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 skills add yeaight7/agent-powerups --skill memory-optimization-workflowgit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/memory-optimization-workflow)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/memory-optimization-workflow"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/memory-optimization-workflow/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/skills/yeaight7/agent-powerups/memory-optimization-workflow"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/memory-optimization-workflow.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 | $0.00039 | $0.00640 |
| Opus 5 | $0.00019 | $0.00320 |
| Sonnet 5 | $0.00008 | $0.00128 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
memory-optimization-workflow 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 yesterday.
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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Optimization Workflow
Overview
Minimize token spend, reread cost, and unnecessary rebuilds.
graphify is the main optimization path for repeated work. Helper tools exist to make hard sources cheaper before graph or direct reading.
When to Use
- mixed corpus and the cheapest inspection path is unclear
- repeated questions over the same files
- need to choose between direct read, conversion, graph build, update, or query
- want to reduce repeated large-context rereads
Do not use for:
- tiny single-file questions where direct reading is already cheapest
- cases where the user explicitly wants raw-file inspection only
Required Checks
apx check graphify
apx check markitdown-file-intake
apx check defuddle
Stop and report missing tools. Do not auto-install without approval.
Fast Routing
| Situation | Cheapest path |
|---|---|
| small readable text corpus, one question | read directly |
| PDF, Office doc, or other binary-like source | markitdown-file-intake |
| noisy web page or article | defuddle |
| repeated questions across same corpus | build with graphify |
| existing graph plus changed sources | graphify --update |
| existing graph plus new question | query graph first |
Decision Rules
- prefer direct reading for small plain-text scope
- prefer Markdown over binary or chrome-heavy formats
- prefer graph query over full reread when a graph already exists
- prefer incremental update over rebuild
- keep helper tools secondary to the main graph path
- keep Obsidian optional; it is not part of the optimization decision unless the user wants vault browsing
Escalation Ladder
- Direct read if scope is already small and readable.
- Convert only if format is the main source of waste.
- Build graph memory when questions will repeat or corpus is broad.
- Update existing graph when sources changed.
- Query existing graph before any broad reread.
Common Failure Modes
- building a graph for a tiny one-shot question
- rereading large corpora after a graph already exists
- converting already-readable Markdown or code
- rebuilding instead of updating
- making helper tools feel primary instead of supportive
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.
- yesterday First seen · 83 lines · 39 tokens per session scan A 5fa6cd478a48
memory-optimization-workflow is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 2d ago), licensed Apache-2.0. It adds 39 tokens to every session and 640 once invoked, about $0.0002 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 2026-09-14.
Other skills, from other repositories
persistent-notes
Save notes locally to /mnt/workspace/notes.json file. Use when user wants to "save a note" or "remember something".
session-summaries
What the chat right-panel session summary shows, what it costs, and how to make a session summarize well. Load when the user asks about the session summary panel, why a summary looks wrong or empty, or how to turn it on.
narco-check
Memory integrity audit. Detects hallucinations, circular confirmations, and state poisoning. Runs automatically after 2 consecutive failures or at nightly deep dive. Uses Opus 4.6 as the auditor model.
openlore
Query and publish to an OpenLore knowledge base over SSH using ordinary shell commands. Use when a task needs project documentation, runbooks, shared team knowledge, or a place to publish findings.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
github-code-review
Review PRs: diffs, inline comments via gh or REST.