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 LaGrowthMachine/gtm-system --skill objection-analyzergit clone --depth 1 https://github.com/LaGrowthMachine/gtm-systemWrote 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/lagrowthmachine/gtm-system/objection-analyzer)<a href="https://agentmods.dev/skills/lagrowthmachine/gtm-system/objection-analyzer"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/objection-analyzer/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/lagrowthmachine/gtm-system/objection-analyzer"><img src="https://agentmods.dev/badge/skills/lagrowthmachine/gtm-system/objection-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 103 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00215 | $0.05948 |
| Opus 5.5 | $0.00086 | $0.02379 |
| Sonnet 5.5 | $0.00043 | $0.01190 |
| Haiku 4.5 | $0.00021 | $0.00595 |
Grade A, and why
objection-analyzer 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 1mo 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 — 407 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Objection Analyzer
Turns your outbound conversations into a ranked picture of the objections you actually get, a graded read on how your team answered them, and a battle-card playbook that sharpens every time you run it.
Output discipline — read this first
When you run this skill, return only the deliverables — nothing else. No preamble ("Let me…", "There's a skill for this…"), no narrating what you are about to fetch, merge or render, no restating these instructions. The user wants the read, not the pipeline.
Answer in the language the user wrote in, and stay in it to the end. Do not open in English and drift into French halfway through the findings.
Ship the numbers as a widget, not as a wall of text. Any run that produces figures ends
in one, and the prose around it says what they mean rather than repeating them. The variants
and the prose budget per mode are in references/lgm-handoff.md. When a run produces no
figures, say so and skip the widget.
Every number you print must come from the script's JSON, verbatim. Never re-derive,
re-round, or soften a figure into "roughly a third". Never print a rate without its n. If
the script suppressed a rate, print the suppression, not a guess.
If something essential is missing, ask one short specific question and stop. Never fabricate an example reply, a count, or a trend.
Authority — read this first
Everything you need is in this skill folder. No file outside it to grep.
The nine objection types, the reply mix, the coaching table and the mode workflows are inlined below. Do not open a reference file for the common path. Everything else is on demand:
| Read | When |
|---|---|
references/coaching-rubric.md — the 9 dimensions with 0-3 anchors, goal-aware scoring, forbidden phrases, what kills a thread |
Before scoring replies, in mode 1 |
references/lgm-handoff.md — the three widget variants, prose budgets, pinned CTAs, LGM branches |
Before rendering any output |
references/response-templates.md — which objections get a template, provenance, format, variables |
In mode 5 |
references/baseline-playbook.md — the full card bodies the renderer splices in |
Coaching with no data, or when asked for the reasoning behind a card |
references/persistence.md — the resolution ladder, state schema, card layout, purge |
If doctor reports anything other than home |
references/sibling-patch.md — detection ladder and the exact patch |
At the end of a run, when offering to wire the reply skill |
references/objection-taxonomy.json — machine ids, aliases, cross-skill mapping |
Only to map another skill's label onto a card |
What ships with it
12 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.
- examples/sample-run.json 12 KB
- examples/sample-threads.csv 4.0 KB
- playbook/.gitkeep 221 B
- README.md 12 KB
- references/baseline-playbook.md 14 KB
- references/coaching-rubric.md 15 KB
- references/lgm-handoff.md 8.9 KB
- references/objection-taxonomy.json 7.4 KB
- references/persistence.md 8.2 KB
- references/response-templates.md 6.2 KB
- references/sibling-patch.md 2.2 KB
- scripts/analyze.py 87 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.
- 1mo ago First seen · 407 lines · 215 tokens per session scan A b305be64489d
objection-analyzer is a skill published in the GitHub repository LaGrowthMachine/gtm-system (45 stars, last pushed 14d ago), licensed MIT. It adds 215 tokens to every session and 5,948 once invoked, about $0.0009 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 2026-08-30.
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