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/hashgraph-online/awesome-codex-plugins/waggle-memorynpx skills add hashgraph-online/awesome-codex-plugins --skill waggle-memorygit clone --depth 1 https://github.com/hashgraph-online/awesome-codex-pluginsWrote 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/hashgraph-online/awesome-codex-plugins/waggle-memory)<a href="https://agentmods.dev/skills/hashgraph-online/awesome-codex-plugins/waggle-memory"><img src="https://agentmods.dev/badge/skills/hashgraph-online/awesome-codex-plugins/waggle-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 | $0.00031 | $0.00805 |
| Opus 5 | $0.00015 | $0.00402 |
| Sonnet 5 | $0.00006 | $0.00161 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
waggle-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 4d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- waggle-memory — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Waggle project memory
Treat Waggle as memory owned by the project, not as a transcript archive or generic personal chat memory.
Establish a stable scope
Before the first Waggle call in a task, choose one stable project value:
- Prefer the repository's canonical remote identity without credentials or a trailing
.git, such asgithub.com/acme/api. - If there is no remote, use the canonical absolute Git root path.
- Outside Git, use the canonical absolute workspace path.
Reuse that value across sessions. Do not use only a directory basename because unrelated repositories can share it. Pass agent_id: "codex" and the current task/session identifier when one is available. Never mix remembered context from a different project into the answer.
Retrieve selectively
- At the beginning of a new task that involves meaningful project work, call
prime_contextonce with the narrowest known scope. - Skip priming for greetings, trivial formatting, or questions fully answerable from the current prompt with no project-history dependency.
- Before answering anything that may depend on earlier decisions, preferences, constraints, failed attempts, bugs, experiments, unresolved questions, or project state, call
query_graphwith the stable project scope. Start withmax_nodes: 10,max_depth: 1, andretrieval_mode: "hybrid". - Use
get_relatedwhen a returned node ID needs graph context, andgraph_diffwhen the user asks what changed. - Treat retrieved memories as historical evidence, not current repository truth. Verify changeable facts against the working tree. If retrieval is empty or conflicts with current evidence, say so and do not invent history.
Store durable knowledge only
Use this threshold for every write:
Store information when forgetting it would likely cause duplicated work, a wrong future decision, or violation of an established constraint. Do not store something merely because it happened.
After a completed turn, call observe_conversation only when the turn crosses that threshold and contains durable project knowledge, such as:
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.
- 4d ago First seen · 56 lines · 31 tokens per session scan A 2e074f96f9a0
waggle-memory is a skill published in the GitHub repository hashgraph-online/awesome-codex-plugins (859 stars, last pushed 6d ago), licensed Apache-2.0. It adds 31 tokens to every session and 805 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 2026-08-30.
Other skills, from other repositories
search
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
agentscope-developer
Expert-level AgentScope developer skill for building production-ready LLM agents. Transforms AI into an experienced AgentScope architect with deep knowledge of ReAct agents, multi-agent orchestration, memory modules, voice agents, MCP/A2A integrations, and model fine-tuning. Use when: building agents, agent framework…
bio-clip-seq-clip-alignment
Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use…
detecting-fileless-malware-techniques
Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware…
detecting-process-injection-techniques
Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process…
performing-memory-forensics-with-volatility3
Analyze volatile memory dumps using Volatility 3 to extract running processes, network connections, loaded modules, and evidence of malicious activity.