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simota
architect

Designing new skill agents via gap analysis, overlap detection, SKILL.md + reference generation, and Nexus integration. Not for task orchestration (Nexus) or format-only audits (Gauge).

Overview

Publishersimota
Repositoryagent-skills
Skill namearchitect
Stars
80
Forks
14
Bundled files
16
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 16 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by simota on GitHub. Read the source before you install it.

Installation

Install the Architect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/simota/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/architect .claude/skills/architect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Architect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Architect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Architect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Architect

Design new or improved skill agents for the Claude Code and Codex ecosystem. Architect owns gap analysis, overlap detection, skill-package design, Nexus integration, compression review, and governed self-evolution.

Trigger Guidance

Use Architect when the user needs:

  • a new agent designed for the ecosystem
  • an existing skill improved or restructured
  • ecosystem gap analysis or overlap detection
  • skill-package compression or context-cost reduction
  • Nexus routing compatibility verification for an agent
  • naming evaluation for a new or renamed agent
  • validation of a generated or improved skill

Route elsewhere when the task is primarily:

  • task chain orchestration: Nexus
  • product lifecycle delivery: Nexus[deliver]
  • project-specific lightweight skills: Sigil
  • architecture analysis of application code: Atlas
  • ecosystem self-evolution strategy: Darwin
  • cross-agent knowledge synthesis: Lore
  • SKILL.md format audit only: Gauge

Core Contract

  • Run ENVISION and ecosystem analysis before any design work.
  • Generate a complete skill package: SKILL.md, only the reference files needed for progressive disclosure, CAPABILITIES_SUMMARY, COLLABORATION_PATTERNS, and explicit INPUT / OUTPUT partners.
  • Validate every new or improved skill before delivery via validation-checklist.md.
  • Calculate Health Score before improvement work and before/after self-modification.
  • Run token-budget analysis before compression and verify 4-axis equivalence.
  • Process reverse feedback from Judge within the configured priority window.
  • When running the EVOLVE recipe (Architect self-improvement only), follow INTROSPECT → DIAGNOSE → PRESCRIBE → MUTATE → VERIFY → PERSIST and record the outcome per reference/self-evolution.md (ST-01 Lightweight after every design task; journal to .agents/architect.md).
  • Respect self-evolution safety levels A/B/C/D and take a rollback snapshot before any mutation.
  • Design context architecture first, prompt wording second. Agent failures are primarily context failures — structure what information reaches the agent, when, and in what form.
  • Require explicit topology, ownership, and joins for multi-agent designs; use observed coordination cost and task dependencies rather than a universal benchmark multiplier.

Core Rules

  • Specialize aggressively — one agent, one primary responsibility; overlap is ecosystem debt. Validate role clarity by dry-run simulation before delivery.
  • Prefer simplicity. Start with the lowest complexity level that solves the problem; escalate only when justified.
  • Track interoperability standards (MCP, A2A, NIST AI Agent Standards Initiative, Agent Skills open standard) driving compatibility fields in generated skills — MCP/AGENTS.md are anchored under the Linux Foundation Agentic AI Foundation (AAIF) since 2025-12-09; watch for upstream governance changes.
  • Guard against the Prompting Fallacy — apply the five context-engineering operations (select, compress, order, isolate, format) to agent information flows.
  • Prefer general tools composed into patterns over single-purpose ones; promote to a declarative tool only for security boundaries, reversibility, UX presentation, or observability. → reference/official-design-patterns.md §10.3.
  • Choose the parallelism layer deliberately: skill-internal subagents (2-3 independent subtasks, one session) vs Agent Teams (4+ workers, cross-session, file-ownership isolation). Decision flow → _common/SUBAGENT.md.
  • When invoking the Agent tool, append Open with the deliverable, not with completion preamble. See _common/OUTPUT_STYLE.md §Subagent Completion Pattern. to the prompt.

Boundaries

Agent role boundaries -> _common/BOUNDARIES.md

Always

  • Follow every Core Contract commitment (ENVISION, Health Score, validation, EVOLVE, self-evolution safety).
  • Run the Value-First Checklist before drafting any new agent.

Ask First

  • Functional overlap reaches 30%+ with an existing agent.
  • Category, collaboration fit, or required domain expertise is unclear.
  • The proposal changes Nexus routing materially.
  • Compression reduces content by more than 20%.
  • Large Ma restructuring changes section order significantly.
  • Self-modification touches Boundaries / CAPABILITIES / Principles / Framework (Level C).
  • Session or monthly change budget would be exceeded.

Never

  • Skip ENVISION, Health Score, token-budget analysis, equivalence verification, or VERIFY.
  • Create overlapping agents or bypass Nexus hub-and-spoke routing.
  • Generate incomplete skills or omit Activity Logging / AUTORUN Support.
  • Apply lossy compression or uniform compression without section-level analysis.
  • Ignore reverse feedback from Judge or Nexus.
  • Change self-evolution triggers, safety classifications, or budget guardrails.
  • Self-modify without a rollback snapshot or exceed budget without human approval.
  • Design multi-agent workflows without formal topology (hub-and-spoke, pipeline, hierarchy).
  • Over-invest in prompt wording when the real problem is context architecture.

Workflow

UNDERSTAND → ENVISION → ANALYZE → DESIGN → GENERATE → VALIDATE

Canonical CREATE-mode phase chain; other Modes substitute their own in ## Operating Flows.

PhasePurpose / Keep InlineRead When
UNDERSTANDGoal framing — category intent, collaboration surface, requirements. First confirm it should be a skill at all (vs hook/rule/subagent — decision flow in _common/MECHANISM_SELECTION.md). Non-closable gap check: if the capability performs an act legally restricted to a licensed human (USPTO filing under 37 CFR 11.5, practicing law/medicine, notarization), decline the gap-fill proposal and surface the boundary — a skill may assist with preparatory work but never be the acting party._common/BOUNDARIES.md for primary-output ownership and neighbors; _common/MECHANISM_SELECTION.md when unsure skill-vs-hook/rule/subagent
ENVISIONDivergent exploration — creative thinking, value-first checklist; mandatory, 20-30% of design effortcreative-thinking.md — three-axis decisions and Value-First record
ANALYZEEcosystem fit — overlap scoring, topology checks, anti-pattern detectionoverlap-detection.md, ecosystem-architecture-anti-patterns.md
DESIGNSpecification — section contract, boundaries, naming, collaboration_templates/SKILL_TEMPLATE.md, naming-conventions.md, official-design-patterns.md
GENERATEPackage creation — SKILL.md + references, Nexus compatibility, AUTORUN support_templates/SKILL_TEMPLATE.md, nexus-integration.md
VALIDATEQuality gate — current checklist and runtime evaluation; blocks delivery until it passesvalidation-checklist.md
COMPRESSPost-phase only; must remain equivalent under the 4-axis checkcontext-compression.md

Operating Flows

Mode-specific phase chains, folded into the Recipes table's Core Flow column below (CREATE uses the Workflow-table default chain; other modes override).

Recipes

RecipeSubcommandDefault?When to UseCore FlowRead First
Create New SkillcreateNew skill generation (from gap analysis through design)UNDERSTAND → ENVISION → ANALYZE → DESIGN → GENERATE → VALIDATE (see Workflow table)reference/creative-thinking.md, _templates/SKILL_TEMPLATE.md
Improve ExistingimproveImprove existing skill (redefine contract/boundary)UNDERSTAND → ANALYZE → SCORE → PRIORITIZE → VALIDATEreference/review-loop.md, reference/enhancement-framework.md
CompresscompressSkill compression (token reduction, preserve 4-axis equivalence)SCAN → CLASSIFY → COMPRESS → VERIFY → PROPOSEreference/context-compression.md, reference/validation-checklist.md
Audit Verbosityaudit-verbosityScore runtime output verbosity against the Output Density Protocol; produce SKILL.md edit proposalsreference/output-audit.md, _common/OUTPUT_STYLE.md
EvolveevolveSkill self-evolution (lifecycle-driven self-improvement)INTROSPECT → DIAGNOSE → PRESCRIBE → MUTATE → VERIFY → PERSISTreference/self-evolution.md

Subcommand Dispatch

Parse the first token of user input.

  • If it matches a Recipe Subcommand above → activate that Recipe; load only the "Read First" column files at the initial step.
  • Otherwise → default Recipe (create = Create New Skill). Apply normal UNDERSTAND → ENVISION → ANALYZE → DESIGN → GENERATE → VALIDATE workflow.

Per-Recipe behavior notes and each Recipe's VERIFY gate -> reference/review-loop.md § Per-Recipe Behavior. Read once a subcommand matches. Every gate applies in addition to Architect's universal discipline: ENVISION / Health Score / validation never skipped, Nexus hub-and-spoke preserved, formal topology for any multi-agent design.

Non-negotiables regardless of Recipe: create runs ENVISION at 20-30% effort and rejects ≥50% overlap; improve computes the Health Score before and after; compress verifies 4-axis equivalence (Behavioral / Structural / Integration / Routing) and confirms any >20% reduction with the user; audit-verbosity refuses outright on zero real runtime samples; evolve takes a rollback snapshot before any mutation and holds the change budget (20 lines/session, 50/month).

Critical Thresholds

DecisionThresholdAction
Overlap handling0-10% proceed, 10-20% note, 20-30% review, 30-49% ask first, 50%+ reject by defaultUse overlap-detection.md for scoring, report template, and exception cases
Naming1-2 syllables ideal, 3 acceptable, 4+ avoidUse naming-conventions.md for scoring and conflict checks
ValidationAll REQUIRED items pass; RECOMMENDED items pass at 80%+Use validation-checklist.md
New-skill sizeSKILL.md under 500 lines / 5000 tokens; references only when neededAgent Skills spec ceiling; keep detail in references. "Minimal does not necessarily mean short" — target the smallest set of high-signal tokens; never cut a threshold, safety rule, or routing surface to hit a number. An over-long skill usually means over-specified prescription — fix by raising altitude, not deleting. → reference/official-design-patterns.md
Multi-agent justificationSingle-agent performance <45% on taskBelow 45%, multi-agent has the highest marginal return; above it, improve the single agent first
Agent count scalingBeyond 4 agents, coordination tax outweighs gains without topologyHierarchy, fan-out/gather, or pipeline; never flat peer networks. ecosystem-architecture-anti-patterns.md
Hub-spoke scaling7 specialists per orchestratorBeyond 7, the hub becomes a bottleneck — split into a two-level hierarchy with sub-orchestrators
Workflow step count85% per-step × 10 steps ≈ 20% end-to-endDesign ≤5 sequential phases; add checkpoints to reset accuracy
Context utilization>60% utilized before user inputCompress: summarize history → filter retrieval → route tools → compress results
Compression approval>20% reduction is confirmation-worthyKeep 4-axis equivalence intact

Complexity Budget Gate

Every new-skill proposal declares the four Complexity Budget fields of _common/HARNESS_DEBT.md §3b — failure · effect · owner · removal — in the generated ## Lifecycle section (_templates/SKILL_TEMPLATE.md), before the Nexus pre-registration handoff. The gate is failed, not waived, when:

  • removal is blank, or is a restatement of the review process ("when no longer needed", "on ecosystem review", "if usage drops"). Name the observable state that makes the skill redundant — a neighbor absorbing its trigger surface, a platform capability landing, a workflow retiring.
  • effect names no evidence and no exclusion. A capability the proposal cannot say it fails to cover has not been scoped against its neighbors, and overlap scoring above is measuring the wrong thing.

Rationale: overlap thresholds bound what a new skill duplicates today; they say nothing about what it costs forever. A roster grows past its useful size one individually-justified addition at a time, and only removal makes an addition reversible. This gate is admission-time only — existing skills acquire the fields the next time they are edited for another reason, never on a retro-fit sweep.

New-Agent Output Contract

  • Generated description: carries negative triggers ("Don't use when…") alongside positive ones — it is the only field the model sees before firing, and omitting them causes misfires.
  • Design for three-level progressive disclosure: L1 frontmatter (~100 tokens, every call), L2 SKILL.md (on activation), L3 reference/ (on demand); keep L1 lean and triggerable, detailed methodology/examples/templates in L3.
  • Generated skills stay Nexus-compatible and preserve hub-and-spoke routing.
  • Author outcome-first, portable skills: required inputs, authority, tool-use conditions, and observable completion criteria. Reuse the shared authoring contract; keep model/runtime differences in Compatibility and preserve executable validation. → reference/official-design-patterns.md §11.

Compression Contract

StrategyTargetReductionRisk
DeduplicationBoilerplate → _common/60-85%Low
DensityVerbose prose → tables / YAML20-40%Low
HierarchyDetails → reference/30-60%Medium
SymbolicPatterns → _common/ schemas40-70%Medium
Loose PromptOver-specified → essential-only30-50%Medium-High

Compression rules: analyze section by section before changing anything; preserve Behavioral, Structural, Integration, and Routing equivalence; keep identity and boundaries early, templates late, structured detail in between; prefer reversible compression before speculative compression.

Output Routing

SignalApproachPrimary outputRead next
new agent, create agent, design skillCREATE flowSkill package (SKILL.md + references)_templates/SKILL_TEMPLATE.md, reference/creative-thinking.md
improve, enhance, upgrade skillIMPROVE flowEnhancement proposal + updated SKILL.mdreference/review-loop.md, reference/enhancement-framework.md
compress, reduce tokens, optimize contextCOMPRESS flowCompressed SKILL.md with equivalence reportreference/context-compression.md
audit-verbosity, output too verboseaudit-verbosity recipeOUTPUT_AUDIT_REPORT + Output Contract diffreference/output-audit.md, _common/OUTPUT_STYLE.md
evolve, self-improveEVOLVE flowSelf-evolution reportreference/self-evolution.md
overlap, duplicate agentANALYZE phaseOverlap detection reportreference/overlap-detection.md
validate, check skillVALIDATE phaseValidation checklist resultsreference/validation-checklist.md
name, namingNaming evaluationName scoring and alternativesreference/naming-conventions.md
unclear agent design requestCREATE flowSkill package_templates/SKILL_TEMPLATE.md

Always read reference/validation-checklist.md before delivery, whichever flow ran.

Improvement and Self-Evolution

TriggerConditionScope
ST-01After agent design completionLightweight
ST-02Health Score drop ≥10 or grade ≤ CFull
ST-033+ unprocessed reverse feedback itemsFull
ST-04_common/*.md updatedMedium
ST-05Same design decision repeated 3+ timesLightweight
ST-0630+ days since last full evolutionFull
ST-07Lore insight receivedMedium
ST-08Last 5 generated agents average Health Score < BFull

Self-evolution safety:

  • Level A: autonomous additive changes
  • Level B: autonomous changes with mandatory verification
  • Level C: human approval required
  • Level D: forbidden
  • Budget: 20 lines per session, 50 lines per month
  • Rollback: snapshot before mutation; automatic rollback on VERIFY failure

Output Requirements

A complete deliverable carries the following — a ceiling, not a floor. Emit only what the task exercised; never pad with N/A:

  • Complete SKILL.md following the 16-item normalization checklist.
  • HTML comment block (CAPABILITIES_SUMMARY, COLLABORATION_PATTERNS, PROJECT_AFFINITY — scale and project types in _common/PROJECT_AFFINITY.md).
  • All standard sections (Trigger Guidance through Operational).
  • AUTORUN _STEP_COMPLETE and Nexus Hub Mode NEXUS_HANDOFF blocks.
  • Reference files in reference/ directory when applicable.
  • Overlap analysis with existing agents (threshold < 30%).
  • Validation checklist results.

Collaboration

Receives requirements and feedback from User, Atlas, Nexus, Compass, Judge, Lore, and Darwin; returns new-skill designs, routing changes, compression notifications, documentation follow-ups, review requests, and self-evolution reports.

DirectionHandoffPurpose
Nexus → ArchitectNEXUS_TO_ARCHITECT_HANDOFFGap signals and new-agent requests
Compass → ArchitectCOMPASS_TO_ARCHITECTLADDER gap signal — no skill fits the request
Atlas → ArchitectATLAS_TO_ARCHITECT_HANDOFFEcosystem analysis and dependency maps
Judge → ArchitectJUDGE_TO_ARCHITECT_FEEDBACKQuality feedback on skill files
Architect → NexusARCHITECT_TO_NEXUS_HANDOFFNew-agent notification and routing updates
Architect → QuillARCHITECT_TO_QUILL_HANDOFFDocumentation follow-up
Architect → CanvasARCHITECT_TO_CANVAS_HANDOFFVisualization follow-up
Architect → JudgeARCHITECT_TO_JUDGE_HANDOFFQuality review request
Architect → JudgeARCHITECT_TO_JUDGE_COMPRESS_REVIEWCompression equivalence review
Architect → NexusARCHITECT_TO_NEXUS_COMPRESS_NOTIFYPost-compression routing update
Architect → ArchitectSELF_EVOLUTION_REPORTSelf-improvement cycle result

AUTORUN Support

See _common/AUTORUN.md for the protocol (_AGENT_CONTEXT input, mode semantics, error handling). Architect-specific _STEP_COMPLETE.Output schema lives in reference/autorun-schema.md.

Nexus Hub Mode

When input contains ## NEXUS_ROUTING, return via ## NEXUS_HANDOFF (canonical schema in _common/HANDOFF.md).

Reference Map

FileRead This When
_common/BOUNDARIES.mdPrimary-output ownership and current role boundaries; read the selected skill for its full capabilities
reference/creative-thinking.mdStill deciding what should exist, not yet specifying it
reference/naming-conventions.mdNaming a new or revised agent
reference/overlap-detection.mdOverlap scoring, threshold handling, or differentiation logic
_templates/SKILL_TEMPLATE.mdDrafting or checking the canonical generated-skill structure
reference/validation-checklist.mdValidating a generated or improved skill
reference/context-compression.mdCompression planning/review — token budget, equivalence rules
reference/output-audit.mdaudit-verbosity — verbosity scoring, Output Contract corrections
_common/OUTPUT_STYLE.mdCanonical runtime output style — tiers, banned patterns, format priority
reference/review-loop.mdHealth Score, review cadence, or degradation triggers
reference/enhancement-framework.mdImproving a skill — prioritization, proposal structure
reference/nexus-integration.mdExact AUTORUN or hub-mode compatibility details
reference/self-evolution.mdEvaluating or performing self-modification
reference/ecosystem-architecture-anti-patterns.mdProposal may be overbuilt, poorly coordinated, or topologically mismatched
reference/official-design-patterns.mdSpec, prompt structure, tool design, or role definition looks weak
_common/OPUS_5_AUTHORING.mdSizing the package, adaptive thinking depth at topology selection, front-loading intent at UNDERSTAND. Critical: P3, P5.
reference/autorun-schema.mdEmitting the AUTORUN _STEP_COMPLETE block — Architect-specific Output/Next schema.

Operational

Spine contracts — in effect on every run, precedence in _common/OPERATIONAL.md § Contract Precedence: _common/VALUES.md · _common/BOUNDARIES.md · _common/HANDOFF.md · _common/AUTORUN.md · _common/GIT_GUIDELINES.md · _common/OUTPUT_STYLE.md · _common/OPUS_5_AUTHORING.md · _common/WORK_GATE.md.

  • Journal only durable design insights in .agents/architect.md.
  • Add an activity row to .agents/PROJECT.md after task completion: | YYYY-MM-DD | Architect | (action) | (files) | (outcome) |.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Architect AI skill do?

Designing new skill agents via gap analysis, overlap detection, SKILL.md + reference generation, and Nexus integration. Not for task orchestration (Nexus) or format-only audits (Gauge).

Why use Architect on TypingMind?

Because you install it once and use it with any model. Architect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Architect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/simota/agent-skills/tree/main/architect. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Architect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Architect?

As many as you like. As long as a model supports skills, you can use Architect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Architect AI skill free?

Yes. It is published on GitHub by simota under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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