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-survey-analyzernpx skills add hoavdc/CodexKit --skill codexkit-survey-analyzergit clone --depth 1 https://github.com/hoavdc/CodexKitWrote 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/hoavdc/codexkit/codexkit-survey-analyzer)<a href="https://agentmods.dev/skills/hoavdc/codexkit/codexkit-survey-analyzer"><img src="https://agentmods.dev/badge/skills/hoavdc/codexkit/codexkit-survey-analyzer.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.01214 |
| Opus 5 | $0.00030 | $0.00607 |
| Sonnet 5 | $0.00012 | $0.00243 |
| Haiku 4.5 | $0.00006 | $0.00121 |
Grade A, and why
codexkit-survey-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 yesterday.
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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Survey Analyzer
When to Use
- After collecting NPS, CSAT, or employee engagement survey responses
- When analyzing market research or customer feedback data
- When leadership needs actionable insights from survey results
- When comparing satisfaction across segments (regions, teams, products)
Procedure
Step 1 — Data Overview
Summarize the survey:
- Total responses vs invitations sent → response rate
- Collection period
- Question types: Likert scale, multiple choice, ranking, open-ended
- Known biases: self-selection, non-response, recency
Step 2 — Quantitative Analysis
For each closed-ended question:
| Question | N | Mean | Median | Std Dev | Distribution Shape |
|---|---|---|---|---|---|
| [Q1 text] | [n] | [mean] | [median] | [sd] | Normal / Skewed L / Skewed R / Bimodal |
Calculate key indices:
- NPS: % Promoters (9–10) − % Detractors (0–6)
- CSAT: % Satisfied (4–5 on 5-point scale)
- Engagement: Overall index from engagement battery
Step 3 — Segment Comparison
Cross-tabulate by key segments:
| Segment | N | Score | vs Overall | Significant? |
|---|---|---|---|---|
| Region A | 120 | 72 | +4 | Yes (p<0.05) |
| Region B | 95 | 65 | −3 | No (p=0.12) |
Test significance:
- Chi-square for categorical × categorical
- t-test or ANOVA for continuous × categorical
- Flag small samples (<30) as unreliable
Step 4 — Open-Ended Theme Extraction
For free-text responses:
- Code responses into themes (max 8–10 themes)
- Count frequency of each theme
- Identify sentiment per theme (positive / neutral / negative)
| Theme | Frequency | % of Responses | Sentiment | Example Quote |
|---|---|---|---|---|
| Onboarding speed | 45 | 18% | Negative | "Took 3 weeks to get access" |
Step 5 — Insight Synthesis
Structure insights as:
- What: the finding (data-driven)
- So What: why it matters (impact)
- Now What: recommended action
What ships with it
4 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.
- yesterday First seen · 149 lines · 60 tokens per session scan A 25b529ecfd8b
codexkit-survey-analyzer is a skill published in the GitHub repository hoavdc/CodexKit (21 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 1,214 once invoked, about $0.0003 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 2026-09-03.
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