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/shandar/memory-map/memory-recallnpx skills add shandar/memory-map --skill memory-recallgit clone --depth 1 https://github.com/shandar/memory-mapWhat 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 | $0.00156 | $0.00843 |
| Opus 5 | $0.00078 | $0.00421 |
| Sonnet 5 | $0.00031 | $0.00169 |
| Haiku 4.5 | $0.00016 | $0.00084 |
Grade A, and why
memory-recall 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.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Recall
Load and intelligently summarise stored memory so a session can pick up exactly where the last one ended.
Works in claude.ai (reads via memory_user_edits view) and Claude Code
(reads ~/.claude/memory.md). Detected automatically.
Step 0 — Detect environment
| Signal | Environment | Load |
|---|---|---|
memory_user_edits tool is available |
claude.ai | references/claude-ai.md |
| Not available / running in terminal | Claude Code | references/claude-code.md |
Read the relevant reference file before proceeding.
Core Protocol
Step 1 — Read the full memory store
Use the environment-appropriate read command (see references) to fetch all stored entries.
Step 2 — Sort entries by relevance to the current request
Memory entries fall into priority tiers:
Tier 1 — Always surface:
CURRENT STATE SNAPSHOT(what's live, what's imminent)- Anything tagged "PENDING" or "next" in entries
Tier 2 — Surface if the user asked for context:
- Project deliverables, decisions, frameworks
- Recent event details, dates, links
Tier 3 — Surface only if directly relevant to the immediate query:
- Historical context, completed work, design tokens
- Personal/operating preferences (PWP, communication style)
Step 3 — Present the recall
Match the format to the trigger:
Proactive recall (start of session, no specific question):
Picking up where we left off:
Current state — [from CURRENT STATE SNAPSHOT entry, paraphrased]
Pending items:
→ [item 1]
→ [item 2]
→ [item 3]
Active projects: [list]
Say "show me the full memory map" to see everything stored.
On-demand recall ("what do you remember?"):
Memory map — [N] entries stored
CURRENT STATE
[paraphrase]
PROJECTS
→ [project 1]: [one-line status]
→ [project 2]: [one-line status]
DELIVERABLES
[count] documents, [count] emails, [count] events
PREFERENCES
[note operating style, tools, communication preferences]
Specific recall ("what do you remember about X?"):
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 119 lines · 156 tokens per session scan A 3744749b334c
memory-recall is a skill published in the GitHub repository shandar/memory-map (2 stars, last pushed 4mo ago), licensed MIT. It adds 156 tokens to every session and 843 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 2026-08-31.
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