Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-summarizergit clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-summarizer)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-summarizer"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-summarizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-summarizer"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-summarizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00082 | $0.01796 |
| Opus 5 | $0.00041 | $0.00898 |
| Sonnet 5 | $0.00016 | $0.00359 |
| Haiku 4.5 | $0.00008 | $0.00180 |
Grade A, and why
call-summarizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 178 lines · 82 tokens per session scan A 5349b1439571
call-summarizer is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (88 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 1,796 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…