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neuroskill-screenshots

NeuroSkill screenshot search commands — `search-images` for OCR text and CLIP vision search, `screenshots-around` for temporal proximity, `screenshots-for-eeg` for finding what was on screen during EEG sessions, and `eeg-for-screenshots` for discovering brain state near matching screenshots. Cross-modal EEG↔screenshot bridging. Use when searching for past screen content, correlating screen activity with brain states, or exploring screenshot history.

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NeuroSkill-com/skills
Dernière activité de la source
15 avril 2026 à 19:50
Langue détectée de SKILL.md
anglais
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13
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SKILL.md
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name
neuroskill-screenshots
description
NeuroSkill screenshot search commands — `search-images` for OCR text and CLIP vision search, `screenshots-around` for temporal proximity, `screenshots-for-eeg` for finding what was on screen during EEG sessions, and `eeg-for-screenshots` for discovering brain state near matching screenshots. Cross-modal EEG↔screenshot bridging. Use when searching for past screen content, correlating screen activity with brain states, or exploring screenshot history.
# NeuroSkill Screenshot Search NeuroSkill can periodically capture screenshots of the active window, embed them with CLIP vision (ONNX), run OCR text extraction, and index both modalities in separate HNSW indices for similarity search. > **Note:** Screenshot capture must be enabled in Settings → Screenshots. > Screenshots are served via `GET /screenshots/<filename>?token=<token>` on the daemon API (auth required) > and appear as indicators on the History day-view heatmap grid. --- ## LLM Tool Calls When calling these commands via the LLM `skill` tool, use `command` + `args`: ```json {"command": "search_screenshots", "args": {"query": "browser", "k": 10}} {"command": "search_screenshots", "args": {"query": "TODO", "mode": "substring"}} {"command": "screenshots_around", "args": {"timestamp": 1740412800, "window_secs": 60}} {"command": "screenshots_for_eeg", "args": {"start_utc": 1740412800, "end_utc": 1740415500}} {"command": "eeg_for_screenshots", "args": {"query": "compiler error", "k": 5}} ``` --- ## Command Overview | Command | Direction | Description | |---|---|---| | `search-images "query"` | Text → Screenshots | Search by OCR text (semantic/substring) | | `search-images --by-image <path>` | Image → Screenshots | Search by visual similarity (CLIP) | | `screenshots-around --at <utc>` | Timestamp → Screenshots | Find screenshots near a specific time | | `screenshots-for-eeg` | **EEG → Screenshots** | Find screenshots captured during EEG epochs | | `eeg-for-screenshots "query"` | **Screenshots → EEG** | Find brain data & labels near matched screenshots | --- ## `search-images` — Search Screenshots by OCR Text or Visual Similarity Search all captured screenshots using OCR-extracted text or CLIP vision embeddings. ### OCR Text Search Modes - `semantic` (default) — embeds the query text and searches the OCR HNSW index - `substring` — direct SQL LIKE matching against raw OCR text ```bash npx neuroskill search-images "compiler error" npx neuroskill search-images "login page" --k 5 npx neuroskill search-images "TODO" --mode substring npx neuroskill search-images "dashboard" --json | jq '.results[].filename' npx neuroskill search-images "slack message" --mode semantic --k 10 npx neuroskill search-images "code review" --json | jq '.results[] | {file: .filename, app: .app_name, sim: .similarity}' ``` ### Vision Search (CLIP) Search by visual similarity using a reference image. The image is embedded via the CLIP model and compared against the screenshots HNSW index. ```bash npx neuroskill search-images --by-image reference.png npx neuroskill search-images --by-image screenshot.jpg --k 5 npx neuroskill search-images --by-image layout-mock.webp --json | jq '.results[].filename' ``` ### Options | Flag | Default | Description | |---|---|---| | `--mode <m>` | `semantic` | Search mode: `semantic` (embedding similarity) or `substring` (literal text match) | | `--by-image <path>` | — | Search by visual similarity instead of OCR text (CLIP vision) | | `--k <n>` | `20` | Maximum number of results to return | | `--json` | — | Output raw JSON (pipe-safe) | | `--full` | — | Human-readable summary + colorized JSON | ### HTTP / WebSocket API ```bash # Semantic OCR search (default): curl -s -X POST http://127.0.0.1:8375/ \ -H "Content-Type: application/json" \ -d '{"command":"search_screenshots","query":"compiler error","k":20,"mode":"semantic"}' # Substring OCR search: curl -s -X POST http://127.0.0.1:8375/ \ -H "Content-Type: application/json" \ -d '{"command":"search_screenshots","query":"TODO","k":10,"mode":"substring"}' # Vision search (base64-encoded image): IMAGE_B64=$(base64 -w0 reference.png) curl -s -X POST http://127.0.0.1:8375/ \ -H "Content-Type: application/json" \ -d "{\"command\":\"search_screenshots_by_image_b64\",\"image_b64\":\"$IMAGE_B64\",\"k\":20}" # Vision search (raw embedding vector): curl -s -X POST http://127.0.0.1:8375/ \ -H "Content-Type: application/json" \ -d '{"command":"search_screenshots_vision","vector":[0.1, 0.2, ...],"k":20}' ``` ### JSON Response ```jsonc { "command": "search_screenshots", "ok": true, "query": "compiler error", "mode": "semantic", // or "vision" for --by-image "k": 20, "count": 3, "results": [ { "timestamp": 20260315143025, "unix_ts": 1741963825, "filename": "20260315/20260315143025.webp", "app_name": "VS Code", "window_title": "skill — cli.ts", "ocr_text": "error[E0308]: mismatched types expected…", "similarity": 0.87 } ] } ``` --- ## `screenshots-around` — Find Screenshots Near a Timestamp Find all screenshots captured within a time window (±seconds) around a specific Unix timestamp. Useful for seeing what was on screen during a specific EEG moment. ```bash npx neuroskill screenshots-around --at 1740412800 npx neuroskill screenshots-around --at 1740412800 --seconds 120 npx neuroskill screenshots-around --at 1740412800 --json | jq '.results[].filename' ``` ### Options | Flag | Default | Description | |---|---|---| | `--at <utc>` | (required) | Target Unix timestamp in seconds | | `--seconds <n>` | `60` | Window size: ±N seconds around the target | ### HTTP / WebSocket API ```bash curl -s -X POST http://127.0.0.1:8375/ \ -H "Content-Type: application/json" \ -d '{"command":"screenshots_around","timestamp":1740412800,"window_secs":60}' ``` ### JSON Response ```jsonc { "command": "screenshots_around", "ok": true, "count": 4, "results": [ { "timestamp": 20260224075945, "unix_ts": 1740412785, "filename": "20260224/20260224075945.webp", "app_name": "Firefox", "window_title": "GitHub - Pull Request", "ocr_text": "Merge pull request #42..." } ] } ``` --- ## `screenshots-for-eeg` — EEG → Screenshots Find screenshots captured near EEG recording timestamps. Given an EEG time range (auto: latest session, or `--start`/`--end`), finds all EEG embedding timestamps in that range, then returns screenshots within `--window` seconds (default ±30s) of each epoch. This is the **"EEG → screenshots" cross-modal bridge**: start from brain data, discover what was on screen at that moment. ```bash npx neuroskill screenshots-for-eeg # auto: last session npx neuroskill screenshots-for-eeg --start 1740412800 --end 1740415500 npx neuroskill screenshots-for-eeg --window 60 # ±60s around each epoch npx neuroskill screenshots-for-eeg --limit 20 # max 20 results npx neuroskill screenshots-for-eeg --json | jq '.results[].screenshot.filename' npx neuroskill screenshots-for-eeg --json | jq '.results[] | {eeg: .eeg_timestamp_utc, file: .screenshot.filename}' ``` ### Options | Flag | Default | Description | |---|---|---| | `--start <utc>` | auto (latest session) | Start of EEG range (unix seconds) | | `--end <utc>` | auto (latest session) | End of EEG range (unix seconds) | | `--window <n>` | `30` | Temporal window: ±N seconds around each EEG epoch | | `--limit <n>` | `50` | Maximum number of screenshot results | ### HTTP / WebSocket API ```bash curl -s -X POST http://127.0.0.1:8375/ \ -H "Content-Type: application/json" \ -d '{"command":"screenshots_for_eeg","start_utc":1740412800,"end_utc":1740415500,"window_secs":30,"limit":50}' ``` ### JSON Response ```jsonc { "command": "screenshots_for_eeg", "ok": true, "start_utc": 1740412800, "end_utc": 1740415500, "window_secs": 30, "eeg_count": 541, "count": 12, "results": [ { "eeg_timestamp_utc": 1740413130, "screenshot": { "timestamp": 20260224080530, "unix_ts": 1740413128, "filename": "20260224/20260224080530.webp", "app_name": "VS Code", "window_title": "main.rs", "ocr_text": "fn dispatch(app: &AppHandle…", "similarity": 0.0 } } ] } ``` --- ## `eeg-for-screenshots` — Screenshots → EEG Find EEG data and labels near screenshots matching an OCR text query. This is the **"screenshots → EEG" cross-modal bridge**: start from what was on screen, discover what your brain was doing at that moment. **Pipeline:** 1. Search screenshots by OCR text (semantic or substring mode). 2. For each matched screenshot, find EEG labels within `--window` seconds. 3. Report the associated EEG session and labels alongside each screenshot. Use this to answer: *"When I was looking at [X], what was my brain doing?"* ```bash npx neuroskill eeg-for-screenshots "compiler error" npx neuroskill eeg-for-screenshots "TODO" --mode substring --k 5 npx neuroskill eeg-for-screenshots "dashboard" --window 120 npx neuroskill eeg-for-screenshots "login page" --json | jq '.results[].labels[].text' npx neuroskill eeg-for-screenshots "slack" --json | jq '.results[] | {file: .screenshot.filename, labels: [.labels[].text]}' ``` ### Options | Flag | Default | Description | |---|---|---| | `--mode <m>` | `semantic` | Search mode: `semantic` or `substring` | | `--k <n>` | `10` | Maximum number of screenshot matches | | `--window <n>` | `60` | Temporal window: ±N seconds to search for EEG labels | ### HTTP / WebSocket API ```bash curl -s -X POST http://127.0.0.1:8375/ \ -H "Content-Type: application/json" \ -d '{"command":"eeg_for_screenshots","query":"compiler error","k":10,"window_secs":60,"mode":"semantic"}' ``` ### JSON Response ```jsonc { "command": "eeg_for_screenshots", "ok": true, "query": "compiler error", "mode": "semantic", "k": 10, "window_secs": 60, "screenshot_count": 3, "count": 3, "results": [ { "screenshot": { "timestamp": 20260315143025, "unix_ts": 1741963825, "filename": "20260315/20260315143025.webp", "app_name": "VS Code", "window_title": "skill — cli.ts", "ocr_text": "error[E0308]: mismatched types expected…", "similarity": 0.87 }, "labels": [ {
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Ce SKILL.md est tres volumineux, SkillsMP affiche donc ici seulement la premiere section. Voir sur GitHub