CALLE-AI
Registry indexed
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
Overview
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.
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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.
python3 scripts/summarize_call.py --transcript path/to/transcript.json --out brief.json
The script performs:
- 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. - 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 amasking_scopefield 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). - 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: unknownrather than guessing. - 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. - Caller fingerprint: a one-way hash of a stable caller identity input
(the masked caller phone number, or an explicit
caller_idfield if provided). Thecall_idis 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:
{
"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
maskedfield is"partial"with amasking_scopedocumenting 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: sensitiveand 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: unknownand an emptyactionslist. 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 installis required for the default (no-call) path. - A CALL-E call result with a transcript. Live calls are out of scope; see the
call-reminderorverify-by-phoneskills for placing calls.
Quick Start
# 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.
File metadata
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
View original text
---
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.
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- MIT
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- 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.
- The script accepts a file path for the transcript; if used in an untrusted environment, an attacker could potentially point it to arbitrary files. However, the skill is designed for local, operator-controlled use and does not exfiltrate data.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 63 GitHub stars
- Stars/forks activity: 63 stars, 127 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
Indexed
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- CALLE-AI/awesome-phone-call-agents
- License
- MIT
- Version
- 1.0.0
- Last GitHub push
- Aug 21, 2026
- Registry updated
- Sep 1, 2026
- Instruction path
- skills/call-summarizer/SKILL.md
Version reported in registry metadata; check source releases before relying on it.
Quality
62/100
Promising
Trust
53/100
Do not auto-install
Audit
70/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- 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.
- The script accepts a file path for the transcript; if used in an untrusted environment, an attacker could potentially point it to arbitrary files. However, the skill is designed for local, operator-controlled use and does not exfiltrate data.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 63 GitHub stars
- Stars/forks activity: 63 stars, 127 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"license": "MIT",
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"Financial research output is not financial advice; require human review before any live investment decision.",
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"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 70,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"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.",
"The script accepts a file path for the transcript; if used in an untrusted environment, an attacker could potentially point it to arbitrary files. However, the skill is designed for local, operator-controlled use and does not exfiltrate data.",
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 62,
"label": "Promising"
},
"supply": {
"track": "Research and knowledge work",
"scenario": "Research agents",
"maintenance": "2mo since push",
"risk": "Needs review"
},
"alternative_skills": [
{
"slug": "yanliudesign-mono-color-skill",
"name": "mono-color",
"url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
"stars": 1919,
"install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
"trust_score": 83,
"audit_score": 90
}
],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"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.",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"The script accepts a file path for the transcript; if used in an untrusted environment, an attacker could potentially point it to arbitrary files. However, the skill is designed for local, operator-controlled use and does not exfiltrate data."
],
"agent_contract": {
"task_input": "Use call-summarizer in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 61/100 Manual review",
"Audit: 70/100 Needs review",
"Safety: 26/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "calle-ai-call-summarizer (call-summarizer)",
"install_command": "npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizer",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "calle-ai-call-summarizer",
"task": "Use call-summarizer in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/calle-ai-call-summarizer",
"api": "https://www.openagentskill.com/api/agent/skills/calle-ai-call-summarizer",
"audit": "https://www.openagentskill.com/skills/calle-ai-call-summarizer/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=calle-ai-call-summarizer&task=Use%20call-summarizer%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20call-summarizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20call-summarizer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/calle-ai-call-summarizer/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/calle-ai-call-summarizer"
}
}For the creator
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- CALLE-AI
- Indexed by
- OpenAgentSkill community index
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