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 client-persona-profilergit 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/client-persona-profiler)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/client-persona-profiler"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/client-persona-profiler/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/client-persona-profiler"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/client-persona-profiler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.02219 |
| Opus 5.5 | $0.00033 | $0.00888 |
| Sonnet 5.5 | $0.00016 | $0.00444 |
| Haiku 4.5 | $0.00008 | $0.00222 |
Grade A, and why
client-persona-profiler 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 1mo 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
client-persona-profiler
Detect who your caller is — and how to keep them.
Unlock lasting customer relationships by understanding the person behind every call, not just the transaction. This skill profiles caller behaviour across interactions (from transcripts obtained with caller consent), builds a long-term loyalty picture, and hands the agent a concrete, archetype-specific playbook for the next call.
Why This Skill Exists
Most call-centre AI focuses on what the caller wants right now. This skill focuses on who they are — their communication style, their loyalty, and their churn risk — so every subsequent interaction is more effective, more personalised, and more likely to convert a one-time caller into a long-term champion.
Scientific Foundation
The classification is a heuristic, not a validated psychometric instrument:
DISC keyword markers are a design choice and the archetype labels are advisory
only (see references/safety.md).
| Research | Relevance |
|---|---|
| Marston, Emotions of Normal People (1928) — DISC | Four-quadrant behavioural model the keyword library is adapted from; DISC's predictive validity is contested in independent academic literature |
| Persona-DB, arXiv:2402.11060 (COLING 2025) | Persona profile storage and retrieval without fine-tuning; conceptual basis for the per-caller profile store |
| Classic RFM (Recency-Frequency-Monetary) model | Basis of the RFMAP-style loyalty score; weights are a skill design choice |
| arXiv:2411.12539 (Nov 2024) — CSAT from transcripts | Transcript sentiment as a satisfaction/loyalty proxy |
Full citations: references/research-papers.md
Quick Start
Heuristic mode (no external dependencies, no model call)
python3 scripts/profile_caller.py \
--transcript path/to/transcript.json \
--profile-dir /var/call-profiles/ \
--caller-id "+14155550100" \
--dry-run \
--out /tmp/persona_card.json
What ships with it
8 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.
- references/disc-playbooks.json 4.1 KB
- references/example-transcript.json 1.2 KB
- references/examples.md 3.7 KB
- references/research-papers.md 2.0 KB
- references/safety.md 3.5 KB
- scripts/profile_caller.py 15 KB runs code
- scripts/test_persona_profiler.py 32 KB runs code
- scripts/validate_profile.py 3.7 KB runs code
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.
- 1mo ago First seen · 261 lines · 82 tokens per session scan A 6c644924499d
client-persona-profiler is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 2,219 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-18.
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