Sticker Manager

Sticker library management for OpenClaw. Use this skill to save, search, tag, rename, clean up, collect, import, and recommend stickers or reaction images. I...

Security Scan

The skill's files, runtime instructions, and required resources are consistent with a local sticker-management tool — it reads/writes a local sticker library and (optionally) fetches remote media and prepares vision-model plans, and it does not request unrelated credentials or perform unexplained network exfiltration.

Purpose & Capability

The repository contents (save, search, rename, batch import/collect, semantic/vision plan scripts) match the declared purpose of local sticker library management. Required artifacts (local library, inbound media) and supported formats are coherent with the features. There are no unexpected credentials or unrelated system dependencies in the manifest.

Instruction Scope

SKILL.md and scripts instruct the agent to read/write the default library (~/.openclaw/workspace/stickers/library/) and inbound media (~/.openclaw/media/inbound/) and to run included Python scripts. Several commands optionally perform network fetches (collect/discover) to user-provided URLs and prepare vision-model payloads for external model execution. These actions are consistent with the stated functionality but do require filesystem access and (for collection) outgoing HTTP requests — which is expected but worth noting.

Install Mechanism

There is no install spec and the repo is instruction-plus-scripts (Python). No downloads from arbitrary URLs or packaged executables are requested. The requirements.txt lists common packages (requests, Pillow, pytest) appropriate for the code. No high-risk install steps (URL downloads/extracts) are present in the provided files.

Credentials

The skill uses environment variables documented in SKILL.md (STICKER_MANAGER_DIR, STICKER_MANAGER_INBOUND_DIR, STICKER_MANAGER_LANG, STICKER_MANAGER_VISION_MODELS). It does not request API keys or other secrets. It references model identifiers (e.g., openai/gpt-5-mini) for vision fallback plans but does not include or require credentials — executing those model steps requires external model/tool credentials which are intentionally left to the outer agent/tooling, consistent with the README.

Persistence & Privilege

The skill is not marked always:true and does not request persistent platform privileges. It reads/writes only within defined local paths (or paths supplied by the user) and does not modify other skills' configurations. Autonomous invocation is the platform default and is not combined with other red flags.

Assessment

This skill appears to be what it claims: a local sticker manager implemented as Python scripts. Before installing or running it, consider: 1) It will read from and write to your local sticker library and inbound media directories (defaults under ~/.openclaw); set STICKER_MANAGER_DIR and STICKER_MANAGER_INBOUND_DIR to an isolated folder if you want to limit impact. 2) The collector/discovery commands will make HTTP requests to URLs you provide — only supply trusted sources. 3) Vision/auto-tag features produce plans referencing external models (e.g., openai/gpt-5-mini) but do not include or ask for API keys; enabling automated vision/model execution requires separate model/tool credentials and integration. 4) If you want extra safety, review the scripts (they are included) and run them in a sandbox or dedicated user account; run the provided tests and the included check_sensitive.py before publishing changes.

Manage a local sticker / reaction-image library with keyword lookup, quality checks, semantic tags, batch import/collection workflows, and media sending support.

Authors:

• Wenzhuang Zhu
• TetraClaw (丁蟹)

Repository / backup source:

• GitHub: https://github.com/TetraClaw/sticker-manager
• If ClawHub is unavailable, use GitHub as the fallback download source.

What this skill does

1. Save the latest inbound image or GIF into a local library
2. Save a specific image from recent chat/media history
3. Search stickers by keyword
4. Rename or delete existing stickers
5. Clean very low-quality files
6. Tag stickers with emotions / scenes / keywords / descriptions
7. Recommend a sticker based on direct text context or recent chat history
8. Batch-collect stickers toward a target count with dedupe and low-quality filtering
9. Batch-import stickers from local directories
10. Prepare vision-model plans for semantic tagging and image understanding
11. Return resolved file paths so the assistant can send matched media with the message tool

Supported formats

• JPG
• JPEG
• PNG
• WEBP
• GIF

Default paths

• Sticker library: ~/.openclaw/workspace/stickers/library/
• Inbound media: ~/.openclaw/media/inbound/

Environment overrides:

• STICKER_MANAGER_DIR
• STICKER_MANAGER_INBOUND_DIR
• STICKER_MANAGER_LANG
• STICKER_MANAGER_VISION_MODELS

Typical use cases

1. Save a sticker

When the user sends an image/GIF and says things like:

• "save sticker"
• "store this"
• "save this image"
• "save the previous image"
• "save that image from chat history"

Basic save:

python3 scripts/save_sticker.py "custom_name"

Save from recent media history:

python3 scripts/save_sticker.py --list-history
python3 scripts/save_sticker.py --history-index=2 "saved_from_history"
python3 scripts/save_sticker.py --source=file_39---example.jpg "saved_by_source"

Quality-aware save:

python3 scripts/save_sticker_auto.py "custom_name"
python3 scripts/save_sticker_auto.py --history-index=3 "quality_checked_name"

If no name is provided, save_sticker_auto.py exits with code 2 and returns analysis markers so the assistant can ask a model to name the image.

2. Search a sticker

Command:

python3 scripts/get_sticker.py "keyword"

List all stickers:

python3 scripts/get_sticker.py

Matching order:

• exact filename match
• partial filename match
• fuzzy containment match

3. Rename / delete / clean

Rename:

python3 scripts/manage_sticker.py rename "old_name" "new_name"

Delete:

python3 scripts/manage_sticker.py delete "name"

Clean very small files:

python3 scripts/manage_sticker.py clean

4. Tag and recommend

Add tags:

python3 scripts/sticker_semantic.py tag "sticker_name" "happy,calm" "meeting,celebration" "thumbs-up,approved" "A calm approval reaction image."

Suggest for direct context:

python3 scripts/sticker_semantic.py suggest "we finally fixed it"
python3 scripts/sticker_semantic.py suggest "we finally fixed it" --strategy=model

List tag database:

python3 scripts/sticker_semantic.py list

Prepare model payload only:

python3 scripts/sticker_semantic.py prepare-model "the user is nervous but pretending to be calm"

Recommend from chat history:

python3 scripts/sticker_semantic.py context-recommend ./chat_history.json --top=5

5. Batch collect / import / discover

Collect from URLs or local files:

python3 scripts/collect_stickers.py --sources-file ./sources.txt --out-dir ./stickers/batch --prefix sticker --target-count 15

If the final count is below target, the command exits with code 2 and prints NEED_MORE=....

Import from local directories:

python3 scripts/batch_import.py ./stickers --target-dir ~/.openclaw/workspace/stickers/library/
python3 scripts/batch_import.py ./stickers --auto-tag

Discover sources from directories, URLs, or pages:

python3 scripts/discover_sources.py ./stickers
python3 scripts/discover_sources.py https://example.com/image1.gif
python3 scripts/discover_sources.py https://example.com/gallery
python3 scripts/discover_sources.py --fetch-urls https://example.com/image1.gif

Default discovery is lightweight:

• local directories are scanned immediately
• remote image URLs are returned as pending unless --fetch-urls is used
• page discovery only counts successfully extracted image URLs

Animation rule (mandatory):

• If the source is originally animated, prefer downloading the animated asset itself.
• Use a generic decision path: file suffix → HTTP Content-Type → downloaded file content validation.
• Do not silently downgrade animated sources to static WEBP/PNG previews.
• If a source looks animated by reference/content-type but the downloaded file validates as static, reject it from the animated batch instead of pretending it is a GIF.
• Before sending or importing a GIF batch, verify the saved file is actually animated-capable rather than a static preview.

6. Auto-tagging and vision planning

Generate a vision plan for a single file:

python3 scripts/sticker_semantic.py auto-tag ./sticker.gif

Generate plans for a directory:

python3 scripts/sticker_semantic.py auto-tag-dir ./stickers/

Standalone vision fallback plan:

python3 scripts/sticker_semantic.py vision-plan ./sample.png "find a doubtful or suspicious emotion"

This returns a JSON plan with:

• image path
• candidate vision models
• prompt goal
• fallback failure message

Suggested default model order:

1. bailian/kimi-k2.5
2. openai/gpt-5-mini

Sending workflow

After resolving a file path, send it with the message tool.

Example:

message(
    action="send",
    channel="telegram",
    target="<chat_id>",
    media="/absolute/path/to/sticker.gif",
    caption="Here you go"
)

If you need to reply to a specific message, pass replyTo="<message_id>".

Quality rules

The auto-save flow checks media size before saving.

Level	File size	Action
High	>= 50KB	save directly
Good	>= 20KB	save directly
Medium	>= 10KB	save directly
Low	>= 5KB	usable, but lower confidence
Too low	< 5KB	reject unless forced

Force-save example:

python3 scripts/save_sticker_auto.py "low_quality_name" --force

Notes for agents

• Prefer semantic names over timestamp-heavy names
• Avoid platform-specific hardcoding in skill logic
• Use environment variables for library location overrides
• Keep send logic in the assistant/tool layer, not inside raw scripts when possible
• Validate file existence before sending
• For image meaning extraction, try the configured vision-capable model chain in STICKER_MANAGER_VISION_MODELS
• If the primary image model fails, try fallback models before giving up
• If all vision models fail, explicitly tell the user that image meaning, semantic tagging, and quality validation could not be completed reliably
• Treat __MODEL_MATCH__, __AUTO_TAG__, __SEMANTIC_BATCH__, __ANALYZE_HISTORY__, and __CONTEXT_RECOMMEND__ markers as structured handoff payloads for the outer assistant layer

Files

• scripts/common.py - shared path, i18n, and vision-plan helpers
• scripts/get_sticker.py - keyword lookup and inventory list
• scripts/manage_sticker.py - rename, delete, and cleanup
• scripts/save_sticker.py - basic save from inbound or history media
• scripts/save_sticker_auto.py - quality-aware save flow
• scripts/sticker_semantic.py - tagging, recommendation, auto-tag planning, and context analysis
• scripts/collect_stickers.py - batch collection with dedupe and semantic-plan output
• scripts/batch_import.py - local-directory import with optional auto-tag planning
• scripts/discover_sources.py - discovery from URLs, directories, and static pages
• scripts/check_sensitive.py - publish-safety scan for sensitive-looking content
• tests/ - pytest coverage for CLI and workflow behaviors