build on what was previously discovered.

Output type: Memory file creation and recall. This skill writes structured session files and retrieves previous session data. It does not produce analysis — it provides the memory layer that other skills draw from.


Why this exists

Every skill in Design System Ops produces findings. But those findings are ephemeral — they exist in the conversation, get summarised, and then vanish. The next time someone runs token-audit, it starts from zero. It cannot tell you whether things got better or worse since last quarter. It cannot cross-reference what drift-detection found last month with what component-audit finds today.

Session Memory fixes this. It creates a structured memory layer that:

  1. Saves findings from any skill run with timestamps, severity, and skill provenance
  2. Recalls findings when a skill asks "what did we find before?"
  3. Compares findings between runs to surface trends (improving, stable, worsening)
  4. Correlates findings across skills to surface patterns that persist over time

This is not a database. It is a structured markdown file per session that accumulates over time. Simple, portable, and readable by both humans and AI.

Boundaries

This skill is a persistence layer — it saves, recalls, compares, and correlates findings. It does not run audits, produce reports, or generate recommendations on its own. If no previous session files exist and the mode is Recall, Compare, or Correlate, inform the user that there is no history to work with and suggest running an audit first. If the session memory directory does not exist, create it on Save. Do not correlate across skills unless at least two different skill sessions exist — a single-skill history is a Compare, not a Correlate.


Configuration

Check for .ds-ops-config.yml in the project root:

memory:
  directory: ".ds-ops/sessions"     # Where session files are stored
  retain_count: 12                   # How many session files to keep
  auto_save: true                    # Automatically save after every skill run
  comparison_window: 3               # How many previous sessions to compare against

Read the full file on GitHub · 337 lines

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. 2d ago First seen · 337 lines · 161 tokens per session scan A 1b68ea0668d2

Subscribe to this mod's changes

session-memory is a skill published in the GitHub repository murphytrueman/design-system-ops (174 stars, last pushed 11d ago), licensed MIT. It adds 161 tokens to every session and 2,934 once invoked, about $0.0008 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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