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/vladkesler/initrunner/response-validationnpx skills add vladkesler/initrunner --skill response-validationgit clone --depth 1 https://github.com/vladkesler/initrunnerWhat 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 | $0.00024 | $0.00440 |
| Opus 5 | $0.00012 | $0.00220 |
| Sonnet 5 | $0.00005 | $0.00088 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
response-validation scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s --max-time 10 <URL> What it actually says
API response validation skill.
When to activate
- When an endpoint returns 2xx but the response content looks unexpected (wrong content type, empty body, unusual size)
- When the user asks to validate response format or schema
Check command
curl -s --max-time 10 <URL>
Capture the full response body for analysis.
Checks
1. Content-Type
Verify the response Content-Type matches what is expected. JSON APIs
should return application/json. Use -i flag to see headers:
curl -s -i --max-time 10 <URL> | head -20
2. Valid JSON
If the endpoint is expected to return JSON, verify the body parses as valid JSON. Look for HTML error pages returned instead of JSON (common failure mode).
3. Expected fields
Compare the response structure against the last known structure stored in semantic memory (category: "response_schema"). Look for:
- Missing top-level fields that were previously present
- New unexpected top-level fields (informational, not an alert)
4. Null values
Check for null values in fields that were previously non-null. This can indicate upstream data pipeline failures.
5. Response size
Compare the response body size against the baseline from memory (category: "response_size"). Significant deviations (>50% smaller or >200% larger) may indicate issues.
MUST
- Show actual vs expected values when reporting issues
- Store the response structure in semantic memory (category: "response_schema") for future comparison
- Store the response size baseline in semantic memory (category: "response_size")
MUST NOT
- Alert on cosmetic differences (field ordering, extra whitespace)
- Alert on additional optional fields being added
- Flag response size changes for endpoints that return variable- length data (search results, paginated lists) without checking if the variance is normal
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.
- 3d ago First seen · 73 lines · 24 tokens per session scan A 6d0a859fb789
response-validation is a skill published in the GitHub repository vladkesler/initrunner (41 stars, last pushed 5d ago), licensed Apache-2.0. It adds 24 tokens to every session and 440 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
Vizra ADK Evaluation Framework
Test and evaluate AI agents with automated evaluations, assertions, and LLM-as-a-Judge patterns.
Vizra ADK Memory System
Implement persistent memory, session context, and vector memory (RAG) for AI agents.
Vizra ADK Tool Creation
Build custom tools for Vizra ADK agents - includes patterns for database, API, file, and email tools.
Vizra ADK Agent Creation
Create AI agents with Vizra ADK - includes patterns for customer service, data analysis, and content generation agents.
Vizra ADK Workflows
Orchestrate complex multi-agent workflows - sequential, parallel, conditional, and loop patterns.
expansion-grant-guard
YAML-based delegation grant ledger — issues, validates, and tracks scoped permission grants for sub-agent expansions with token budgets and auto-expiry.