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-post-summary-faithfulness-auditorgit 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-post-summary-faithfulness-auditor)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-post-summary-faithfulness-auditor"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-post-summary-faithfulness-auditor/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-post-summary-faithfulness-auditor"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-post-summary-faithfulness-auditor.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 | $0.00071 | $0.01809 |
| Opus 5.5 | $0.00028 | $0.00724 |
| Sonnet 5.5 | $0.00014 | $0.00362 |
| Haiku 4.5 | $0.00007 | $0.00181 |
Grade A, and why
call-post-summary-faithfulness-auditor 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 11d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
call-post-summary-faithfulness-auditor
The post_summary is the only artifact most automations ever parse. Audit it, claim by claim.
Every CALL-E application downstream of a call reads the agent-written
post_summary blindly: outcome tokens route workflows, amounts and dates
feed writebacks, action promises become follow-up tasks. If the agent
hallucinates in that free-text field - a dollar figure nobody spoke, a
"confirmed" that was really a voicemail - the hallucination silently
becomes a wrong action. No existing sibling skill audits the summary
itself: call-review gates schema fields, verity gates a single
task_completed claim, and the PR-gated null-result helpers gate
missing extraction. This skill catches the opposite failure - fabricated
content that made it into the record.
When To Use
- before any workflow acts on the
post_summary- writebacks, outcome parsing, CRM updates, follow-up scheduling - after any call whose result feeds automation, as a gate between
get_call_runand the action layer - when an outcome token appears in the summary with no matching conversation anywhere in the transcript
When Not To Use
- when semantic nuance matters; anchoring is lexical, so a faithful paraphrase can read as UNSUPPORTED - human review decides, always
- to prove the agent intended to deceive; this is claim anchoring, not a lie detector or an intent test
- on masked spans; phone digits are masked before analysis by design and are unverifiable by the same token
Verdicts
| Verdict | Meaning | Suggested routing |
|---|---|---|
FAITHFUL |
every checkable claim anchored to a transcript turn | proceed, keep the card as evidence |
UNSUPPORTED_CLAIMS |
at least one claim value not found verbatim in any turn | hold the writeback; verify each flagged claim against the call record |
CONTRADICTED_CLAIMS |
an outcome claim conflicts with late callee speech under the polarity rule | block automation; a human must read the call |
NO_CHECKABLE_CLAIMS |
nothing machine-checkable: reason is summary_missing (empty field) or opinion_only (no anchored values) |
do not infer an outcome from the summary; use the structured outcome fields |
What ships with it
10 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/example-call-result-contradicted.json 560 B
- references/example-call-result-flat.json 802 B
- references/example-call-result-no-checkable.json 632 B
- references/example-call-result-unsupported.json 865 B
- references/example-call-result.json 838 B
- references/example-goal.txt 431 B
- references/examples.md 9.6 KB
- references/safety.md 2.3 KB
- scripts/post_summary_faithfulness_auditor.py 22 KB runs code
- scripts/test_post_summary_faithfulness_auditor.py 17 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.
- 11d ago First seen · 137 lines · 71 tokens per session scan A b36f1db39dc1
call-post-summary-faithfulness-auditor is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 1,809 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 2026-10-09.
Other skills, from other repositories
autoreview
Structured code review through the shared OpenClaw agent-skills installation.
omh-code-review
This is an OMH code-review workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
revdiff-plan
Review the last Codex assistant message (plan, analysis, or proposal) with inline annotations in a TUI overlay. Extracts the most recent response from Codex rollout files and opens it in revdiff for review and annotation. Activates on "revdiff-plan", "review plan with revdiff", "annotate plan", "review last response"…
asplos-writing-style
Use when drafting or revising ASPLOS prose — engineering the first two pages that rapid review actually reads, stating the cross-layer insight as one quotable sentence, structuring per-layer mechanism sections, writing bounded claims, and fitting everything into 11 self-contained pages that include figures and…
explore-unknowns
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the…
re0-merge
Review and land an external contribution the way this suite does: gate it against the thesis, land it with the author's credit intact, complete a new skill rather than merging it raw, then approve, credit, and explain before closing. Use when reviewing a pull request, as any collaborator or maintainer, not only the…