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-correction-propagation-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-correction-propagation-auditor)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-correction-propagation-auditor"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-correction-propagation-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-correction-propagation-auditor"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-correction-propagation-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 | $0.00079 | $0.02250 |
| Opus 5.5 | $0.00032 | $0.00900 |
| Sonnet 5.5 | $0.00016 | $0.00450 |
| Haiku 4.5 | $0.00008 | $0.00225 |
Grade A, and why
call-correction-propagation-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 9d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
call-correction-propagation-auditor
A mid-call correction that never reaches the record is a silent lie the summary tells.
Every CALL-E application downstream of a call parses the agent-written
post_summary blindly: dates feed writebacks, times feed calendars,
amounts feed charges. Agents correct themselves mid-call all the time -
"Sorry, I said Tuesday; I meant Thursday" - and conversation-analysis
work shows speakers prefer exactly this self-repair form. But intrinsic
self-correction is unreliable: an agent that fixed its own error in
speech may still write the pre-correction value into the record, because
a later correction does not automatically invalidate the earlier stored
value in an LLM's context. The sibling
call-post-summary-faithfulness-auditor anchors each summary claim to
ANY occurrence in the transcript - so a summary carrying the
PRE-correction "Tuesday" still grades SUPPORTED, because "Tuesday" was
genuinely spoken. That is this skill's companion blind spot: faithfulness
to the transcript is not faithfulness to the correction. This skill
follows each agent self-correction chain and verifies that the final
corrected value - not the superseded one - reached the summary, before
automation acts on the stale one.
When To Use
- before any writeback, calendar entry, or outcome parse acts on the
post_summaryof a call where the agent restated or corrected details - after any call whose transcript shows the agent revising a date, time, amount, count, or address mid-conversation
- combined with
call-post-summary-faithfulness-auditor: run both - the faithfulness auditor anchors claims correction-agnostically, this skill grades what happened to each correction
When Not To Use
- when the CALLEE revised their answer ("Actually, make it Friday") - that is answer instability, the object of the provenance-grade skill
- when the CALLEE initiated the repair ("No, Thursday") - callee-side
other-repair signals belong to
call-repair-sequence-auditor - for semantic repair analysis; marker detection is lexical, so a paraphrased correction can go undetected
- on masked spans; phone digits are masked before analysis by design and are unverifiable by the same token
What ships with it
9 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-none.json 508 B
- references/example-call-result-stale.json 701 B
- references/example-call-result-unconfirmed.json 563 B
- references/example-call-result.json 702 B
- references/example-goal.txt 412 B
- references/examples.md 5.7 KB
- references/safety.md 2.8 KB
- scripts/correction_propagation_auditor.py 25 KB runs code
- scripts/test_correction_propagation_auditor.py 23 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.
- 9d ago First seen · 164 lines · 79 tokens per session scan A af9b8344773f
call-correction-propagation-auditor is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 2,250 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 2026-10-11.
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