# Autoskill

> Observa la pantalla del usuario vía screenpipe, detecta flujos de trabajo repetidos, los compara con las skills existentes y redacta nuevas skills o recetas de composición para los patrones aún no cubiertos.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/autoskill
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/autoskill.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: MIT license
Actualizado: el mes pasado
Coste de contexto: 152 tok instalada, 3k tok al activarse, 10.7k tok con todos los archivos del bundle
Bundle: 15 archivos, 42 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill autoskill --agent cline
```

## Qué hace

- Consulta la API local del daemon screenpipe para obtener el historial de pantalla del usuario en una ventana de tiempo
- Elimina emails, claves API, tokens y teléfonos de los datos capturados antes de cualquier análisis
- Agrupa sesiones repetidas en clústeres por app y las compara con las skills existentes mediante embeddings locales
- Clasifica cada clúster como reuse, compose o novel y redacta un SKILL.md nuevo o una receta de composición cuando corresponde
- Permite promover una propuesta revisada a skills/<name>/ con scripts/autoskill.py promote

## Cuándo usarla

- El usuario pide analizar su trabajo reciente y proponer skills basadas en lo que realmente hace
- Se quiere encontrar recetas de composición para flujos de trabajo que se repiten
- Se quiere redactar una skill nueva a partir del historial de trabajo reciente

## Cuándo no

- Para preguntas puntuales sobre screenpipe en sí o consultas de pantalla en tiempo real
- Sin una solicitud explícita del usuario, porque analiza contenido local sensible
- Si el daemon screenpipe no está accesible en localhost:3030, la skill se niega a ejecutarse

## Qué la activa

- "Analiza mi última semana de trabajo y proponme nuevas skills"
- "Mira lo que he estado haciendo y dime qué no está cubierto todavía"
- "Redacta una skill a partir de mi flujo de trabajo reciente"

## Antes de instalar

- Necesita el daemon screenpipe corriendo en localhost:3030 con SCREENPIPE_TOKEN, además de un backend LLM local (LM Studio) o, opcionalmente, ANTHROPIC_API_KEY o FOUNDRY_API_KEY.
- Necesita en el PATH: git, python
- Variables de entorno: ANTHROPIC_API_KEY, FOUNDRY_API_KEY, SCREENPIPE_TOKEN
- makes network requests
- needs API credentials

## Archivos

- .gitignore — 34 B
- SKILL.md — 12 KB
- config.yaml — 2 KB
- references/https-proxy.md — 1 KB
- references/screenpipe-config.yaml — 2 KB
- scripts/autoskill.py — 1 KB
- scripts/backends.py — 4 KB
- scripts/cluster.py — 2 KB
- scripts/doctor.py — 3 KB
- scripts/fetch_window.py — 1 KB
- scripts/match_skills.py — 1 KB
- scripts/promote.py — 2 KB
- scripts/redact.py — 2 KB
- scripts/run.py — 7 KB
- scripts/synthesize.py — 2 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo MIT license. Esta sección es el documento original y está en inglés.

# autoskill

> **Requires a running [screenpipe](https://github.com/screenpipe/screenpipe) daemon.** This skill has no alternate data source — it reads exclusively from the local screenpipe HTTP API (default `http://localhost:3030`). If the daemon isn't running, `run()` raises `ScreenpipeUnreachable` with install instructions.

> **Network access & environment variables.** This skill makes authenticated HTTP requests to (a) the user's local screenpipe daemon on loopback, and (b) the user-configured LLM backend — one of `http://localhost:1234/v1` (LM Studio, default), `https://api.anthropic.com` (opt-in Claude), or a user-supplied BYOK Foundry gateway. The skill reads three environment variables — `SCREENPIPE_TOKEN`, `ANTHROPIC_API_KEY`, `FOUNDRY_API_KEY` — and uses each only to authenticate to the single endpoint its name implies. No other network destinations, no telemetry, no data egress to any third party.

## Overview

Turn the user's own workflow history — captured passively by the local [screenpipe](https://github.com/screenpipe/screenpipe) daemon — into new skills. This skill is on-demand: the user invokes it with a time window, it queries screenpipe's local HTTP API, clusters repeated workflow patterns, compares each pattern against the existing skills in this repo, and produces a staged folder of proposals the user can review, edit, and promote.

## When to Use This Skill

Invoke this skill when the user asks to:
- "Analyze my last 4 hours / day / week and propose new skills."
- "Look at what I've been doing and tell me what's not covered yet."
- "Draft a skill from my recent workflow."
- "Find composition recipes for workflows I repeat."

Do **not** invoke it for one-off questions about screenpipe itself, for real-time screen queries, or without an explicit user request — the skill analyzes sensitive local content and must stay explicitly user-triggered.

## Privacy Posture

- **Screenpipe handles app/window filtering at capture time.** Install a starter deny-list by copying `references/screenpipe-config.yaml` into the user's screenpipe config. Sensitive apps (password managers, messaging, banking) are never OCR'd in the first place.
- **Raw OCR never leaves the machine.** `scripts/fetch_window.py` pulls data over localhost HTTP. `scripts/cluster.py` reduces the timeline to app/duration/title summaries. `scripts/redact.py` strips emails, API keys, bearer tokens, and phone numbers as defense-in-depth before any cluster summary reaches the LLM.
- **LLM backend defaults to `local`.** The recommended setup is [LM Studio](https://lmstudio.ai/) running `Gemma-4-31B-it` — strong reasoning at a size that fits on most workstation GPUs, and no data ever leaves your machine. Cloud backends (`claude`, `foundry`) are opt-in and documented in `config.yaml` for users who explicitly want them. Detection and embeddings always run locally regardless of backend choice.
- **Dry-run mode** (`--plan`) prints the exact timeline that will be analyzed before any LLM call.
- **TLS for localhost** (optional, for corporate policy): see `references/https-proxy.md` for the Caddy pattern.

## Prerequisites

### 1. Screenpipe daemon

Either install the official release or build from source. Either way the daemon binds HTTP on `localhost:3030` by default.

**From source** (recommended if you want the CLI daemon without the desktop GUI):

```bash
git clone --depth 1 https://github.com/mediar-ai/screenpipe.git
cd screenpipe
cargo build -p screenpipe-engine --release
# System deps (macOS): cmake + full Xcode.app (not just Command Line Tools).
#   brew install cmake
#   # if xcodebuild plug-ins error: sudo xcodebuild -runFirstLaunch
./target/release/screenpipe doctor   # confirm permissions + ffmpeg
./target/release/screenpipe record --disable-audio --use-pii-removal
```

First run will prompt for macOS Screen Recording permission. Grant it and relaunch.

### 2. Screenpipe API token

The local API now requires bearer auth. Retrieve your token and export it:

```bash
export SCREENPIPE_TOKEN=$(screenpipe auth token)
```

(Or set `screenpipe.token` directly in `config.yaml` — env var is preferred since it keeps secrets out of version control.)

### 3. Python environment

Via `pipenv` from the repo root:

```bash
pipenv install httpx pyyaml sentence-transformers
```

The embedding model (`sentence-transformers/all-MiniLM-L6-v2`, ~80 MB) downloads on first run.

### 4. Local LLM (default path) — LM Studio

- Install [LM Studio](https://lmstudio.ai/).
- Download `Gemma-4-31B-it` (or another strong reasoning model; adjust `local.model` in `config.yaml`).
- Load it via the CLI for headless use (no GUI required):

```bash
lms load gemma-4-31b-it --context-length 131072 --gpu max -y
lms status   # confirm server running on :1234
```

### 5. Cloud LLM backends (optional, opt-in)

Only if you explicitly opt out of local:
- `claude`: set `ANTHROPIC_API_KEY`, flip `backend: claude` in `config.yaml`.
- `foundry`: set `FOUNDRY_API_KEY`, flip `backend: foundry`, set `foundry.endpoint` to your corporate gateway URL.

## Architecture

```
screenpipe daemon (user-installed)
        │  HTTP on localhost:3030
        ▼
scripts/fetch_window.py    → normalized timeline events
scripts/redact.py          → regex scrub (defense-in-depth)
scripts/cluster.py         → sessions + clusters (local only)
scripts/match_skills.py    → top-k vs existing 135 skills (local embeddings)
scripts/synthesize.py      → LLM judge: reuse / compose / novel
        │
        ▼
~/.autoskill/proposed/<timestamp>/        (default; override with --out)
  ├── report.md
  ├── composition-recipes/<name>/SKILL.md
  └── new-skills/<name>/SKILL.md

scripts/promote.py         → user-approved proposal → skills/<name>/
```

## Workflow

The skill ships a unified CLI at `scripts/autoskill.py` with three subcommands:

```bash
python scripts/autoskill.py doctor   --config config.yaml --skills-dir ../
python scripts/autoskill.py run      --start ... --end ... --config config.yaml
python scripts/autoskill.py promote  --proposed ~/.autoskill/proposed/<ts> --skills-dir ../ --name <skill>
```

### 0. Preflight with `doctor`

Before a full run, verify every dependency in one shot:

```bash
python scripts/autoskill.py doctor \
  --config skills/autoskill/config.yaml \
  --skills-dir skills
```

The report covers `config` (backend choice valid), `skills_dir` (exists), `screenpipe` (reachable + authed), and `llm` (LM Studio serving or API key present). Non-zero exit on any failure, with the offending line marked `error`.

### 1. Run the pipeline

```bash
export SCREENPIPE_TOKEN=$(screenpipe auth token)
python scripts/autoskill.py run \
  --start "2026-04-17T00:00:00Z" \
  --end   "2026-04-17T23:59:59Z" \
  --config skills/autoskill/config.yaml \
  --skills-dir skills
```

Proposals land in `~/.autoskill/proposed/<timestamp>/` by default, keeping experimental output out of the skills repo. Pass `--out PATH` to override.

Internally:
1. **Fetch** — `fetch_window` paginates screenpipe's `/search` endpoint, normalizes events to `{ts, app, window_title, text, content_type}`.
2. **Redact** — `redact` scrubs emails, API keys, bearer tokens, phones from OCR text and window titles as defense-in-depth over screenpipe's own PII removal.
3. **Cluster** — `segment_sessions` splits on idle gaps (default 10 min) and drops short sessions; `cluster_sessions` groups sessions by app-signature and keeps clusters of size `min_cluster_size` (default 2).
4. **Match** — `load_skill_descriptions` reads frontmatter from every `SKILL.md` in `skills/`; `top_k_matches` ranks each cluster against all skills using local `sentence-transformers` embeddings (cosine similarity).
5. **Synthesize** — `synthesize` prompts the configured LLM backend to classify each cluster as `reuse`, `compose`, or `novel` and emit a SKILL.md body where appropriate.
6. **Report** — writes `<out_dir>/<ts>/report.md`, plus `new-skills/<name>/SKILL.md` or `composition-recipes/<name>/SKILL.md` for each proposal.

Add `--dry-run` to stop after clustering; this skips the LLM (and the sentence-transformers load), writing only `plan.md` for inspection.

### 2. Review and promote

Open `~/.autoskill/proposed/<ts>/report.md`, edit drafts in place, delete anything you don't want. Then:

```bash
python scripts/autoskill.py promote \
  --proposed ~/.autoskill/proposed/2026-04-17T14-30-00 \
  --skills-dir skills \
  --name zotero-pubmed-helper
```

`promote` moves the directory into `skills/<name>/`, refusing to overwrite an existing skill. Exits non-zero with a friendly error if the proposal isn't found or the target already exists.

## Configuration

See `config.yaml` for the full shape. Default values (local-first):

```yaml
backend: local
local:
  endpoint: http://localhost:1234/v1   # LM Studio's Developer server
  model: Gemma-4-31B-it

screenpipe:
  url: http://localhost:3030           # or https://screenpipe.local via Caddy

cluster:
  min_session_minutes: 5
  idle_gap_minutes: 10
  min_cluster_size: 2
```

To opt into a cloud backend:

```yaml
backend: claude                         # or foundry
claude:
  model: claude-opus-4-7
```

## Composition recipes vs new skills

- **compose**: the LLM judged that chaining existing skills covers the workflow. The emitted SKILL.md is intentionally thin — frontmatter + a "Workflow" section that invokes existing skills in order. The same agent runtime that discovered the skill can then invoke it end-to-end.
- **novel**: no combination of existing skills covers it. A fuller SKILL.md is drafted, still following repo conventions (frontmatter, Overview, When to Use, Workflow). The user should always review new-skill drafts before promoting.

## Testing

The skill is covered by a small pytest suite at `tests/autoskill/` in the repository root. Each script is unit-tested in isolation with dependency injection (mock HTTP transport, stub backend, stub embedder):

```bash
python -m pytest tests/autoskill -v
```

## Composition with other skills in this repo

The autoskill's embedding index covers all 135 sibling skills. Workflows that look like scientific writing will match `scientific-writing` / `literature-review` / `citation-management`; figure work will match `scientific-schematics` / `generate-image` / `infographics`; slide prep matches `scientific-slides` / `pptx`; etc. When a cluster scores high against two or three sibling skills the emitted composition recipe names them explicitly, so the user's future agent invocations use the optimized paths already documented in this repo.

## Dónde encaja

- Categoría: [Automatización](https://skillsagentes.com/categorias/automatizacion.md) — Flujos de varios pasos que se ejecutan sin supervisión.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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