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.
git clone --depth 1 https://github.com/mrmyothet/zach-hair-studioWrote 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/commands/mrmyothet/zach-hair-studio/gsd-mempalace-recall)<a href="https://agentmods.dev/commands/mrmyothet/zach-hair-studio/gsd-mempalace-recall"><img src="https://agentmods.dev/badge/commands/mrmyothet/zach-hair-studio/gsd-mempalace-recall/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/mrmyothet/zach-hair-studio/gsd-mempalace-recall"><img src="https://agentmods.dev/badge/commands/mrmyothet/zach-hair-studio/gsd-mempalace-recall.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00022 | $0.01177 |
| Opus 5 | $0.00011 | $0.00589 |
| Sonnet 5 | $0.00004 | $0.00235 |
| Haiku 4.5 | $0.00002 | $0.00118 |
Grade A, and why
gsd-mempalace-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 9d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STOP -- DO NOT READ THIS FILE. You are already reading it. This prompt was injected into your context by the command system. Using the Read tool on this file wastes tokens. Begin executing Step 0 immediately.
Step 0 -- Banner
Before ANY tool calls, display this banner:
GSD > MEMPALACE RECALL
Then proceed to Step 1.
Step 1 -- Config Gate
Check whether the MemPalace capability is enabled by reading .planning/config.json directly with the Read tool.
DO NOT use gsd-tools config get-value -- it hard-exits on missing keys.
- Read
.planning/config.jsonwith the Read tool. - If the file does not exist: write the "unavailable" stub (Step 4) and STOP.
- Parse the JSON. Proceed to Step 2 only if
config.mempalace && config.mempalace.enabled === trueandconfig.mempalace.recall_on_plan !== false. Otherwise display the disabled message and STOP (recall_on_plan: falseturns plan-time recall off while leaving the rest of the capability enabled).
Disabled message:
GSD > MEMPALACE RECALL
MemPalace memory is disabled. To activate:
node <runtime-home>/gsd-core/bin/gsd-tools.cjs config-set mempalace.enabled true
Recall is opt-in; the loop proceeds normally without it.
This step is onError: skip at plan:pre -- recall never blocks planning.
Step 2 -- Resolve wing, mode, and transport
- Wing. Use
config.mempalace.wingif non-empty; otherwise derive fromconfig.project_code; otherwise fall back to the repository directory name. - Mode. Read
config.mempalace.memory_mode(augment|kg_backend|replace, defaultaugment). Onlyaugmentis wired today, so recall always treats the palace as additive;kg_backend/replaceare forward-declared and behave asaugment. - Transport. Prefer the MCP tools (
mempalace_*) in interactive runs when your MemPalace MCP server is registered and your runtime permits those tools. Otherwise — headless/cron/autonomous runs, or runtimes that don't grant the MemPalace MCP tools — use the CLI (mempalace wake-up,mempalace search), which this skill'sBashallow-tool always covers. If neither is reachable, go to Step 4. - Topic. Read the phase
CONTEXT.md(the consumed artifact). Derive a short search query from its title, goal, and key decisions.
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.
- 9d ago First seen · 103 lines · 22 tokens per session scan A babb21998c7e
gsd-mempalace-recall is a command published in the GitHub repository mrmyothet/zach-hair-studio (11 stars, last pushed 28d ago), licensed MIT. It adds 22 tokens to every session and 1,177 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 2026-09-03.
Other commands, from other repositories
session-end
I'll summarize this coding session and update the memory system with our accomplishments.
minutes-ideas
Surface recent voice memos and ideas captured from any device. Use when the user asks "what ideas did I have?", "what were my recent memos?", "what did I record while walking?", or wants to recall a captured thought.
learn
Force claude-smart to extract learnings from this session now.
memory-store
Store an insight, decision, or pattern to memory.
cc-memory
Configure persistent memory that survives across sessions using a layered approach: split rule files for always-loaded context, auto-memory for organic learning, and optional MCP-backed long-term memory for large codebases.
analyze-context
USE WHEN you want to analyze project context before starting work on a task. Calls context + recall, then synthesizes goals, decisions, gotchas, and relevant memories into a pre-task brief.