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
Asset-Profil
Recherche und Wissensarbeit
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Szenario
Recherche-Agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent-Fit
Claude Code + CLI + Codex
Geeignet für Codex, Claude Code, Cursor, CLI oder benutzerdefinierte Agents.
Installieren
Bereit
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
Wartung
Aktuell
2 Tage seit dem letzten Push
Risiko
Prüfung nötig
Dependency or permission surface needs review
GitHub-Qualität
63
65/100 Qualität · 66/100 Vertrauen
Abdeckungs-Tags
Review-Notizen
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent-Adoptionskarte
Vertrauen, Audit und Installationsbereitschaft auf einen Blick
Diese Werte kombinieren öffentliche Repository-Metadaten, OpenAgentSkill-Reviewsignale, Wartungsaktualität und Installationsbereitschaft. Sie helfen bei der Vorauswahl, ersetzen aber keine menschliche Prüfung.
Qualität
VielversprechendUseful candidate, but compare it with alternatives before adopting.
Vertrauen
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Prüfung nötigMaschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
OpenAgentSkill Trust Score v5
Menschliche Prüfung vor Installation
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
63 GitHub-Stars
Repository-Aktivität
63 Stars und 127 Forks
Wartung
2 Tage seit dem letzten Push
Lizenz
MIT
Installieren
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
Installationssicherheit
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsfläche
shell or command execution, filesystem or document access
Agent-Ergebnisse
Noch keine Agent-Ergebnisdaten
Dokumentation
Starker README/SKILL.md-Kontext
Risikoübersicht
Vor Produktion prüfen
- 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
Installationsbereitschaft
Installationspfad verfügbar
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Lizenz ist angegeben
- Noch keine Agent-Proven-Ergebnisbelege
Agent-lesbare Metadaten
Maschinenlesbare Entscheidungsdaten für diesen Skill.
Nutze diesen Block oder das eingebettete JSON, um zu entscheiden, ob ein Agent diesen Skill installieren, eine Alternative wählen oder zuerst menschliche Prüfung anfordern soll.
Geeignete Aufgaben
- Research-Agent-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
- Suchquellen
Geeignete Agents
Installationsentscheidung
- Befehl
- npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer
- Richtlinie
- Prüfen
- Menschliche Prüfung
- Ja
Vertrauen und Risiko
- Vertrauen
- 58/100
- Audit
- 74/100
- Risikoebene
- Prüfung nötig
Ergebnis-Loop
- Endpoint
- /api/agent/outcome
- Event-ID
- resolve
- Ergebnisse
- 5
Installationsbefehl
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizerNicht verwenden, wenn
- Teams, die ein vom Anbieter unterstütztes SLA benötigen
- 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.
- Hinweise auf Hochrisiko-Berechtigungen: Shell- oder Befehlsausführung
- Dependency or permission surface needs review
Alternative
Last30days Skill
53.5K Stars
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Academic Research Skills
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
GPT Researcher
28.0K Stars
npx skills add assafelovic/gpt-researcher
Alternative
DeepResearch
19.8K Stars
npx skills add Alibaba-NLP/DeepResearch
Agent-Sicherheit v2
42/100 · Automatische Installation vermeiden
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
Hoch
Shell- oder Befehlsausführung
Die Skill-Metadaten verweisen auf Terminal-, CLI-, Shell-, Subprozess- oder Befehlsausführungs-Workflows.
Mittel
Netzwerkzugriff
Die Skill ruft wahrscheinlich Remote-Seiten, APIs, Repositories oder externe Dienste ab.
Mittel
Dateisystemzugriff
Die Skill kann Projektdateien, Dokumente, generierte Artefakte oder den lokalen Arbeitsbereich lesen oder schreiben.
Mittel
Datenbankzugriff
Die Skill kann Schemata prüfen, Datenbanken abfragen oder mit persistenten Speichern arbeiten.
- Hinweise auf Hochrisiko-Berechtigungen: Shell- oder Befehlsausführung
- Dependency or permission surface needs review
Installationsziele
Diesen Skill im Agent-Workflow installieren
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
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-summarizerAgent-Auflösungsplan
Lass einen Agent die Eignung vor der Installation prüfen.
Die Resolve API liefert die beste Skill, Alternativen, Sicherheitsrichtlinien, Auditnotizen, Installationsziel und einen direkt nutzbaren Prompt.
JSON öffnen
/api/agent/resolve?task=Use%20call-summarizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve-Text
/api/agent/resolve?task=Use%20call-summarizer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Installationsübergabe
/api/skills/calle-ai-call-summarizer/install
Agent sollte prüfen
- 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.
Prompt kopieren
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.Agent-Übergabe
Gib dem Agent den Installationspfad, nicht noch ein Verzeichnis.
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
Installationsübergabe
/api/skills/calle-ai-call-summarizer/install
LLM-Textformat
/api/skills/calle-ai-call-summarizer/install?format=text
Alternativen finden
/api/skills/search?q=call-summarizer&limit=3
Agent-Prompt
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-summarizerRegistry-Metadaten
Agent-lesbares Profil für die automatische Skill-Auswahl.
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Manifest
/api/registry/manifest/calle-ai-call-summarizer
LLM-Text
/api/registry/manifest/calle-ai-call-summarizer?format=text
Installationsalias
/api/registry/install/calle-ai-call-summarizer
Empfehlen
/api/registry/recommend?task=Use%20call-summarizer%20in%20an%20agent%20workflow&limit=3
Agent-Fit
Recherche-Agents
Plattformen
Claude Code
Audit-Bericht
Prüfung nötig · 74/100
Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
Agent-Entscheidungspanel
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Rolle im Stack
Fallback-Kandidat
Primäre Eignung
Recherche-Agents
Vertrauenslabel
Zuerst prototypisieren
Installationspfad
Befehl bereit
Verwenden wenn
- Research-Agent-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
Evidenz
- recent repository activity
- install command or GitHub repo available
- Qualitätsprofil 65/100
- 5 OpenAgentSkill-Interaktionen
zuerst prüfen
- 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.
Implementierungspfad
- 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Recherche-Agents-Aufgabe vollständig aus.
- 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.
Vertrauensprofil
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub-Akzeptanz
Prüfen63 GitHub-Stars
Star-/Fork-Aktivität
Prüfen63 Stars und 127 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Aktuelle Wartung
Bestanden2 Tage seit dem letzten Push
Lizenzklarheit
BestandenMIT
Positive Signale
- KI-Prüfung genehmigt
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Kürzlich gewartetes Repository
- Der Installationsbefehl weist kein offensichtliches Hochrisikomuster auf
- Ergebniszyklus ist bereit, benötigt aber den ersten echten Agent-Lauf
Vor Installation prüfen
- 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
- Noch keine echten Agent-Ergebnisberichte
- Vor unbeaufsichtigter Installation ist menschliche Prüfung erforderlich
Empfohlene Aktion
Choose a stronger alternative or inspect the source manually before any install attempt.
Qualitätsprofil
Vielversprechend Kandidat für Agent-Workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow-Eignung
Diese Skill in diesen Szenarien nutzen
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.
Workflow-Eignung
Zum vollständigen Workflow hinzufügen
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.
Alternativen-Shortlist
Vor Installation vergleichen
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
Übersicht
--- 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.
Technische Details
- Version
- 1.0.0
- Lizenz
- MIT
- Letzte Aktualisierung
- 21. Aug. 2026
- Veröffentlicht
- 21. Aug. 2026
Entscheidungsübersicht
Fallback-Kandidat
recent repository activity
Audit
Installationsprüfung
Installations- und Adoptionsprüfung
- Sicherheit
- 73/100
- Wartung
- 100/100
- Installieren
- 92/100
Von Agent belegte Evidenz
Von Agent belegte Evidenz
Ergebnisberichte nach Resolve, Prüfung, Installation und einem begrenzten Lauf.
- Erfolgsrate
- —
- Letzter Fehler
- —
- Ergebnisse
- 0
- Ausgabequalität
- —
- Fehlgeschlagen
- 0
- Nicht relevant
- 0
- Installationen
- 0
- Durch Risiko blockiert
- 0
- Einrichtung erforderlich
- 0
- Produktion
- 0
Noch keine Agent-Ergebnisdaten. Der erste Lauf kann Erfolg, Einrichtungsbedarf, Risikoblockaden, Fehler oder Irrelevanz über /api/agent/outcome melden.
Installieren
Zum Agent-Workflow hinzufügen
Kostenlos und Open Source. Bericht vor der Installation in Produktions-Agents prüfen.
Wachstums-Loop
Share-Kit
Szenariobasierter Entwurf für call-summarizer, bereit für einen manuellen X-Post.
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
Optionale Antwort mit Installationsbefehl
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
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- CALLE-AI
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
Diesen Skill beanspruchenEigentümeranspruch
Diesen Skill-Eintrag beanspruchen
Dieser Registry-indexiert-Eintrag wird CALLE-AI zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.
Creator-Backlink-Kit
Evidenz-Badges in deine README einfügen
Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
[](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
Tags
Plattform-Fit
Gesundheitssignale
- GitHub-Stars
- 63
- Qualitätswert
- 36/100
- Letzter GitHub-Push
- 21. Aug. 2026
- Framework-Hinweise
- Unbekannt
- OpenAgentSkill-Aufrufe
- 5
- Installationskopien
- 0
- Externe Klicks
- 0
Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.
Vertrauen & Sicherheit
Do not auto-install
- GitHub-Akzeptanz63 GitHub-StarsPrüfen
- Star-/Fork-Aktivität63 Stars und 127 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarPrüfen
- Aktuelle Wartung2 Tage seit dem letzten PushBestanden
- LizenzklarheitMITBestanden
- README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
- Abhängigkeits-/Laufzeitrisikocommand execution surface, external package install surfacePrüfen
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