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 structured-outcome-followup-callgit 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/structured-outcome-followup-call)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/structured-outcome-followup-call"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/structured-outcome-followup-call/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/structured-outcome-followup-call"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/structured-outcome-followup-call.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 | $0.00051 | $0.01241 |
| Opus 5 | $0.00026 | $0.00620 |
| Sonnet 5 | $0.00010 | $0.00248 |
| Haiku 4.5 | $0.00005 | $0.00124 |
Grade A, and why
structured-outcome-followup-call 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 8d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
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.
- assets/example_rubric.json 701 B
- references/examples.md 587 B
- references/result_schema_guide.md 2.6 KB
- references/safety_checklist.md 3.7 KB
- references/safety.md 578 B
- scripts/mock_provider.py 5.9 KB runs code
- scripts/orchestrate_example.py 4.2 KB runs code
- scripts/test_orchestrate_example.py 6.0 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.
- 8d ago First seen · 124 lines · 51 tokens per session scan A c6fe2855021e
structured-outcome-followup-call is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (88 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 1,241 once invoked, about $0.0003 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 2026-09-03.
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