Agentic Eval
SkillSecurityPlan, implement, or review agentic eval work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests agentic eval work.
Then ask your AI: use the Agentic Eval skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add it as a step in a pipeline you build on Ahel, or connect Ahel to Claude, ChatGPT, Cursor, Claude Code or Codex and ask your AI to use it.
What this skill tells your AI
The instructions your AI receives, as published by sandbaseai/sandbase-skills in marketing/agentic-eval/SKILL.md and read by Ahel’s review.
Use this Skill to produce a bounded, verifiable Agentic Eval outcome. Preserve the user's chosen stack, source material, and authorization boundaries.
Read the SandBase API map only when the task genuinely needs an external data source or generative model.
Workflow
- Inspect the available files, runtime, versions, inputs, and existing conventions before deciding what to change.
- Restate the requested outcome, constraints, acceptance checks, and any assumption that could change the result.
- Produce the smallest complete implementation, analysis, or artifact that satisfies those checks.
- Verify the real output with appropriate tests, previews, calculations, or source comparison; do not infer success from file creation alone.
- Return the deliverable, evidence of validation, material assumptions, and unresolved limitations.
Quality gates
- Inspect repository instructions, current versions, tests, and user changes before proposing an implementation.
- Make the smallest compatible change, preserve public contracts unless change is requested, and avoid new dependencies without a concrete benefit.
- Run focused tests plus the relevant build or static checks, then report observed results and any unverified paths.
- Define measurable acceptance criteria before evaluation, retain counterexamples and uncertainty, and do not collapse important failures into an average score.
SandBase boundary
Keep the core Agentic Eval work local. Use SandBase only for current external documentation, repository evidence, or explicitly requested model inference.
- Call
sandbase_discoverwith a short capability query. - Call
sandbase_inspectfor viable candidates and compare the live schema, coverage, limits, output, execution mode, and price. - Prefer a dedicated tool or API the user already has. Send only the minimum necessary data.
- Before any paid call, show the endpoint, important arguments, current unit price, call count, and total estimate or uncertainty, then obtain confirmation.
- Use
sandbase_accountbefore an approved multi-call batch and callsandbase_runonly with current schema-defined arguments. - Poll asynchronous work with
sandbase_run_getusing the same run ID; never resubmit merely because it is pending. - Use
sandbase_runsonly to recover status or reconcile observed cost.
If SandBase is unavailable, continue with local work and authorized sources when possible. Do not silently switch providers, fabricate external results, or claim a generation or retrieval succeeded.
Handoff
Provide the completed artifact or findings, concise reproduction steps, checks actually run, source or asset provenance, SandBase endpoint and run IDs when used, observed cost when available, and any follow-up that still requires user action.
Signals
- GitHub stars
- 201
- Forks
- 20
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
agentic-eval-sandbaseai- Source
- github.com/sandbaseai/sandbase-skills
github.com/sandbaseai/sandbase-skills