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Post-call persona detection skill. An agent skill from CALLE-AI/awesome-phone-call-agents.
Install Client Persona Profiler
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents client-persona-profiler --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/client-persona-profiler .claude/skills/client-persona-profiler && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "client-persona-profiler" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/client-persona-profiler into .claude/skills/client-persona-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-persona-profiler", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/client-persona-profilerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents client-persona-profiler --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/client-persona-profiler .agents/skills/client-persona-profiler && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "client-persona-profiler" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/client-persona-profiler into .agents/skills/client-persona-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-persona-profiler", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents client-persona-profiler --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/client-persona-profiler .cursor/skills/client-persona-profiler && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "client-persona-profiler" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/client-persona-profiler into .cursor/skills/client-persona-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-persona-profiler", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CALLE-AI/awesome-phone-call-agents.git --path skills/client-persona-profiler--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents client-persona-profiler --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/client-persona-profiler .gemini/skills/client-persona-profiler && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "client-persona-profiler" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/client-persona-profiler into .gemini/skills/client-persona-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-persona-profiler", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CALLE-AI/awesome-phone-call-agents client-persona-profilerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/client-persona-profiler .github/skills/client-persona-profiler && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "client-persona-profiler" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/client-persona-profiler into .github/skills/client-persona-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-persona-profiler", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents client-persona-profiler --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/client-persona-profiler .opencode/skills/client-persona-profiler && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "client-persona-profiler" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/client-persona-profiler into .opencode/skills/client-persona-profiler/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "client-persona-profiler", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
client-persona-profiler- GitHub stars
- 107
- Token cost
- ~2.3k tokens
- SKILL.md length
- 546 words
- Files
- 9 (incl. scripts, references)
- Skills in repo
- 101
- Repo updated
- First seen
- Licence
- MIT
At a glance
Post-call persona detection skill. An agent skill from CALLE-AI/awesome-phone-call-agents.
- Tasks that involve Performance optimization
- SKILL.md covers Why This Skill Exists, Scientific Foundation, Quick Start and Input, plus 9 more sections
- Runs Python scripts from its folder; calls python3
What it does
Client Persona Profiler is an agent skill from CALLE-AI/awesome-phone-call-agents. Post-call persona detection skill. Analyses a CALL-E transcript to classify the caller's behavioural archetype (heuristic DISC keyword scoring), compute an RFMAP-style loyalty score across accumulated call history, persist a privacy-preserving hashed profile, and return a structured persona card with a personalised next-call strategy playbook. Runs in heuristic mode only, with sensitive-topic human-review flags.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `references/disc-playbooks.json`, `references/example-transcript.json` and `references/examples.md`).
It sits in Development, covering Performance optimization. The repository describes itself as: Portable phone-call Agent Skills, apps, examples, adapters, and scheduler recipes for AI agents. The licence is MIT.
When your agent uses it
- Tasks that involve Performance optimization
Example prompts
- “/client-persona-profiler”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 38d4118. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 3 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Client Persona Profiler loads about 2.3k tokens when it runs, and up to ~6k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 546 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from CALLE-AI/awesome-phone-call-agents at commit 38d4118, republished under its MIT licence (© CALLE-AI). 546 words, ~2,262 tokens.
Download SKILL.mdSave it as
.claude/skills/client-persona-profiler/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.- name
- client-persona-profiler
- description
- Post-call persona detection skill. Analyses a CALL-E transcript to classify the caller's behavioural archetype (heuristic DISC keyword scoring), compute an RFMAP-style loyalty score across accumulated call history, persist a privacy-preserving hashed profile, and return a structured persona card with a personalised next-call strategy playbook. Runs in heuristic mode only, with sensitive-topic human-review flags.
- license
- MIT
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.jsonValidate output schema
python3 scripts/validate_profile.py --card /tmp/persona_card.jsonInput
The skill accepts any CALL-E transcript in one of two formats:
Format A — array of turns:
[
{"role": "agent", "text": "Hello, how can I help you today?"},
{"role": "callee", "text": "I need to see all the policy documents first."}
]Format B — wrapper object:
{
"call_id": "calle-20260915-001",
"transcript": [
{"role": "agent", "text": "Hello, how can I help you today?"},
{"role": "callee", "text": "I need to see all the policy documents first."}
]
}Supported turn keys: role / speaker, and text / content / message.
Output — Persona Card
{
"caller_token": "sha256:3f9c8e2a1b7d...",
"analysis_timestamp": "2026-09-15T09:00:00Z",
"interaction_count": 5,
"first_seen_days_ago": 42,
"last_seen_days_ago": 3,
"persona_archetype": "Analytical",
"archetype_confidence":"high",
"disc_scores": {
"D": 0.0, "I": 0.0769, "S": 0.0, "C": 0.9231
},
"sentiment_trajectory": ["neutral", "neutral", "neutral"],
"sentiment_trend": "stable",
"rfmap_loyalty_score": 65,
"loyalty_tier": "high_value",
"churn_risk": "medium",
"call_driver": "unknown",
"sensitive_topics": [],
"recommended_playbook": {
"archetype": "Analytical",
"open_with": "Lead with facts, data, and specifics. Reference documented policies.",
"avoid": "Emotional appeals, vague generalisations, premature commitments.",
"close_with": "Offer written confirmation. Give them time to evaluate.",
"loyalty_lever":"Transparency, consistency between what is said and what is delivered.",
"churn_warning":"Discovered discrepancies between promises and reality."
},
"flags": [],
"profile_version": 5,
"analysis_mode": "heuristic",
"dry_run": false,
"schema_version": "1.0"
}call_driver is always "unknown" in heuristic mode (no intent extraction is
performed); it is kept in the schema for future extensions.
DISC Archetype Reference
| Archetype | Key Trait | Engagement Style |
|---|---|---|
| Dominant (D) | Results-driven, decisive | Direct, brief, outcome-focused |
| Influential (I) | People-oriented, enthusiastic | Story-driven, warm, community-focused |
| Steady (S) | Consistent, supportive | Calm, step-by-step, no surprises |
| Analytical (C) | Detail-oriented, systematic | Data-backed, documented, deliberate |
| Undetermined | Insufficient signal | Balanced, neutral — gather more turns |
Show full SKILL.md (222 more words)Show less
RFMAP Loyalty Tiers
| Score | Tier | Churn Risk |
|---|---|---|
| ≥ 80 | Champion | Low |
| 60–79 | High Value | Low / Medium |
| 40–59 | At Risk | Medium |
| < 40 | Low Value | High |
Flags
| Flag | Meaning |
|---|---|
LOW_TURN_COUNT | Fewer than --min-turns turns; archetype is unreliable |
UNDETERMINED_ARCHETYPE | Top two DISC dimensions are within the margin; archetype is Undetermined |
CHURN_RISK_ELEVATED | RFMAP score is below 55 |
REQUIRES_HUMAN_REVIEW | Sensitive subject matter (medical, legal, financial, or emergency keywords) detected in the transcript; the matched topics are listed in sensitive_topics |
Command-Line Reference
usage: profile_caller.py [-h] --transcript TRANSCRIPT
[--profile-dir PROFILE_DIR]
[--caller-id CALLER_ID]
[--playbook PLAYBOOK]
[--min-turns MIN_TURNS]
[--dry-run]
[--out OUT]
options:
--transcript Path to the transcript JSON file (required)
--profile-dir Directory to read/write persistent caller profiles
(default: ./profiles)
--caller-id Explicit caller identity string (hashed before storage)
(default: auto-derived from transcript metadata)
--playbook Path to the DISC playbooks JSON file
(default: references/disc-playbooks.json)
--min-turns Minimum callee turns before emitting an archetype label
(default: 4)
--dry-run Analyse without writing to the profile store
--out Write persona card JSON to this path (default: stdout)Privacy & Safety
- One-way hashing: The
caller_idis SHA-256 hashed before storage. Raw identity never reaches disk or output. - No PII in output:
validate_profile.pyscans for phone numbers and email addresses and fails if any are found. - Local storage only: Profiles are stored as
.jsonlfiles on the local filesystem. No cloud, no external API. - Protected attributes excluded: Race, ethnicity, religion, political views, and health status are explicitly outside scope.
- Advisory only: The
recommended_playbookis a suggestion, not an automated action. A human decides whether and how to apply it.
Full safety reference: references/safety.md
Files
skills/client-persona-profiler/
├── SKILL.md ← This file
├── scripts/
│ ├── profile_caller.py ← Main analysis runner
│ ├── validate_profile.py ← Output schema validator
│ └── test_persona_profiler.py ← Test suite (84 tests)
└── references/
├── disc-playbooks.json ← Archetype strategy playbooks
├── example-transcript.json ← Sample transcript
├── examples.md ← Usage examples
├── research-papers.md ← Scientific citations
└── safety.md ← Privacy and ethics referenceRunning Tests
# Run via pytest (recommended)
python3 -m pytest skills/client-persona-profiler/scripts/test_persona_profiler.py -v
# Or run directly
python3 skills/client-persona-profiler/scripts/test_persona_profiler.pyExpected: all tests pass, zero network calls, zero file writes (dry-run by default).
Integration with CALL-E
In a CALL-E pipeline, invoke this skill as a post-call step:
[call ends] → [transcribe] → [profile_caller.py] → [persona card] → [agent uses playbook on next call]The persona card can be stored in the agent's context store and injected into the system prompt at the start of the next call:
System: The caller's DISC archetype is Analytical.
Open with data. Avoid emotional appeals. Offer written confirmation.© CALLE-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 8 other files (scripts, references) in skills/client-persona-profiler of CALLE-AI/awesome-phone-call-agents.
- SKILL.md
- references/disc-playbooks.json
- references/example-transcript.json
- references/examples.md
- references/research-papers.md
- references/safety.md
- scripts/profile_caller.py
- scripts/test_persona_profiler.py
- scripts/validate_profile.py
Open the folder on GitHubat commit 38d4118
Compare with similar skills
Client Persona Profiler next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Client Persona Profiler this skillCALLE-AI/awesome-phone-call-agents | 107 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 4 repos | ~1.1k | Automated safety check: Pass | MIT | |
| LLM Torch Profiler Analysissgl-project/sglang | 37k | 2 repos | ~6.4k | Automated safety check: Pass | Apache-2.0 | |
| Pycrazyguitar/pysheeet | 8.2k | — | ~886 | Automated safety check: Pass | MIT | |
| Cmux Debugging Guidemanaflow-ai/cmux | 28k | 1 repos | ~1.1k | Automated safety check: Pass | Custom licence | |
| Analyzing .NET Performancedotnet/skills | 5.6k | 3 repos | ~3.1k | Automated safety check: Pass | MIT |
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Categories
Questions about Client Persona Profiler
What does Client Persona Profiler do?
Post-call persona detection skill. An agent skill from CALLE-AI/awesome-phone-call-agents. Client Persona Profiler is an agent skill from CALLE-AI/awesome-phone-call-agents. Post-call persona detection skill.
When should I use Client Persona Profiler?
Client Persona Profiler fits situations like: tasks that involve Performance optimization.
How do I install Client Persona Profiler in Claude Code?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a claude-code`. Or copy the skill folder (skills/client-persona-profiler in CALLE-AI/awesome-phone-call-agents) into .claude/skills/client-persona-profiler in your project. Claude Code loads it when a task matches its description.
How do I install Client Persona Profiler in Codex?
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a codex`. Or copy the skill folder (skills/client-persona-profiler in CALLE-AI/awesome-phone-call-agents) into .agents/skills/client-persona-profiler in your project. Codex loads it when a task matches its description.
Can I use Client Persona Profiler in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CALLE-AI/awesome-phone-call-agents --skill client-persona-profiler -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/client-persona-profiler, .gemini/skills/client-persona-profiler, .github/skills/client-persona-profiler and .opencode/skills/client-persona-profiler in your project.
What does Client Persona Profiler need to run?
Going by SKILL.md and its folder, Client Persona Profiler needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
Does Client Persona Profiler access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Client Persona Profiler safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Client Persona Profiler use?
Client Persona Profiler is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Client Persona Profiler use?
About 2.3k tokens (SKILL.md is roughly 9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.7k tokens, read only when the agent opens those files.
What are the alternatives to Client Persona Profiler?
Skills that share tags, products or a category with Client Persona Profiler: Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), LLM Torch Profiler Analysis (sgl-project/sglang, 37k stars), Py (crazyguitar/pysheeet, 8.2k stars) and Cmux Debugging Guide (manaflow-ai/cmux, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Client Persona Profiler?
CALLE-AI (a GitHub organization) maintains it in CALLE-AI/awesome-phone-call-agents, which has 107 GitHub stars. The repository holds 101 skills in this directory. The repository was last updated on October 10, 2026.
Source: CALLE-AI/awesome-phone-call-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.