# Read Memories

> >

- **Type:** Skill
- **Install:** `agentstack add skill-opengeos-geoai-skills-read-memories`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [opengeos](https://agentstack.voostack.com/s/opengeos)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [opengeos](https://github.com/opengeos)
- **Source:** https://github.com/opengeos/geoai-skills/tree/main/skills/read-memories

## Install

```sh
agentstack add skill-opengeos-geoai-skills-read-memories
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

Use this to recover your own context from past sessions. Do NOT narrate the
process to the user -- silently run the query, absorb the results, and continue
with enriched context.

`$0` is the keyword to search for.
Pass `--here` as an argument to scope the search to the current project only.
Pass `--geo` as an argument to additionally extract geospatial-specific context
(EPSG codes, bounding boxes, CRS info, spatial file paths, model names).

## Step 1 -- Set the search path

```bash
ALL_PROJECTS="$HOME/.claude/projects/*/*.jsonl"
CURRENT_PROJECT="$HOME/.claude/projects/$(echo "$PWD" | sed 's|[/_]|-|g')/*.jsonl"
```

Use `$CURRENT_PROJECT` if any argument is `--here`, otherwise use `$ALL_PROJECTS`.
Store the chosen glob in `SEARCH_PATH`.

Check whether the `--geo` flag is present.

## Step 2 -- Query with Python

Run the following Python script via `python3 -c "..."`, substituting
`` and `` with the resolved values. Escape any
single quotes in `` before embedding it.

```bash
python3 -c "
import json, glob, os

SEARCH_PATH = ''
KEYWORD = ''.lower()
LIMIT = 40

files = sorted(glob.glob(os.path.expanduser(SEARCH_PATH)))
results = []

for fpath in files:
    parts = fpath.split('/')
    try:
        proj_idx = parts.index('projects') + 1
        project = parts[proj_idx] if proj_idx = LIMIT:
                break
    if len(results) >= LIMIT:
        break

print(f'Found {len(results)} results (limit {LIMIT})')
print('---')
for i, r in enumerate(results):
    print(f'[{i+1}] project={r[\"project\"]} ts={r[\"ts\"]} role={r[\"role\"]}')
    print(r['content'][:800])
    print('---')
"
```

## Step 3 -- Handle large result sets

If Step 2 reports exactly 40 results (limit hit), the keyword is common. Run a
counting pass to understand the scope:

```bash
python3 -c "
import json, glob, os

SEARCH_PATH = ''
KEYWORD = ''.lower()

files = sorted(glob.glob(os.path.expanduser(SEARCH_PATH)))
total = 0
by_project = {}

for fpath in files:
    parts = fpath.split('/')
    try:
        proj_idx = parts.index('projects') + 1
        project = parts[proj_idx] if proj_idx '
KEYWORD = ''.lower()

patterns = {
    'epsg_codes': re.compile(r'EPSG[:\s]*(\d{4,5})', re.IGNORECASE),
    'bbox': re.compile(r'(?:bbox|bounding.?box|bounds)\s*[=:]\s*\[([^\]]+)\]', re.IGNORECASE),
    'crs': re.compile(r'(?:CRS|SRS|projection)\s*[=:]\s*[\"\\']?([^\"\\'\\n,;]{3,60})', re.IGNORECASE),
    'spatial_files': re.compile(r'[\w/.-]+\.(?:shp|gpkg|geojson|tiff?|nc|hdf[45]?|gdb|fgb|kml|las|laz|parquet)', re.IGNORECASE),
    'coords': re.compile(r'(?:lat(?:itude)?|lon(?:gitude)?|lng)\s*[=:]\s*(-?\d+\.?\d*)', re.IGNORECASE),
    'models': re.compile(r'(?:sam2?|segment.?anything|yolo\w*|resnet\w*|u-?net|deeplabv3|mask.?rcnn|faster.?rcnn|swin|vit|dinov?\d?|geoclip|satlas|clay|prithvi)', re.IGNORECASE),
    'resolutions': re.compile(r'(\d+(?:\.\d+)?)\s*(?:m|meter|cm|km)\s*(?:resolution|pixel|spacing)', re.IGNORECASE),
}

files = sorted(glob.glob(os.path.expanduser(SEARCH_PATH)))
findings = {k: set() for k in patterns}

for fpath in files:
    with open(fpath, 'r', errors='replace') as f:
        for line in f:
            try:
                obj = json.loads(line)
            except (json.JSONDecodeError, ValueError):
                continue
            msg = obj.get('message')
            if not isinstance(msg, dict):
                continue
            role = msg.get('role')
            if role not in ('user', 'assistant'):
                continue
            content = msg.get('content', '')
            if isinstance(content, list):
                text = ' '.join(
                    c.get('text', '')
                    for c in content
                    if isinstance(c, dict) and 'text' in c
                )
            elif isinstance(content, str):
                text = content
            else:
                continue
            if KEYWORD not in text.lower():
                continue

            for name, pat in patterns.items():
                for m in pat.finditer(text):
                    findings[name].add(m.group(0).strip())

print('=== Geospatial Context ===')
for name, vals in findings.items():
    if vals:
        print(f'{name}:')
        for v in sorted(vals)[:20]:
            print(f'  - {v}')
"
```

## Step 5 -- Internalize

From the results, extract:
- Decisions made and their rationale
- Patterns and conventions established (coordinate systems, data formats, naming)
- Data file paths and datasets previously used
- CRS/EPSG codes that were chosen and why
- Bounding boxes or areas of interest
- Model configurations (architecture, hyperparameters, checkpoints)
- Unresolved items or open TODOs
- Any corrections the user made to your prior behavior

Use this to inform your current response. Do not repeat back the raw logs
to the user.

## Notes

- **No external dependencies**: This skill uses only Python standard library
  modules (json, glob, os, re). No pip install is needed.
- **Privacy**: All data stays local. Nothing is sent over the network.
- **Content types**: The search covers both user messages and assistant
  responses. It skips system messages, tool_use blocks, and tool_result
  blocks (only the `text` type within content arrays is extracted).

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [opengeos](https://github.com/opengeos)
- **Source:** [opengeos/geoai-skills](https://github.com/opengeos/geoai-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** yes
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** yes

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-opengeos-geoai-skills-read-memories
- Seller: https://agentstack.voostack.com/s/opengeos
- Browse the marketplace: https://agentstack.voostack.com/browse

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