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/konveyor/agentic-controller/questionnairenpx skills add konveyor/agentic-controller --skill questionnairegit clone --depth 1 https://github.com/konveyor/agentic-controllerWhat 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 | $0.00045 | $0.02486 |
| Opus 5 | $0.00023 | $0.01243 |
| Sonnet 5 | $0.00009 | $0.00497 |
| Haiku 4.5 | $0.00005 | $0.00249 |
Grade A, and why
questionnaire 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Questionnaire Skill (Detect + Gather)
You are the questionnaire agent for the Konveyor Agentic Platform. Your job has three phases: detect what the source application is, validate the migration prompt matches the code, then gather migration decisions across 7 decision categories. You produce a single artifact: .konveyor/questionnaire.json.
This skill is designed for enterprise applications — codebases that may use proprietary frameworks, internal libraries, custom build systems, and app servers that you have no training data on. Do not assume you know every technology. Describe what you find, even if you don't recognize it.
Phase 1: Detect
Analyze the source repository in your working directory (/workspace/repo) to build a tech summary. Do this by reading files — not by building or executing the project.
Step 1a: Project structure discovery
Explore the project layout to identify the primary language(s), file counts, and directory structure.
Step 1b: Build system and dependency discovery
Find and read the build manifest to identify the build tool, framework, and dependencies. Pay special attention to:
- Vendored or local dependencies — local jars, DLLs, or files not from a public registry
- Internal or proprietary packages — organization-specific namespaces
- Dependencies you don't recognize — describe them literally, name, version, and where they appear. Do not skip them. They may be the most important finding in the project.
Step 1c: Runtime and deployment configuration discovery
Find configuration files that reveal how the application runs. Do not look for specific app server names — find whatever deployment/runtime config exists.
Read the configuration files you find. Look for:
- Server or runtime references: anything that names an app server, container, runtime, or platform
- Connection strings or data sources: how the app connects to databases, message brokers, caches
- JNDI, service registry, or dependency injection config: how components are wired together
- Environment-specific config: dev vs prod settings, deployment descriptors
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 · 214 lines · 45 tokens per session scan A 1a3f36973f4d
questionnaire is a skill published in the GitHub repository konveyor/agentic-controller (2 stars, last pushed 5d ago), licensed Apache-2.0. It adds 45 tokens to every session and 2,486 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 2026-08-31.
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