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Agent skill

Structured Outcome Followup Call

by CALLE-AI in CALLE-AI/awesome-phone-call-agents

Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable…

MITAuto-check passedEducation

Install Structured Outcome Followup Call

skills CLI
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill structured-outcome-followup-call -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install CALLE-AI/awesome-phone-call-agents structured-outcome-followup-call --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ 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/structured-outcome-followup-call .claude/skills/structured-outcome-followup-call && rm -rf skills-src

Use ~/.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/

Facts

Skill name
structured-outcome-followup-call
GitHub stars
107
Token cost
~1.4k tokens
SKILL.md length
602 words
Files
9 (incl. scripts, references, assets)
Skills in repo
101
Repo updated
First seen
Licence
MIT

At a glance

Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable…

  • Works in 4 steps: Define your questions and result schema → Write your rubric → Run it → …
  • A follow-up action — all runnable in mock mode with zero live calls
  • SKILL.md covers What this skill does, Status, When to use this skill and How it works, plus 3 more sections
  • Runs Python scripts from its folder; calls python

What it does

Structured Outcome Followup Call is an agent skill from CALLE-AI/awesome-phone-call-agents. Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable in mock mode with zero live calls or credentials.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts, reference files and assets (for example `assets/example_rubric.json`, `references/examples.md` and `references/result_schema_guide.md`).

It sits in Education, covering Quizzes and assessments. 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

  • A follow-up action — all runnable in mock mode with zero live calls
  • Tasks that involve Quizzes and assessments

Example prompts

  • “/structured-outcome-followup-call”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Define your questions and result schema
  2. Write your rubric
  3. Run it
  4. Swap in a real provider (not included yet)

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:

    • python

    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

Structured Outcome Followup Call loads about 1.4k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 70 tokens; SKILL.md has 602 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.3k

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). 602 words, ~1,415 tokens.

Download SKILL.mdSave it as .claude/skills/structured-outcome-followup-call/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
structured-outcome-followup-call
description
Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable in mock mode with zero live calls or credentials.
license
MIT

Structured Outcome Follow-up Call

What this skill does

Many phone-call workflows aren't really "have a conversation" — they're "call someone, get a small set of specific answers, decide what happens next based on those answers." This skill packages that pattern for CALL-E:

place call (goal-driven task + resultSchema)
    -> CALL-E adapts the conversation to gather the answers
    -> webhook returns structured answers
    -> your rubric scores them deterministically
    -> a follow-up action fires based on the score

It is not a specific workflow like a reminder call or an appointment booking call — it's the reusable scaffolding underneath any workflow that follows the shape above. Bring your own questions, your own rubric, and your own follow-up action; this skill handles the call lifecycle, the provider abstraction, and the safe-to-develop-without-a-live-call part.

Status

Reference implementation, mock-mode-first. scripts/mock_provider.py simulates CALL-E completely (no network calls, no credentials) so you can read, run, and adapt this skill before ever touching a live CALL-E account. scripts/orchestrate_example.py is a complete, runnable, non-healthcare example (a delivery-exception follow-up call) that exercises the whole pattern end to end using the mock provider.

A real-CALL-E adapter is intentionally not included in this first contribution — see "What's deliberately left out" below.

When to use this skill

Use this when you're building an agent that needs to:

  • Ask a small number of specific questions over the phone (not an open-ended conversation)
  • Turn the answers into a decision using rules you can write down and explain
  • Take an automatic next step for some outcomes, without a human reviewing every call

Don't use this for open-ended conversational calls, calls where the "right" response can't be reduced to a rubric, or anything where the follow-up action needs a human judgment call before firing (see the safety checklist for where that line is).

How it works

1. Define your questions and result schema
python
from structured_call import CallQuestion

questions = [
    CallQuestion(key="package_received", prompt="Did the package arrive at the address?"),
    CallQuestion(key="condition_ok", prompt="Was the package in good condition?"),
    CallQuestion(key="reschedule_needed", prompt="Does delivery need to be rescheduled?"),
]

scripts/mock_provider.py turns this list into both a natural-language task description for CALL-E's goal-driven call model and a JSON resultSchema, the same way described in references/result_schema_guide.md.

2. Write your rubric

A rubric is just a function: structured_answers -> (level, score, reasons). It's intentionally not part of this skill's code — your rubric is domain-specific and you should be able to read it top to bottom without touching the call machinery. See assets/example_rubric.json for the delivery-exception example's rubric, expressed as data so it's easy to adapt without writing a new scoring function from scratch.

Show full SKILL.md (243 more words)Show less
3. Run it
bash
python scripts/orchestrate_example.py

This runs the full pipeline against the mock provider and prints the outcome for each of three canned scenarios (no issue / minor issue / needs reschedule), so you can see the shape of the whole thing before wiring up anything real.

4. Swap in a real provider (not included yet)

Everything in scripts/mock_provider.py implements one small interface (initiate_call, parse_webhook_event) — a real CALL-E adapter is a second implementation of that interface, not a rewrite of anything else. This keeps today's contribution runnable and inspectable without requiring reviewers to have CALL-E credentials to evaluate it.

What's deliberately left out (and why)

  • No real CALL-E network calls. Keeping this contribution mock-only for now means anyone can clone, read, and run it in under a minute with zero setup — which is worth more to the community than a live adapter that only some contributors can verify. A real adapter is a natural, small follow-up contribution once this pattern itself has been reviewed.
  • No specific domain logic (healthcare, delivery, HR, etc.) baked into the skill itself — only in the example. The skill is the scaffolding; the example is one illustration of it.
  • No notification/paging integrations. The example's "follow-up action" is a printed log line, matching this repo's own guidance to keep examples safe-by-default.

Files

structured-outcome-followup-call/
├── SKILL.md
├── scripts/
│   ├── mock_provider.py        # Standalone mock CALL-E client + orchestration loop (stdlib only)
│   └── orchestrate_example.py  # Runnable, non-healthcare worked example
├── references/
│   ├── result_schema_guide.md  # How to write a resultSchema CALL-E can reliably fill
│   └── safety_checklist.md     # Consent, idempotency, phone formatting, credential & action boundaries
└── assets/
    └── example_rubric.json     # The delivery-exception example's scoring rubric, as data

See also

references/safety_checklist.md before adapting this to any real workflow — in particular the note on where automatic follow-up actions should and shouldn't be allowed to fire without a human in the loop.

© 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, assets) in skills/structured-outcome-followup-call of CALLE-AI/awesome-phone-call-agents.

  • SKILL.md
  • assets/example_rubric.json
  • references/examples.md
  • references/result_schema_guide.md
  • references/safety.md
  • references/safety_checklist.md
  • scripts/mock_provider.py
  • scripts/orchestrate_example.py
  • scripts/test_orchestrate_example.py

Open the folder on GitHubat commit 38d4118

Compare with similar skills

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AI Engineering Placement Quizrohitg00/ai-engineering-from-scratch67k—~2kAutomated safety check: PassMIT
Codebase to Coursezarazhangrui/codebase-to-course5.7k—~4.4kAutomated safety check: PassNone
AI Engineering Phase Quizrohitg00/ai-engineering-from-scratch67k—~2.1kAutomated safety check: PassMIT
Scholar EvaluationK-Dense-AI/claude-scientific-writer2.4k2 repos~2.9kAutomated safety check: NotesMIT

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Categories

Questions about Structured Outcome Followup Call

What does Structured Outcome Followup Call do?

Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable…. Structured Outcome Followup Call is an agent skill from CALLE-AI/awesome-phone-call-agents. Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable in mock mode with zero live calls or credentials.

When should I use Structured Outcome Followup Call?

Structured Outcome Followup Call fits situations like: A follow-up action — all runnable in mock mode with zero live calls; tasks that involve Quizzes and assessments.

How do I install Structured Outcome Followup Call in Claude Code?

Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill structured-outcome-followup-call -a claude-code`. Or copy the skill folder (skills/structured-outcome-followup-call in CALLE-AI/awesome-phone-call-agents) into .claude/skills/structured-outcome-followup-call in your project. Claude Code loads it when a task matches its description.

How do I install Structured Outcome Followup Call in Codex?

Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill structured-outcome-followup-call -a codex`. Or copy the skill folder (skills/structured-outcome-followup-call in CALLE-AI/awesome-phone-call-agents) into .agents/skills/structured-outcome-followup-call in your project. Codex loads it when a task matches its description.

Can I use Structured Outcome Followup Call 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 structured-outcome-followup-call -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/structured-outcome-followup-call, .gemini/skills/structured-outcome-followup-call, .github/skills/structured-outcome-followup-call and .opencode/skills/structured-outcome-followup-call in your project.

What does Structured Outcome Followup Call need to run?

Going by SKILL.md and its folder, Structured Outcome Followup Call needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Structured Outcome Followup Call 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 Structured Outcome Followup Call 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 Structured Outcome Followup Call use?

Structured Outcome Followup Call 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 Structured Outcome Followup Call use?

About 1.4k tokens (SKILL.md is roughly 5.7k 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 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Structured Outcome Followup Call?

Skills that share tags, products or a category with Structured Outcome Followup Call: DeepTutor CLI (HKUDS/DeepTutor, 41k stars), AI Engineering Placement Quiz (rohitg00/ai-engineering-from-scratch, 67k stars), Codebase to Course (zarazhangrui/codebase-to-course, 5.7k stars) and AI Engineering Phase Quiz (rohitg00/ai-engineering-from-scratch, 67k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Structured Outcome Followup Call?

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.