Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add mikeparcewski/wicked-garden/plugin install wicked-gardenWrote 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/mikeparcewski/wicked-garden/mem-ingest)<a href="https://agentmods.dev/skills/mikeparcewski/wicked-garden/mem-ingest"><img src="https://agentmods.dev/badge/skills/mikeparcewski/wicked-garden/mem-ingest/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/mikeparcewski/wicked-garden/mem-ingest"><img src="https://agentmods.dev/badge/skills/mikeparcewski/wicked-garden/mem-ingest.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 | $0.00129 | $0.01441 |
| Opus 5 | $0.00064 | $0.00720 |
| Sonnet 5 | $0.00026 | $0.00288 |
| Haiku 4.5 | $0.00013 | $0.00144 |
Grade A, and why
wicked-garden-mem-ingest scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mem Ingest Worker
You ingest source files into the wicked-estate knowledge store so later
knowledge.recall / cited-answer calls can return them with source
citations. This ports the brain-era ingest pipeline (FOLD-2, Phase 5-S7)
onto estate: you read, extract, and chunk (DEC-R: the agent is the
reasoner); the engine writes and ranks (knowledge.ingest /
knowledge.write — deterministic, never calls a model). Estate's embedded
method card skill://knowledge-ingest/SKILL.md is the chunking doctrine
this pipeline follows.
Parameters
- source (required): path to a file or directory
- scope (optional): estate scope, default
project:<cwd-basename> - title (optional): document title, default derived from the filename
The write seam
All writes go through the mem backend (stdio shim → estate MCP):
sh "${CLAUDE_PLUGIN_ROOT}/scripts/_python.sh" \
"${CLAUDE_PLUGIN_ROOT}/scripts/mem/estate_memory.py" ingest -
with a JSON body on stdin (chunks are long — never inline them in argv):
{"title": "<doc title>", "chunks": ["<chunk 1>", "<chunk 2>"],
"scope": "<scope>", "source": "<absolute-or-repo-relative source path>"}
Rules that make chunks worth storing:
- Self-contained — one idea per chunk (a section, a slide, a sheet, a table row group). Recall returns chunks verbatim; a fragment that needs its neighbours is a bad chunk.
- Source provenance always —
sourceis what a cited answer cites. An uncited chunk cannot backwicked-garden-search answer. Never omit it. - For a single standalone fact, use the
writeaction instead ({"content": ..., "class": "concept", "source": ...}).
Step 1 — assess the source
- Single file → Step 2 (text) or Step 3 (binary) by extension.
- Directory → Step 4 (batch).
- Health check when in doubt:
... estate_memory.py health '{}'— on"ok": false, stop and report the degrade (estate binary missing or store unreachable). Do not fake success.
Text extensions: .md .txt .csv .html .htm .json .py .js .jsx .ts .tsx .sh .rs .go .java .yaml .yml .toml. Binary (vision) extensions: .pdf .docx .pptx .xlsx .png .jpg .jpeg .gif .webp.
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.
- 11d ago First seen · 132 lines · 129 tokens per session scan A d56c0c0eee33
wicked-garden-mem-ingest is a skill published in the GitHub repository mikeparcewski/wicked-garden (9 stars, last pushed today), licensed MIT. It adds 129 tokens to every session and 1,441 once invoked, about $0.0006 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.
Other skills, from other repositories
obsidian
Obsidian vault: search/read/write notes, backlinks, Bases, Canvas.
loopx-doc-registry
Use when a connected LoopX project is asked to read, remember, record, index, register, or use a durable project material such as a Lark/wiki/design doc, research note, SOP, owner packet, migration report, benchmark paper, or external material source. Use even when the user does not mention LoopX or doc registry.
mindos-zh
A local knowledge-base assistant for storing and finding decisions, meeting notes, procedures, troubleshooting lessons, and preferences across conversations and coding agents. It works only with the MindOS knowledge base.
excel-author
Create and edit Excel (.xlsx) workbooks with openpyxl. Supports formulas, charts, formatting, and data analysis.
ppt-author
Create and edit PowerPoint (.pptx) presentations programmatically. Requires python-pptx.
ocr-document
Extract text from PDFs, images, and scanned documents. Uses pymupdf (local) or optional cloud OCR APIs.