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 agentmods add skills/hoavdc/codexkit/codexkit-csat-sentiment-analyzernpx skills add hoavdc/CodexKit --skill codexkit-csat-sentiment-analyzergit clone --depth 1 https://github.com/hoavdc/CodexKitWhat 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 | $0.00047 | $0.00902 |
| Opus 5 | $0.00023 | $0.00451 |
| Sonnet 5 | $0.00009 | $0.00180 |
| Haiku 4.5 | $0.00005 | $0.00090 |
Grade A, and why
codexkit-csat-sentiment-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 2d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CSAT Sentiment Analyzer
When to Use
- Analyzing CSAT, NPS, app reviews, support comments, or post-interaction feedback.
- Finding recurring customer pain points and service improvement themes.
- Preparing support, CX, product, or leadership feedback summaries.
- Comparing sentiment across segments, channels, agents, products, or time periods.
Procedure
Step 1 - Normalize Feedback
Identify source, date range, channel, score type, segment, and any metadata. Keep raw counts separate from percentages.
Step 2 - Classify Sentiment
Use a simple sentiment label:
- positive
- neutral
- negative
- mixed
- unclear
Include confidence when comments are short or ambiguous.
Step 3 - Code Themes
Group feedback into themes such as speed, quality, pricing, reliability, usability, billing, support tone, missing features, or documentation.
Step 4 - Quantify Patterns
Report counts, percentages, average score, trend direction, and representative examples. Avoid claiming statistical significance without enough data.
Step 5 - Recommend Actions
Link every action to a theme and owner group: support, product, docs, billing, success, operations, or leadership.
Inputs
| Input | Required | Format |
|---|---|---|
| Feedback dataset | Yes | Comments, scores, reviews, tickets |
| Date range | Recommended | Start and end date |
| Segments | Optional | Plan, region, product, channel, agent |
| Scoring system | Optional | CSAT 1-5, NPS, thumbs up/down |
| Business context | Optional | Launch, outage, policy change |
Output
## CSAT Sentiment Analysis - [Period]
### Executive Summary
[Key trend, sentiment, and action]
### Score Snapshot
| Metric | Value | Notes |
|--------|-------|-------|
### Theme Breakdown
| Theme | Sentiment | Count | Percent | Representative Comment | Recommended Action |
|-------|-----------|-------|---------|------------------------|--------------------|
### Segment Differences
| Segment | Pattern | Confidence |
|---------|---------|------------|
### Action Plan
| Owner | Action | Evidence | Priority |
|-------|--------|----------|----------|
What ships with it
1 file 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.
- 2d ago First seen · 121 lines · 47 tokens per session scan A 87d549f9cb57
codexkit-csat-sentiment-analyzer is a skill published in the GitHub repository hoavdc/CodexKit (21 stars, last pushed 3mo ago), licensed MIT. It adds 47 tokens to every session and 902 once invoked, about $0.0002 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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