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 mihkarp/trash-panda --skill trash-pandagit clone --depth 1 https://github.com/mihkarp/trash-pandaWrote 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/mihkarp/trash-panda/trash-panda)<a href="https://agentmods.dev/skills/mihkarp/trash-panda/trash-panda"><img src="https://agentmods.dev/badge/skills/mihkarp/trash-panda/trash-panda/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/mihkarp/trash-panda/trash-panda"><img src="https://agentmods.dev/badge/skills/mihkarp/trash-panda/trash-panda.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 | $0.00155 | $0.01723 |
| Opus 5 | $0.00077 | $0.00861 |
| Sonnet 5 | $0.00031 | $0.00345 |
| Haiku 4.5 | $0.00015 | $0.00172 |
Grade A, and why
trash-panda 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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trash Panda 🦝
He digs through your chats and drags the good stuff back to the den.
The den is a plain den/ folder of markdown — one file per project — that he
appends to. He never overwrites your hand edits.
Commands: /dig (main), /den, /sniff, /feed <chat>, /wash, /nap.
/dig — the main loop
Step 1 — Read the den (get context)
- Make sure
den/exists. If not, create it with an emptyden/index.md. - Read every
den/*.mdfile. Build a list of:- existing project files (filename → project name)
- existing artifact titles per project (for dedup)
- Read the last-dig timestamp from the top of
den/index.md(line format:<!-- last_dig: YYYY-MM-DDTHH:MM:SSZ -->). If absent, treatlast_dig= 7 days ago.
Step 2 — Read chats since the last dig
recent_chats(n=20, sort_order="desc")
If last_dig is older than 3 days, page back with before= for full coverage.
Only process chats updated AFTER last_dig.
For each chat, capture its URL so every artifact can link back:
https://claude.ai/chat/<id> (or the ChatGPT URL when digging an export).
Step 3 — Extract artifacts
Scan each chat and pull out four types:
| Type | Signals in the text |
|---|---|
| 📝 To Do | "need to", "next step", "send X", "write the…", "follow up", explicit next actions |
| 💎 Shiny | "idea", "what if", "could try", "worth exploring" — a thought worth keeping |
| 🗺️ The Plan | a decided approach, strategy, sequence of steps, "here's the plan", "we'll do X then Y" |
| 📜 Loot | something actually produced — a draft, doc, CV, reading list, snippet, named output |
For each artifact determine:
- title — short, concrete, ≤ 80 chars
- type — todo / shiny / plan / loot
- project — match an existing den project, or a new project name, or
inbox - confidence — high / mid / low
- source — the chat URL (for the backlink)
Step 4 — Dedup
Before keeping a candidate, compare against existing titles in the target project file. If a near-match already exists (>70% meaning overlap), skip it. The same Shiny must never land twice.
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 · 196 lines · 155 tokens per session scan A 15ea5328419a
trash-panda is a skill published in the GitHub repository mihkarp/trash-panda (4 stars, last pushed 2mo ago), licensed MIT. It adds 155 tokens to every session and 1,723 once invoked, about $0.0008 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 2026-08-31.
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