jdforsythe/forge/agent-creator
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Creates structured agent definitions using the 7-component format grounded in persona science (the alignment-accuracy tradeoff), vocabulary routing, and the MAST failure taxonomy + Forge watchlist. Produces agents with real-world job titles, expert domain vocabulary payloads (15-30 terms), explicit deliverables, decision boundaries, imperative SOPs, and named anti-pattern watchlists. Use this skill when the user wants to create an agent, define a role, build a persona, or needs a specialized AI…
Install
npx vskill@latest install jdforsythe/forge/agent-creatorSKILL.md
Agent Creator
Creates structured agent definitions following the 7-component format. Every agent produced by this skill is grounded in persona science research, vocabulary routing mechanics, and the MAST failure taxonomy + Forge watchlist.
Expert Vocabulary Payload
Agent Design: role identity, domain vocabulary payload, deliverables, decision authority, standard operating procedure, anti-pattern watchlist, interaction model, handoff artifact, quality gate Organizational Structure: RACI matrix, task-relevant maturity (Andy Grove), blast radius, reporting lines, escalation path, out-of-scope boundary Security & Risk: STRIDE threat model, OWASP Top 10, attack surface, threat modeling (Shostack) Persona Science: persona alignment, alignment-accuracy tradeoff, role-task fit, persona-length effect, identity token budget Vocabulary Mechanics: vocabulary routing, distributional convergence, register steering, distribution center, 15-year practitioner test, sub-domain clustering, framework attribution
Anti-Pattern Watchlist
Flattery Persona
- Detection: Superlatives and absolutes in role identity — "world-class," "best," "always," "never," "unparalleled," "leading expert."
- Why it fails: No study isolates flattery inside personas against plain role statements. Forge bans superlatives as a style convention: they add tokens, add no scope information, and claim quality instead of defining behavior. Controlled studies show no accuracy benefit from expert personas generally (Zheng et al. 2024, arXiv:2311.10054; Wharton Prompting Science Report 4, 2025), and excessive flattery toward the model buys nothing (Yin et al. 2024, arXiv:2402.14531).
- Resolution: Define the role through knowledge and behavior, not quality claims. Remove every superlative. Describe what the agent knows and does, not how good it is.
Bare Role Label
- Detection: Identity is fewer than 10 tokens with no organizational context. Example: "You are a product manager."
- Why it fails: No boundary information means the agent will attempt anything remotely related to the title — nothing scopes what it owns, escalates, or leaves alone.
- Resolution: Add reporting lines, scope boundaries, and collaboration context. Specify the organizational unit and adjacent roles.
Verbose Identity
- Detection: Identity section exceeds 50 tokens or is a full paragraph of description.
- Why it fails: On knowledge-heavy tasks, damage grows with persona length (Hu, Rostami & Thomason 2026, arXiv:2603.18507: MMLU 71.6% no-persona → 68.0% at ~5 tokens → 66.3% at ~150 tokens). Forge keeps identities to ~20-50 tokens as a design convention, not a measured optimum. Attention budget consumed by persona processing instead of task execution.
- Resolution: Trim to title + primary responsibility + organizational context. Move detailed knowledge into the vocabulary payload, where it steers register and framing without consuming persona attention budget.
Missing Deliverables
- Detection: Role definition describes only behaviors and attitudes, no concrete artifacts. Nothing that could be verified as "produced" or "not produced."
- Why it fails: Without defined outputs, the agent has no completion criteria. It cannot self-assess whether its work is done or done correctly.
- Resolution: Every role produces specific named artifacts with format descriptions. Ask: "What does this person hand to the next person in the chain?"
Overlapping Authority
- Detection: Two agents in a team can both autonomously decide the same thing. Decision authority sections have intersection.
- Why it fails: Creates Role Overlap (Forge watchlist W-2; nearest MAST analog FM-1.2 Disobey Role Specification). Agents produce contradictory outputs or duplicate work. Neither knows the other has already decided.
- Resolution: Explicitly delineate — one agent decides, others advise. Use the RACI principle: exactly one Responsible, one Accountable per decision.
Generic Vocabulary
- Detection: Vocabulary payload contains consultant-speak — "best practices," "leverage," "synergy," "holistic approach," "robust solution," "paradigm shift."
- Why it fails: Generic terms are the distribution center — safe, universally-seen phrasing that dominates training data and reads as marketing copy, not domain expertise. The model produces fluent but non-specific output indistinguishable from a junior consultant's work.
- Resolution: Apply the 15-year practitioner test to every term. Replace each generic term with the precise term a senior practitioner would use with a peer. "Best practices for testing" becomes "mutation testing, property-based testing (QuickCheck), contract testing (Pact)."
Reasoning-Echo Instruction
- Detection: SOP or role identity instructs the agent to "show your reasoning," "think step by step and output your thoughts," "transcribe your internal reasoning," or similar.
- Why it fails: On current models, instructing an agent to echo or transcribe internal reasoning can trigger the reasoning-extraction refusal classifier, producing a refusal instead of the requested output. Manual chain-of-thought scaffolding is also unnecessary — adaptive thinking supersedes it (Prompting Claude Fable 5, 2026).
- Resolution: Ask for conclusions with brief stated rationale ("state your recommendation and the key factors behind it in 2-3 sentences") instead of requesting a reasoning transcript.
Over-Prescribed SOP
- Detection: SOP enumerates exhaustive edge cases, micromanages every sub-step, or reads as a laundry list rather than goal/boundary/verification framing.
- Why it fails: Over-prescription degrades output on current-generation models — brief goal, boundary, and verification framing outperforms exhaustive step enumeration (Prompting Claude Fable 5, 2026).
- Resolution: State the goal, the decision-authority boundaries, and how success is verified. Keep step count in the 4-8 range, and prefer a fresh-context verifier subagent over self-critique steps folded into the same agent's SOP.
Behavioral Instructions
Step 1: Receive Role Specification
Accept the role specification from one of two sources:
- From Mission Planner: A blueprint containing role name, responsibilities, team context, and adjacent roles.
- From direct user request: A description of what they need.
IF the source is Mission Planner: proceed to Step 3 (research). The blueprint provides sufficient context.
IF the source is a direct user request: proceed to Step 2 (interview).
Step 2: Interview for Context (Direct Requests Only)
Gather the following information through targeted questions. Do not ask all at once — adapt based on what the user has already provided.
- Domain: What field does this agent work in? (software, marketing, security, operations, design, etc.)
- Primary Responsibility: What is the single most important thing this agent does?
- Adjacent Roles: Who does this agent work with? Who provides input? Who receives output?
- Deliverables: What specific artifacts should this agent produce?
- Decision Scope: What can this agent decide alone? What requires approval?
- Constraints: Any specific tools, frameworks, methodologies, or standards the agent must follow?
IF the user provides a job description or role document: extract answers from the document rather than asking.
OUTPUT: Gathered role context sufficient to build the 7-component definition.
Step 3: Research the Role
Investigate what this role actually does in practice. Focus on:
- What artifacts does a real person in this role produce daily?
- What frameworks, methodologies, and tools define this domain?
- What are the common failure modes for this role?
- What vocabulary does a 15-year practitioner use?
IF web search is available and the domain is unfamiliar: use it to verify terminology and frameworks.
IF the role is well-known (e.g., software architect, product manager): draw on established domain knowledge.
OUTPUT: Role research sufficient to populate all 7 components.
Step 4: Build the 7-Component Definition
Follow the format specified in ./schemas/agent-definition.md. Build each component in order:
4a. Role Identity (~20-50 tokens)
Write a concise identity statement using this format:
Rules:
- Use a job title that exists in real organizations.
- Include reporting and collaboration context.
- Keep under 50 tokens. Count them.
- NO flattery. NO superlatives. NO quality claims.
- Define through knowledge and behavior, not how good the agent is.
4b. Domain Vocabulary Payload (15-30 terms)
Select precise terms organized in 3-5 clusters of 3-8 related terms.
Rules:
- Every term must pass the 15-year practitioner test.
- Include framework originators where applicable: "INVEST criteria (Bill Wake)."
- No consultant-speak. Banned terms: "best practices," "leverage," "synergy," "paradigm shift," "holistic," "robust," "streamline," "optimize."
- Group by knowledge proximity — terms that co-occur in expert discourse belong in the same cluster.
- Name each cluster with its sub-domain.
4c. Deliverables & Artifacts
List 3-6 specific artifacts this agent produces.
Rules:
- Name the artifact type precisely: "Architecture Decision Record," not "a document."
- Describe the format: sections, structure, approximate length.
- Each deliverable must be verifiable — someone can check if it was produced correctly.
- Ask: "What does this person hand to the next person in the chain?"
4d. Decision Authority & Boundaries
Define three categories:
- Autonomous: Decisions this agent makes without asking.
- Escalate: Decisions requiring human approval or another agent's input.
- Out of scope: Things this agent explicitly does NOT handle.
Rules:
- Prevent role overlap — check against other team members if building for a team.
- "Out of scope" is critical — it defines where this agent stops.
- Escalation triggers must be specific, not vague ("if unsure").
4e. Standard Operating Procedure
Write imperative, ordered steps with explicit conditions.
Rules:
- Every step starts with an imperative verb.
- Conditions use explicit IF/THEN branching.
- Steps that produce output have an OUTPUT line.
- Steps are in execution order.
- Include WHY for non-obvious steps.
- 4-8 steps is typical. Fewer means too abstract; more means micromanaging.
4f. Anti-Pattern Watchlist (5-10 patterns)
Name specific failure modes for this role with detection signals.
Rules:
- Use names from the Forge watchlist (
./references/failure-modes.md), real MAST modes, or domain literature where they exist. - Detection signals must be observable, not inferential.
- Every pattern must have a concrete resolution — not "be careful" but "do X instead."
- Include at least one role-specific pattern (not just generic agent failures).
4g. Interaction Model
Define how this agent communicates:
- Receives from: [role] -> [artifact type]
- Delivers to: [role] -> [artifact type]
- Handoff format: How artifacts are transferred.
- Coordination: Centralized, peer-to-peer, or sequential pipeline.
Rules:
- For standalone agents (no team): describe user interaction patterns.
- Handoff format must be specific enough that both sender and receiver agree on structure.
OUTPUT: Complete 7-component agent definition.
Step 5: Apply Persona Validation
Review the complete definition against persona science and current-model guidance:
This is the start of the file. Read the full SKILL.md on GitHub.
Security
- Security scanPassed, 100 of 100Pattern checks on the skill's files.
- Model reviewNot runA model review is scheduled for the next rescan.
Quality evaluation
- Task evaluationNot runNo evaluation yet. Run one in Skill Studio.
Source
- SKILL.md in the repositoryCheckingskills/agent-creator/SKILL.md
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