AI Skill Report Card
Generating AI Agents From Text
Quick Start14 / 15
Turn one sentence into a complete agent spec:
Input: "an agent that reviews pull requests and flags security issues"
Output (generated in seconds):
- Name/Persona: "SecGuard" — a meticulous, security-focused code reviewer
- Instructions (master prompt): Role, scope, review checklist, tone, output format
- Pre-wired tools: Code search, GitHub/GitLab integration, static analysis lookup, web search
- Suggested knowledge placeholders: "Upload your secure coding guidelines," "Upload past vulnerability reports"
- Starter slash commands:
/review-pr,/flag-vulnerabilities,/summarize-risk
Then: customize name, instructions, commands, language → launch.
Recommendation▾
Add a bad-outcome example (e.g., generic/vague agent spec) to contrast with good examples per grading emphasis on showing both outcomes
Workflow13 / 15
Progress:
- Step 1: Capture the one-sentence agent description from the user
- Step 2: Derive a name and persona that matches the described role/tone
- Step 3: Write the master prompt (instructions) — purpose, scope, behavior rules, output style
- Step 4: Select tools from the catalog that match the agent's tasks (or let defaults apply)
- Step 5: Propose knowledge placeholders — what docs/data this agent should be trained on
- Step 6: Generate 3-5 starter slash commands representing core actions
- Step 7: Present the full config for review/edit (name, instructions, commands, language)
- Step 8: Finalize — ready to launch and chat immediately
Recommendation▾
Include a reusable template/schema (e.g., JSON/YAML structure for name, persona, instructions, tools, commands) to make output format consistent and machine-parseable
Examples15 / 20
Example 1: Input: "an agent that summarizes meeting transcripts and lists action items" Output:
- Name: "MeetingScribe"
- Persona: Concise, organized, neutral notetaker
- Instructions: "You summarize meeting transcripts into key decisions and action items. Always output: Summary, Decisions, Action Items (owner + due date if mentioned). Keep summaries under 200 words."
- Tools: Document parser, calendar lookup, task creation
- Knowledge placeholders: "Upload past meeting notes for style reference"
- Commands:
/summarize,/extract-actions,/recap-week
Example 2: Input: "an agent that helps brainstorm blog post ideas for a SaaS marketing team" Output:
- Name: "IdeaSpark"
- Persona: Creative, energetic, marketing-savvy brainstorm partner
- Instructions: "Generate blog post ideas tailored to SaaS audiences. For each request, provide 5 titles, a one-line angle, and target keyword. Ask clarifying questions if the niche is unclear."
- Tools: Web search, SEO keyword lookup, content calendar
- Knowledge placeholders: "Upload brand voice guide," "Upload competitor blog examples"
- Commands:
/brainstorm,/keyword-ideas,/outline-post
Recommendation▾
Address edge cases like ambiguous or underspecified one-sentence descriptions, conflicting tool requests, or multi-agent team generation mentioned in the description but not covered in workflow/examples
Best Practices
- Keep the master prompt specific about output format — vague instructions produce inconsistent agent behavior.
- Match persona tone to the task domain (playful for brainstorming, precise for compliance/security).
- Default to the generator's suggested toolkit unless the user names specific integrations; over-equipping with irrelevant tools adds noise.
- Treat knowledge placeholders as prompts for the user to fill, not assumptions — flag them clearly as "upload X here."
- Starter commands should map to the agent's 3-5 most frequent actions, not an exhaustive list.
- Always surface the full config for a quick human review pass before finalizing — generation is a draft, not final.
Common Pitfalls
- Don't generate an agent with a generic, personality-less persona — it reduces usability and trust.
- Don't overload instructions with every possible edge case; keep the master prompt focused on core purpose and clear output rules.
- Don't skip tool selection reasoning — wiring irrelevant tools (e.g., calendar access for a pure writing agent) adds unnecessary complexity.
- Don't leave knowledge placeholders vague ("upload stuff") — specify exactly what kind of document would help.
- Don't treat the generated agent as unchangeable — always present it as a fast first draft that the user can edit (name, instructions, commands, language).