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Model Profile Resolution

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a5c-ai
model-profile-resolution

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

Overview

Publishera5c-ai
Repositorybabysitter
Skill namemodel-profile-resolution
Stars
1.8K
Forks
112
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by a5c-ai on GitHub. Read the source before you install it.

Installation

Install the Model Profile Resolution 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.
    https://github.com/a5c-ai/babysitter/tree/main/library/methodologies/gsd/skills/model-profile-resolution
  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/a5c-ai/babysitter.git /tmp/babysitter
mkdir -p .claude/skills
cp -r /tmp/babysitter/library/methodologies/gsd/skills/model-profile-resolution .claude/skills/model-profile-resolution
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Model Profile Resolution 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 Model Profile Resolution 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 Model Profile Resolution 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.

  • balanced: Mix of high and mid-tier models. Good quality at moderate cost. Default profile.
  • budget: Cheapest viable models for all agents. Acceptable quality at minimum cost.

The active profile is stored in .planning/config.json under the profile field, or can be overridden with the --profile flag.

Capabilities

1. Read Active Profile

Load the current profile from configuration:

json
// .planning/config.json
{
  "profile": "balanced",
  ...
}

If no profile is set, default to balanced.

2. Profile Definitions

Each profile maps agent roles to specific models:

yaml
quality:
  orchestrator: claude-opus-4-6
  gsd-planner: claude-opus-4-6
  gsd-executor: claude-opus-4-6
  gsd-verifier: claude-opus-4-6
  gsd-plan-checker: claude-opus-4-6
  gsd-phase-researcher: claude-opus-4-6
  gsd-project-researcher: claude-opus-4-6
  gsd-research-synthesizer: claude-opus-4-6
  gsd-roadmapper: claude-opus-4-6
  gsd-codebase-mapper: claude-opus-4-6
  gsd-debugger: claude-opus-4-6
  gsd-integration-checker: claude-opus-4-6

balanced:
  orchestrator: claude-opus-4-6
  gsd-planner: claude-sonnet-4
  gsd-executor: claude-sonnet-4
  gsd-verifier: claude-sonnet-4
  gsd-plan-checker: claude-sonnet-4
  gsd-phase-researcher: claude-sonnet-4
  gsd-project-researcher: claude-sonnet-4
  gsd-research-synthesizer: claude-sonnet-4
  gsd-roadmapper: claude-sonnet-4
  gsd-codebase-mapper: claude-sonnet-4
  gsd-debugger: claude-sonnet-4
  gsd-integration-checker: claude-sonnet-4

budget:
  orchestrator: claude-sonnet-4
  gsd-planner: claude-haiku-4
  gsd-executor: claude-haiku-4
  gsd-verifier: claude-haiku-4
  gsd-plan-checker: claude-haiku-4
  gsd-phase-researcher: claude-sonnet-4
  gsd-project-researcher: claude-haiku-4
  gsd-research-synthesizer: claude-haiku-4
  gsd-roadmapper: claude-haiku-4
  gsd-codebase-mapper: claude-haiku-4
  gsd-debugger: claude-sonnet-4
  gsd-integration-checker: claude-haiku-4

Note: Budget profile uses Sonnet for research and debugging where quality matters most.

3. Per-Agent Model Mapping

Resolve the model for a specific agent:

resolve(agent: "gsd-executor", profile: "balanced")
-> { model: "claude-sonnet-4", profile: "balanced" }

resolve(agent: "gsd-planner", profile: "quality")
-> { model: "claude-opus-4-6", profile: "quality" }

4. Profile Switching

Switch profiles at runtime:

json
// Before
{ "profile": "balanced" }

// After set-profile quality
{ "profile": "quality" }

Profile switches take effect on the next agent spawn. Already-running agents are not affected.

5. Model Capability Validation

Ensure selected model supports required agent features:

Validate gsd-executor needs:
  [PASS] Code generation: all models support
  [PASS] Tool use: all models support
  [PASS] Long context: all models support 200k
  [PASS] Structured output: all models support

Validate gsd-debugger needs:
  [PASS] Code analysis: all models support
  [PASS] Reasoning: opus-4-6 > sonnet-4 > haiku-4
  [WARN] Complex debugging may benefit from quality profile

6. Cost Estimation

Estimate cost per profile for a workflow:

Workflow: execute-phase (Phase 72, 2 plans, 7 tasks)
  Agent spawns: 2 executor + 1 verifier = 3 agents
  Estimated tokens per agent: ~50,000 input + ~20,000 output

  quality (opus-4-6 for all):
    Input:  3 * 50k * $0.015/1k = $2.25
    Output: 3 * 20k * $0.075/1k = $4.50
    Total: ~$6.75

  balanced (sonnet-4 for agents):
    Input:  3 * 50k * $0.003/1k = $0.45
    Output: 3 * 20k * $0.015/1k = $0.90
    Total: ~$1.35

  budget (haiku-4 for agents):
    Input:  3 * 50k * $0.00025/1k = $0.04
    Output: 3 * 20k * $0.00125/1k = $0.08
    Total: ~$0.12

Note: Cost estimates are approximate and based on typical token usage patterns.

Tool Use Instructions

Resolving Profile

  1. Use Read to load .planning/config.json
  2. Extract profile field (default to balanced if not set)
  3. Return profile name

Mapping Agent to Model

  1. Resolve active profile
  2. Look up agent name in profile mapping
  3. Return model identifier
  4. If agent not in mapping, use profile default

Switching Profile

  1. Use Read to load current config
  2. Validate new profile name (quality/balanced/budget)
  3. Update profile field
  4. Write updated config (handled by gsd-tools or direct edit)

Cost Estimation

  1. Accept workflow description (agent types and counts)
  2. Map agents to models via active profile
  3. Estimate token usage per agent
  4. Calculate cost based on model pricing
  5. Return cost breakdown

Process Integration

  • All agent-spawning processes call this skill at initialization:
    • new-project.js - Resolve models for 4+ parallel researchers
    • plan-phase.js - Resolve model for planner, plan-checker
    • execute-phase.js - Resolve model for executor, verifier
    • quick.js - Resolve model for planner, executor
    • debug.js - Resolve model for debugger
    • map-codebase.js - Resolve model for 4 parallel mappers
    • iterative-convergence.js - Resolve model per iteration

Output Format

json
{
  "operation": "resolve|switch|validate|estimate",
  "status": "success|error",
  "profile": "balanced",
  "mapping": {
    "agent": "gsd-executor",
    "model": "claude-sonnet-4"
  },
  "costEstimate": {
    "profile": "balanced",
    "agentCount": 3,
    "estimatedCost": "$1.35",
    "breakdown": {
      "input": "$0.45",
      "output": "$0.90"
    }
  }
}

Configuration

SettingDefaultDescription
profilebalancedActive model profile
customMappings{}Override specific agent-model mappings
costWarningThreshold10.00Warn if estimated workflow cost exceeds this

Error Handling

ErrorCauseResolution
Unknown profileProfile name not quality/balanced/budgetUse one of the three valid profiles
Unknown agentAgent name not in profile mappingUse profile default model
Config not found.planning/config.json missingUse default profile (balanced)
Model deprecatedMapped model no longer availableUpdate profile mapping to current model

Constraints

  • Only three built-in profiles: quality, balanced, budget
  • Profile changes take effect on next agent spawn only
  • Cost estimates are approximate (actual costs depend on token usage)
  • Custom agent-model overrides in config take precedence over profile defaults
  • Model availability depends on the runtime environment (Claude Code, OpenCode, etc.)
  • Budget profile still uses capable models for critical tasks (research, debugging)

Frequently asked questions

What does the Model Profile Resolution AI skill do?

Resolve model profile (quality/balanced/budget) at orchestration start and map agents to specific models. Enables cost/quality tradeoffs by selecting appropriate AI models for each agent role.

Why use Model Profile Resolution on TypingMind?

Because you install it once and use it with any model. Model Profile Resolution 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 Model Profile Resolution in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/a5c-ai/babysitter/tree/main/library/methodologies/gsd/skills/model-profile-resolution. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Model Profile Resolution?

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 Model Profile Resolution?

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

Is the Model Profile Resolution AI skill free?

Yes. It is published on GitHub by a5c-ai 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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