Borrowing it
Nothing to install: this file belongs to ucsandman/DashClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ucsandman/DashClaw/main/.claude/commands/memory-self-review.mdgit clone --depth 1 https://github.com/ucsandman/DashClawWrote 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/ucsandman/dashclaw/memory-self-review)<a href="https://agentmods.dev/commands/ucsandman/dashclaw/memory-self-review"><img src="https://agentmods.dev/badge/commands/ucsandman/dashclaw/memory-self-review/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/ucsandman/dashclaw/memory-self-review"><img src="https://agentmods.dev/badge/commands/ucsandman/dashclaw/memory-self-review.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 | $0.00043 | $0.00748 |
| Opus 5.5 | $0.00017 | $0.00299 |
| Sonnet 5.5 | $0.00009 | $0.00150 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
memory-self-review 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 1mo 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 — 33 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are running DashClaw's self-improvement loop. Your job is to turn the last $1 (default 7) days of agent history into a small, concrete set of PROPOSED improvements to the operator's persistent instructions and memory. This is human-gated: you PROPOSE, you do NOT edit CLAUDE.md, MEMORY.md, or any memory/settings file. The operator reviews and applies.
Keep it cheap and tight — this should be a short, scannable proposal, not an essay.
1. Memory health check
- Read
C:\Users\sandm\.claude\projects\C--Projects-DashClaw\memory\MEMORY.md; report its size vs the ~24.4KB cap. If it's over ~20KB, identify the longest index entries and the inline paragraphs that should move into their own topic file (the index must stay a lean pointer list — seereference_memory_system_boundary.md). - Flag memory files whose status reads as finished history (e.g. "SHIPPED", "RESOLVED", "LANDED on main") that could be demoted to a terse one-line pointer to keep recall sharp.
2. Mine recent history (read-only)
- Use the claude-mem MCP (
mcp-search:search,timeline,get_observations) to scan the last $1 days for: repeated corrections from the operator, the same bug class recurring, repeated tool sequences that could be a workflow, and tasks that stalled or were redone. - Skim
C:\Users\sandm\.claude\stats-cache.jsonfor shifts in volume, model mix (Opus vs Sonnet/Haiku share), and effort that suggest a routing or cost-discipline tweak. - Be evidence-based: only surface a pattern you can point to (cite session ids / observation ids / dates). A pattern must appear 3+ times to become a proposed rule. Do not invent patterns to look thorough.
3. Propose (do not apply)
For each finding, write a proposal block:
FINDING: <what recurred> (evidence: <ids/dates>, seen Nx)
PROPOSED CHANGE: <exact edit — which file, which section, the literal line to add/change>
RATIONALE: <why this prevents the recurrence>
Prefer the smallest durable change: a new one-line rule in the right CLAUDE.md, a new/updated memory file, or a MEMORY.md trim. If something is better solved by a new subagent or workflow than a rule, say so.
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.
- 1mo ago First seen · 33 lines · 43 tokens per session scan A 0bc019ed2af1
memory-self-review is a command published in the GitHub repository ucsandman/DashClaw (313 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 748 once invoked, about $0.0002 per session on Opus 5.5. 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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