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/rtazima/claude-proj-blueprint/memorynpx skills add rtazima/claude-proj-blueprint --skill memorygit clone --depth 1 https://github.com/rtazima/claude-proj-blueprintWrote 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/rtazima/claude-proj-blueprint/memory)<a href="https://agentmods.dev/skills/rtazima/claude-proj-blueprint/memory"><img src="https://agentmods.dev/badge/skills/rtazima/claude-proj-blueprint/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 5.1 | $0.00052 | $0.00425 |
| Opus 5 | $0.00026 | $0.00212 |
| Sonnet 5 | $0.00010 | $0.00085 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
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 5d 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.
What it actually says
Long-Term Memory
When to use
- Before implementing: search for previous solutions to similar problems
- Before deciding: search for already-documented ADRs and trade-offs
- After incidents: search for post-mortems of similar issues
- When reviewing: search for project patterns and conventions
How to search
# General semantic search (project memory)
python memory/query.py "your question here" --agent-format
# Search only in architectural decisions
python memory/query.py "rate limiting" --type docs --agent-format
# Search in code
python memory/query.py "authentication middleware" --type code --agent-format
# Search in commits
python memory/query.py "fix deploy" --type git_commit --agent-format
# Search global cross-project memory
python memory/query.py "how did we solve auth" --global --agent-format
# Search both project + global, merged by relevance
python memory/query.py "rate limiting strategy" --both --agent-format
Always use --agent-format to get structured markdown output.
When to index
# After significant changes to docs/ or src/
python memory/index.py --incremental
# ADRs, post-mortems, and learner reports are auto-promoted to global memory
# if global_memory.enabled is true in config.yaml
Rules
- ALWAYS check memory before creating an ADR (one may already exist on the topic)
- ALWAYS check before implementing a complex feature
- If memory returns relevant context, CITE the source in the implementation plan
- If no results, move on without mentioning memory
Setup (if not configured)
pip install -r memory/requirements.txt
python memory/index.py
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.
- 5d ago First seen · 57 lines · 52 tokens per session scan A 1e84b26122a7
memory is a skill published in the GitHub repository rtazima/claude-proj-blueprint (20 stars, last pushed 3mo ago), licensed MIT. It adds 52 tokens to every session and 425 once invoked, about $0.0003 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.
Other skills, from other repositories
project
Create or maintain an Architecture Studio project — initialize its record bundle, remember sourced facts, capture or supersede decisions, inspect project status, or migrate a 1.x PROJECT.md. Use when the user says “set up the project,” “remember this,” “we decided,” asks about project context, or runs /as:project.
init-workspace-documentation
Skill "init-workspace-documentation" from griddynamics/rosetta, covering agent memory.md, agent memory, preventive rules, what worked and what failed.
decision-memory
Use when planning, designing, reviewing, or changing code in a repository that keeps ADRs (usually docs/adr) — to load the decisions that already govern the work, check a plan or diff against them, or record a new decision. Also use when a choice feels already-settled and you cannot find where it was settled.
memory-continuity
Preserves cross-session continuity with deterministic MEMORY.md, index.md, and snapshot handoffs under .agentic/memories/.
plan-memory-index
Maintains concise indexed memory entries for applied plans under .agentic/memories/ with clear boundaries between memory and plan sources. Memory retrieval is prioritized before loading historical plan files.
self-improving-agent
Log learnings, errors, and corrections to .learnings/ for continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects the agent, (3) A knowledge gap is identified, (4) A better approach is found. Captures corrections, insights, errors, and feature requests; promotes broadly…