daemon-blockint-tech/agentic-enteprises-skill

prompt-engineer-agent-prompts-evals

Guides prompt engineering for tool-using agents—system and developer prompts, tool schemas, handoffs and subagents, golden datasets, offline eval harnesses, regression CI, LLM-as-judge rubrics, and release gates for prompt changes. Use when authoring agent prompts, building eval suites for agents or copilots, debugging tool-selection failures, designing judge rubrics, or gating prompt deploys—not for general few-shot/CoT patterns only (prompt-engineer), full RAG pipelines (ai-engineer), adversa…

First seen May 20, 2026

Installation

$ npx skills add daemon-blockint-tech/agentic-enteprises-skill --skill prompt-engineer-agent-prompts-evals

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 8
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,710 B
  • docs SUMMARY.md 735 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 33 installs

SKILL.md

Prompt Engineer — Agent Prompts & Evals

When to Use

  • Write or refactor system/developer prompts for agents with tools
  • Design tool descriptions and parameter schemas the model must choose correctly
  • Build golden datasets and scenario suites (multi-turn, failures, edge cases)
  • Implement offline eval harnesses and CI regression for prompts
  • Define LLM-as-judge rubrics and human calibration samples
  • Set release gates when prompts or tool lists change
  • Debug wrong tool, looping, or format break in agent traces

When NOT to Use

  • General prompt patterns without agent/eval focus → prompt-engineer
  • End-to-end RAG indexing, retrieval, serving → ai-engineer
  • Jailbreak and abuse red-team engagements → ai-redteam
  • Org-wide model rollout and incident ops → ai-lead-ops
  • Token cost program across product → ai-token-improvement-plan-engineer
  • Vertical squad management and launch PM → engineering-manager-vertical-ai-products
  • Prompt/eval team management and governance → engineering-manager-agent-prompts-evals

Related skills

Need Skill
Broad prompt design and production guardrails prompt-engineer
RAG, agents in production code ai-engineer
Launch eval gates (manager view) engineering-manager-vertical-ai-products
Risk tier and policy ai-risk-governance
Adversarial testing ai-redteam

Core Workflows

1. Agent prompt structure

System vs developer messages, tools block, constraints, handoffs.

See references/agentsystemprompts.md.

2. Eval datasets

Golden sets, coverage matrix, synthetic and SME-labeled examples.

See references/evaldatasetdesign.md.

3. Harness and metrics

Offline runs, pass/fail, tool accuracy, trajectory checks.

See references/evalharnesspatterns.md.

4. Judges and rubrics

LLM judge design, bias controls, human agreement.

See references/llmjudgerubrics.md.

5. Versioning and regression

Prompt semver, baselines, CI gates, rollback.

See references/promptversioningregression.md.

6. Scenario catalog

Multi-turn, tool error, refusal, escalation cases.

See references/agentevalscenarios.md.

Output standards

  • Every eval case has input, expected behavior (not always exact text), tags
  • Tool schema changes include eval delta in PR description
  • Judge rubrics published with scoring scale and known failure modes
  • No production prompt change without baseline comparison on golden set

When to load references

  • Promptsreferences/agentsystemprompts.md
  • Datareferences/evaldatasetdesign.md
  • Harnessreferences/evalharnesspatterns.md
  • Judgesreferences/llmjudgerubrics.md
  • CIreferences/promptversioningregression.md
  • Scenariosreferences/agentevalscenarios.md