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 nthnclrk/enablement-skills --skill call-insights-to-objectionsgit clone --depth 1 https://github.com/nthnclrk/enablement-skillsWrote 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/nthnclrk/enablement-skills/call-insights-to-objections)<a href="https://agentmods.dev/skills/nthnclrk/enablement-skills/call-insights-to-objections"><img src="https://agentmods.dev/badge/skills/nthnclrk/enablement-skills/call-insights-to-objections/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/nthnclrk/enablement-skills/call-insights-to-objections"><img src="https://agentmods.dev/badge/skills/nthnclrk/enablement-skills/call-insights-to-objections.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 | $0.00077 | $0.00897 |
| Opus 5 | $0.00039 | $0.00449 |
| Sonnet 5 | $0.00015 | $0.00179 |
| Haiku 4.5 | $0.00008 | $0.00090 |
Grade A, and why
call-insights-to-objections 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 12d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Call insights to objections
Cluster what buyers actually said across a defined call set. Stop at the brief. The governed library is a different job.
Fails. "Approved response: 'Our implementation is proven and low-risk across similar rollouts.'"
Passes. "Implementation capacity appeared in 6 of 18 usable discovery calls across 4 accounts. Buyer language: 'we do not have anyone to own the rollout.' Observed consequence: extra validation, not a hard stop. Route the response asset to objection-library-builder."
One recorded call is call-review-coach. Why deals were won or lost is win-loss-synthesis.
Confirm Inputs First
Ask only for the inputs that change the clusters:
- The decision this brief must inform
- Time window, call types, segments, personas, and available call count
- Source format: transcript, notes, or mixed, plus which metadata exists
- Whether the audience needs a diagnostic brief, an action backlog, or inputs for a later library
Do not force shared-context setup for a self-contained source set. Do not invent outcomes, quotes, or usage.
Read The Right Reference
Read references/objection-coding-framework.md before you code, count, or assign confidence. Read references/source-system-guide.md when the evidence comes from a call platform, shared notes, or a message export.
Default Workflow
- Frame the set. Decision question, inclusion rules, window, slices, and unit of analysis. Done when a reviewer can see which conversations are in and which are out.
- Register every eligible conversation. Stable source ID and the metadata needed to trace a finding. Unusable or missing content is missing coverage, not an objection-free call.
- Code without collapsing too early. Explicit objection, implied concern, buyer question, and seller interpretation stay separate. Tag type, buyer language, response, observed consequence, and a root-cause hypothesis.
- Count once per conversation for prevalence. Extra mentions are intensity, not a bigger sample. Show the eligible-call denominator and unique-account breadth on every compared slice.
- Cluster the same concern, not the same wording. Merge only when the underlying buyer issue is the same. Keep distinct concerns separate even when the later response would overlap.
- Score impact from what happened on the call. Blocking, material friction, open concern, or unknown. Deal impact and root cause stay hypotheses unless the evidence establishes them.
- Recommend the smallest next action. Message clarification, proof, product feedback, seller practice, process, or more research. Owner and validation signal. Do not write approved response language.
- Check concentration and privacy. One rep, account, or team must not silently drive the conclusion. Redact what a broad audience should not see.
What ships with it
3 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.
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.
- 12d ago First seen · 71 lines · 77 tokens per session scan A 1648f39d825c
call-insights-to-objections is a skill published in the GitHub repository nthnclrk/enablement-skills (13 stars, last pushed 23d ago), licensed MIT. It adds 77 tokens to every session and 897 once invoked, about $0.0004 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 2026-08-30.
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