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 Abhigyan-Shekhar/Waggle-mcp --skill waggle-primegit clone --depth 1 https://github.com/Abhigyan-Shekhar/Waggle-mcpWrote 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/abhigyan-shekhar/waggle-mcp/waggle-prime)<a href="https://agentmods.dev/skills/abhigyan-shekhar/waggle-mcp/waggle-prime"><img src="https://agentmods.dev/badge/skills/abhigyan-shekhar/waggle-mcp/waggle-prime/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/abhigyan-shekhar/waggle-mcp/waggle-prime"><img src="https://agentmods.dev/badge/skills/abhigyan-shekhar/waggle-mcp/waggle-prime.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 | $0.00026 | $0.00168 |
| Opus 5 | $0.00013 | $0.00084 |
| Sonnet 5 | $0.00005 | $0.00034 |
| Haiku 4.5 | $0.00003 | $0.00017 |
Grade A, and why
waggle-prime 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 12d 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.
This is a copy
100% identical to waggle-prime — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Prime project context
- Determine the stable project scope: prefer the credential-free Git remote identity, otherwise the canonical absolute Git root or workspace path.
- Call
prime_contextwith thatproject,agent_id: "codex", and the current session identifier when available. - Summarize only the returned decisions, constraints, failed approaches, open questions, and next steps relevant to the current task.
- Distinguish remembered history from facts verified in the current working tree. Do not invent context when the result is empty.
This is the Codex skill equivalent of /waggle-prime; invoke it as $waggle-prime.
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.
- 12d ago First seen · 14 lines · 26 tokens per session scan A e8cc9ab076de
waggle-prime is a skill published in the GitHub repository Abhigyan-Shekhar/Waggle-mcp (40 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 168 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to waggle-prime, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
graph-ask
Ask any natural language question about the memory graph. You generate Cypher directly and execute it. Use when the user has a complex or ad-hoc question that the standard graph tools don't cover.
ingest-audio
Transcribe a local audio or video file using Whisper and ingest it into the memory graph. Use when the user has a local MP3, WAV, M4A, MP4, or similar audio/video file they want to add to their knowledge graph.
ingest
Ingest a file or URL into the memory graph. Handles local files (text, PDF, DOCX, XLSX, images, etc.) and URLs (web pages, YouTube, Wikipedia, RSS). Use when the user wants to add any document or web content to their knowledge graph.
graph-backup
Export the memory graph to a timestamped JSONL backup file. Use before risky operations or on demand.
graph-briefing
Generate a session briefing from the memory graph — recent changes, unresolved contradictions, relevant context for the current project. Use at the start of a session to catch up, or when switching projects.
graph-dream
Manually run the graph memory dream process to extract entities from recent conversations and ingested documents. Use when the user wants to update the graph now rather than waiting for the scheduled run.