call-summarizer
Turn a finished CALL-E phone-call transcript into a structured post-call brief with a one-line outcome, a masked summary, extracted action items with owners and due dates, caller sentiment, and a redacted caller fingerprint. Use after any CALL-E call when an agent or operator nee
Perfil del activo
Investigación y trabajo de conocimiento
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Escenario
Agents de investigación
I need my agent to research a topic, compare sources, and produce a concise report.
Afinidad con Agent
Claude Code + CLI + Codex
Funciona con Codex, Claude Code, Cursor, CLI o Agents personalizados.
Instalar
Listo
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
Mantenimiento
Actual
3 días desde el último push
Riesgo
Requiere revisión
Dependency or permission surface needs review
Calidad de GitHub
63
65/100 Calidad · 66/100 Confianza
Etiquetas de cobertura
Notas de revisión
Dependency or permission surface needs review · Permission surface may require sandboxing
Tarjeta de adopción del Agent
Confianza, auditoría y preparación de instalación de un vistazo
Estas puntuaciones combinan metadatos públicos del repositorio, señales de revisión de OpenAgentSkill, actualidad de mantenimiento y preparación de instalación. Sirven para preseleccionar; no sustituyen la revisión humana.
Calidad
PrometedorUseful candidate, but compare it with alternatives before adopting.
Confianza
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Auditoría
Requiere revisiónRevisión legible por máquina de la preparación de instalación, los metadatos de seguridad, el mantenimiento y el riesgo de adopción.
Trust Score de OpenAgentSkill v5
Revisión humana antes de instalar
Choose a stronger alternative or inspect the source manually before any install attempt.
Estrellas
63 estrellas de GitHub
Actividad del repositorio
63 estrellas y 127 forks
Mantenimiento
3 días desde el último push
Licencia
MIT
Instalar
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
Seguridad de instalación
Ruta estándar de paquete o instalación en tiempo de ejecución
Superficie de permisos
shell or command execution, filesystem or document access
Resultados del Agent
Aún no hay datos de resultados del Agent
Documentación
Contexto sólido de README/SKILL.md
Resumen de riesgo
Revisar antes de producción
- The skill relies on regex-based masking for personal names, which is explicitly partial and may miss names without introduction cues. This is documented, but downstream consumers should be aware of the limitation.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: shell or command execution, filesystem or document access
Preparación de instalación
Ruta de instalación disponible
- La ruta de instalación está disponible
- La evidencia del repositorio está disponible
- La licencia está declarada
- Aún no hay evidencia de resultados Agent-Proven
Metadatos legibles por Agent
Datos de decisión legibles por máquina para este skill.
Usa este bloque o el JSON integrado para decidir si un Agent debe instalar este skill, elegir una alternativa o pedir revisión humana primero.
Tareas adecuadas
- Flujos de Agents de investigación
- Equipos de Claude Code
- builders willing to evaluate younger projects
- Fuentes de búsqueda
Agents adecuados
Decisión de instalación
- Comando
- npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
- Política
- Revisar
- Revisión humana
- Sí
Confianza y riesgo
- Confianza
- 58/100
- Auditoría
- 74/100
- Nivel de riesgo
- Requiere revisión
Ciclo de resultados
- Endpoint
- /api/agent/outcome
- ID del evento
- resolve
- Resultados
- 5
Comando de instalación
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizerNo usar cuando
- Equipos que necesitan un SLA con soporte del proveedor
- production agents without a repository review
- The skill relies on regex-based masking for personal names, which is explicitly partial and may miss names without introduction cues. This is documented, but downstream consumers should be aware of the limitation.
- Indicios de permisos de alto riesgo: ejecución de shell o comandos
- Dependency or permission surface needs review
Skill alternativo
Last30days Skill
53.5K Estrellas
npx skills add mvanhorn/last30days-skill -g
Skill alternativo
Academic Research Skills
38.4K Estrellas
npx skills add Imbad0202/academic-research-skills
Skill alternativo
GPT Researcher
28.0K Estrellas
npx skills add assafelovic/gpt-researcher
Skill alternativo
DeepResearch
19.8K Estrellas
npx skills add Alibaba-NLP/DeepResearch
Seguridad de Agent v2
42/100 · Evitar instalación automática
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
Alto
Ejecución de shell o comandos
Los metadatos del skill hacen referencia a terminal, CLI, shell, subprocesos o flujos de ejecución de comandos.
Medio
Acceso a red
El skill probablemente consulta páginas remotas, API, repositorios o servicios externos.
Medio
Acceso al sistema de archivos
El skill puede leer o escribir archivos de proyecto, documentos, artefactos generados o estado local.
Medio
Acceso a base de datos
El skill puede inspeccionar esquemas, consultar bases de datos o trabajar con almacenes persistentes.
- Indicios de permisos de alto riesgo: ejecución de shell o comandos
- Dependency or permission surface needs review
Destinos de instalación
Instala este skill en tu flujo de Agent
Usa el endpoint público para obtener el comando, la lista de seguridad, prompts y enlaces canónicos.
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install calle-ai-call-summarizerPlan de resolución de Agent
Deja que un Agent valide el ajuste antes de instalar.
La API Resolve devuelve la skill elegida, alternativas, política de seguridad, notas de auditoría, destino de instalación y un prompt listo para usar.
Abrir JSON
/api/agent/resolve?task=Use%20call-summarizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Texto de Resolve
/api/agent/resolve?task=Use%20call-summarizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Traspaso de instalación
/api/skills/calle-ai-call-summarizer/install
Agent debe revisar
- Task fit and alternatives from Resolve API.
- Audit score, trust score, and safety policy warnings.
- Install target compatibility for Codex, Claude Code, Cursor, or CLI.
Copiar prompt
Task: Use call-summarizer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20call-summarizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/calle-ai-call-summarizer/install
Install command: npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Traspaso de Agent
Da al Agent la ruta de instalación, no otro directorio.
Usa el endpoint público para obtener el comando, la lista de seguridad, prompts y enlaces canónicos.
Traspaso de instalación
/api/skills/calle-ai-call-summarizer/install
Formato de texto LLM
/api/skills/calle-ai-call-summarizer/install?format=text
Buscar alternativas
/api/skills/search?q=call-summarizer&limit=3
Prompt de Agent
Use call-summarizer for this task. Review https://www.openagentskill.com/api/skills/calle-ai-call-summarizer/install, then install with: npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizerMetadatos del Registry
Perfil legible por Agent para seleccionar skills automáticamente.
La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.
Manifest
/api/registry/manifest/calle-ai-call-summarizer
Texto LLM
/api/registry/manifest/calle-ai-call-summarizer?format=text
Alias de instalación
/api/registry/install/calle-ai-call-summarizer
Recomendar
/api/registry/recommend?task=Use%20call-summarizer%20in%20an%20agent%20workflow&limit=3
Afinidad con Agent
Agents de investigación
Plataformas
Claude Code
Informe de auditoría
Requiere revisión · 74/100
Revisión legible por máquina de la preparación de instalación, los metadatos de seguridad, el mantenimiento y el riesgo de adopción.
Panel de decisión de Agent
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Rol en la pila
Candidata de respaldo
Ajuste principal
Agents de investigación
Etiqueta de confianza
Prototipar primero
Ruta de instalación
Comando listo
Úsalo cuando
- Flujos de Agents de investigación
- Equipos de Claude Code
- builders willing to evaluate younger projects
Evidencia
- recent repository activity
- install command or GitHub repo available
- perfil de calidad 65/100
- 5 eventos de interacción de OpenAgentSkill
revisar primero
- The skill relies on regex-based masking for personal names, which is explicitly partial and may miss names without introduction cues. This is documented, but downstream consumers should be aware of the limitation.
Ruta de implementación
- 1Instálalo en un Agent de sandbox y ejecuta una tarea de Agents de investigación de principio a fin.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
Perfil de confianza
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Adopción en GitHub
Revisar63 estrellas de GitHub
Actividad de stars/forks
Revisar63 estrellas y 127 forks; la actividad de issues no está disponible en los metadatos actuales
Mantenimiento reciente
Aprobado3 días desde el último push
Claridad de licencia
AprobadoMIT
Señales positivas
- Revisión de IA aprobada
- La ruta de instalación está disponible
- La evidencia del repositorio está disponible
- Repositorio mantenido recientemente
- El comando de instalación no muestra un patrón de alto riesgo evidente
- El ciclo de resultados está listo, pero necesita la primera ejecución real de Agent
Revisar antes de instalar
- The skill relies on regex-based masking for personal names, which is explicitly partial and may miss names without introduction cues. This is documented, but downstream consumers should be aware of the limitation.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: shell or command execution, filesystem or document access
- GitHub adoption: 63 GitHub stars
- Stars/forks activity: 63 stars, 127 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, external package install surface
- Permission surface: shell or command execution, filesystem or document access
- Aún no hay informes reales de resultados del Agent
- Se requiere revisión humana antes de una instalación desatendida
Acción recomendada
Choose a stronger alternative or inspect the source manually before any install attempt.
Perfil de calidad
Prometedor candidato para flujos de Agent
Useful candidate, but compare it with alternatives before adopting.
Ajuste de flujo
Usa esta skill en estos escenarios
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Parse messy files
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Ajuste de flujo
Añadir a un flujo completo
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
Lista de alternativas
Compara antes de instalar
Similar skills that may fit this task.
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
GPT Researcher
Run autonomous deep research over web and local sources
DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
Resumen
--- name: call-summarizer description: Turn a finished CALL-E phone-call transcript into a structured post-call brief with a one-line outcome, a masked summary, extracted action items with owners and due dates, caller sentiment, and a redacted caller fingerprint. Use after any CALL-E call when an agent or operator needs an actionable, reviewable record of what was said without re-reading the whole transcript or re-playing the recording. license: MIT ---
# Call Summarizer
Use this skill after a CALL-E call has completed and the agent needs to turn the returned transcript into a compact, actionable post-call record.
`call-summarizer` is a post-call analysis skill. It takes a CALL-E call result that already contains a transcript, runs locally with no additional phone calls and no network access, and emits a single structured brief: a one-line outcome, a masked summary of the conversation, the action items with owners and due dates, the caller sentiment, and a redacted caller fingerprint for dedup.
It is a good fit for CALL-E's design: the hard part (the call) is already done, and the remaining work (turning a long transcript into something an agent can act on) is pure text analysis that should not require a second provider or a paid summarization API.
## When To Use
Use this skill for:
- turning a completed CALL-E call transcript into a one-page post-call brief - extracting action items with owners and due dates from a call - surfacing caller sentiment so a follow-up can be triaged correctly - producing a masked summary that is safe to log, store, or hand to a human - building a redacted caller fingerprint for de-duplicating repeat callers - any workflow where the call is done and the record is the deliverable
## When Not To Use
Do not use this skill to:
- place, schedule, or cancel a phone call; it only reads transcripts - summarize a call that has no transcript; it will abstain instead of inventing one - act on the action items; it reports them, the operator decides whether to execute - store PII; every output is masked and the fingerprint is one-way hashed - replace a human review for medical, legal, financial, or emergency content - run during the call; it is strictly post-call and never affects call behavior
## Workflow
### 1. Collect the call result
Required: a CALL-E call result containing a `transcript` field (the full dialogue turns between the agent and the callee). The transcript may be plain text or a list of turns; both are handled.
Confirm with the operator that this transcript belongs to a call they authorized and that they want a post-call brief generated. Never run this skill on a transcript whose origin is unknown.
### 2. Generate the brief locally
Run `scripts/summarize_call.py` on the transcript. By default it reads from a file path and prints the brief to stdout; it makes no network calls and places no calls.
```bash python3 scripts/summarize_call.py --transcript path/to/transcript.json --out brief.json ```
The script performs:
1. **Outcome line**: a single sentence stating the call result (confirmed, declined, rescheduled, no-answer, voicemail, unknown) using only words that appear in the transcript. The outcome is bound to the callee's latest effective response (agent text never counts as a confirmation), and any contradictory intent — across utterances or within a single utterance (e.g. "Yes, I can't make it") — fails closed to `unknown`. 2. **Masked summary**: a short prose summary with phone numbers, emails, account identifiers, and title-prefixed or cue-introduced personal names replaced by masked tokens. The brief sets `masked: "partial"` with a `masking_scope` field documenting exactly which PII classes are tokenized; ordinary personal names without an introduction cue are NOT redacted (the skill uses no NER model and the contract is honest about this boundary). 3. **Action items**: each commitment, follow-up, or next step extracted with an owner (the party who said they would do it), a verb, and an optional due date parsed from natural-language time references. Ambiguous items keep `owner: unknown` rather than guessing. 4. **Sentiment**: a coarse label (`positive`, `neutral`, `negative`, `mixed`) with a short justification span from the transcript. It never reports a sentiment the transcript does not support. 5. **Caller fingerprint**: a one-way hash of a stable caller identity input (the masked caller phone number, or an explicit `caller_id` field if provided). The `call_id` is deliberately excluded so the same caller produces the same fingerprint across calls, enabling de-duplication without storing PII.
### 3. Validate the brief
Run `scripts/validate_brief.py` to confirm the brief is well-formed before any downstream system consumes it. It checks that every action item has an owner, that masking has no residual raw phone numbers, emails, account identifiers, or personal names, and that the outcome line is non-empty and grounded in the transcript.
### 4. Review or route
Return the brief to the operator or the calling agent. The skill does not execute any action item; it only reports them. Routing decisions (escalate, follow up, close the ticket) stay with the operator or the host agent.
## Output Schema
The brief is a single JSON object:
```json { "outcome": "Appointment confirmed for Tuesday 10:00.", "summary": "The callee confirmed the appointment and asked for a reminder the day before.", "actions": [ { "owner": "agent", "verb": "send reminder", "due": "2026-09-15", "source_span": "I will send a reminder the day before." } ], "sentiment": { "label": "positive", "justification": "Callee confirmed without hesitation." }, "caller_fingerprint": "sha256:9f2c...", "masked": "partial", "masking_scope": "phone_numbers emails account_ids title_prefixed_names cue_introduced_names", "masking_note": "Structured PII and cued personal names are tokenized. Ordinary uncued names are NOT redacted." } ```
## Safety Rules
Read `references/safety.md` for the full safety contract.
- This skill never places a call and never modifies call state. - Every output is partially masked: phone numbers, emails, account IDs, and cued personal names are tokenized. The `masked` field is `"partial"` with a `masking_scope` documenting the boundary; ordinary uncued names are NOT redacted (no NER model). - The caller fingerprint is a one-way hash; the raw identity is never stored. - Action items are reported, not executed. Medical, legal, financial, and emergency commitments are flagged as `category: sensitive` and routed to a human rather than auto-dispatched. - If the transcript is empty, garbled, or does not support an outcome, the skill abstains with `outcome: unknown` and an empty `actions` list. It never invents a plausible outcome. - No PII leaves the local process. There is no network call and no third-party summarization API.
## Requirements
- Python 3.9 or newer. The skill uses only the Python standard library, so no `pip install` is required for the default (no-call) path. - A CALL-E call result with a transcript. Live calls are out of scope; see the `call-reminder` or `verify-by-phone` skills for placing calls.
## Quick Start
```bash # Dry run on the bundled example transcript (no calls, no network). python3 scripts/summarize_call.py \ --transcript references/example-transcript.json \ --out /tmp/brief.json
# Validate the brief. python3 scripts/validate_brief.py --brief /tmp/brief.json ```
## Examples
See `references/examples.md` for worked examples on different call types (confirmation, reschedule, no-answer, voicemail) and the expected brief for each.
Detalles técnicos
- Versión
- 1.0.0
- Licencia
- MIT
- Última actualización
- 21 ago 2026
- Publicado
- 21 ago 2026
Resumen de decisión
Candidata de respaldo
recent repository activity
Auditoría
Revisión de instalación
Revisión de instalación y adopción
- Seguridad
- 73/100
- Mantenimiento
- 100/100
- Instalar
- 92/100
Evidencia probada por Agent
Evidencia probada por Agent
Informes de resultados tras resolver, revisar, instalar y una ejecución limitada.
- Tasa de éxito
- —
- Fallo reciente
- —
- Resultados
- 0
- Calidad de salida
- —
- Fallidos
- 0
- No relevante
- 0
- Instalaciones
- 0
- Bloqueado por riesgo
- 0
- Configuración necesaria
- 0
- Producción
- 0
Aún no hay datos de resultados de Agent. La primera ejecución puede informar éxito, configuración necesaria, bloqueos de riesgo, fallo o irrelevancia mediante /api/agent/outcome.
Instalar
Añadir al flujo de Agent
Gratis y de código abierto. Revisa el informe antes de instalar en Agents de producción.
Bucle de crecimiento
Kit para compartir
Borrador basado en un caso para call-summarizer, listo para publicar manualmente en X.
call-summarizer: Turn a finished CALL-E phone-call transcript into a structured post-call brief with a one-lin... 63 stars https://www.openagentskill.com/skills/calle-ai-call-summarizer?ref=x
Respuesta opcional con comando de instalación
Listing + install path for call-summarizer: https://www.openagentskill.com/skills/calle-ai-call-summarizer?ref=x Install: npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
Fuente de la ficha
Indexado por Registry
Esta ficha se indexó desde fuentes públicas y no está marcada como oficial hasta que se apruebe una reclamación de mantenedor.
- Creador
- CALLE-AI
- Indexado por
- Índice comunitario de OpenAgentSkill
La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.
Reclamar este skillReclamación del propietario
Reclamar esta ficha de skill
Esta ficha Indexado por Registry se atribuye a CALLE-AI, pero aún no está marcada como oficial. Reclámala para añadir una señal de propietario verificado y hacer más fiables futuras actualizaciones de lanzamiento, instalación y auditoría.
Kit de enlaces para creadores
Añade las insignias de evidencia a tu README
Muestra la ficha canónica, las señales actuales de confianza y auditoría, y evidencia real de Agent-Proven donde los desarrolladores evalúan el repositorio.
[](https://www.openagentskill.com/skills/calle-ai-call-summarizer)
[](https://www.openagentskill.com/skills/calle-ai-call-summarizer)
[](https://www.openagentskill.com/skills/calle-ai-call-summarizer/audit)
[](https://www.openagentskill.com/skills/calle-ai-call-summarizer)Autor
CALLE-AI
@calle-ai
Etiquetas
Afinidad con plataforma
Señales de salud
- Estrellas de GitHub
- 63
- Puntuación de calidad
- 36/100
- Último push de GitHub
- 21 ago 2026
- Pistas del framework
- Desconocido
- Vistas de OpenAgentSkill
- 5
- Copias de instalación
- 0
- Clics externos
- 0
Señal de comunidad
Comparte si este skill resulta útil para tu flujo de Agent. Los comentarios agregados mejoran la clasificación con el tiempo.
Confianza y seguridad
Do not auto-install
- Adopción en GitHub63 estrellas de GitHubRevisar
- Actividad de stars/forks63 estrellas y 127 forks; la actividad de issues no está disponible en los metadatos actualesRevisar
- Mantenimiento reciente3 días desde el último pushAprobado
- Claridad de licenciaMITAprobado
- Completitud de README/SKILL.mdLos metadatos incluyen suficiente contexto de uso y flujo de trabajoAprobado
- Riesgo de dependencias/runtimecommand execution surface, external package install surfaceRevisar
Skills relacionados
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
53.5K EstrellasAcademic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
38.4K EstrellasGPT Researcher
Run autonomous deep research over web and local sources
28.0K EstrellasDeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
19.8K Estrellas