Agent skill

Architecture Model Bootstrap

by QoderAI in QoderAI/better-harness

A skill your agent uses to refine a generated Better Harness architecture model into a confirmed one for the Impact pane.

MITAuto-check passed

Install Architecture Model Bootstrap

skills CLI
$ npx skills add QoderAI/better-harness --skill architecture-model-bootstrap -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install QoderAI/better-harness architecture-model-bootstrap --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/QoderAI/better-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/architecture-model-bootstrap .claude/skills/architecture-model-bootstrap && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
architecture-model-bootstrap
GitHub stars
2.4k
Token cost
~1.3k tokens
SKILL.md length
647 words
Files
2 (incl. references)
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses to refine a generated Better Harness architecture model into a confirmed one for the Impact pane.

  • Works in 4 steps: The generated candidate. Open the Impact… → Workspace manifests. package.json (root… → Directory evidence. The tracked path… → …
  • Refine a generated Better Harness architecture model into a confirmed one for the Impact pane
  • SKILL.md covers When to use, Inputs (the evidence pack), Workflow and Grounding rules (hard…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Architecture Model Bootstrap is an agent skill from QoderAI/better-harness. Use to refine a generated Better Harness architecture model into a confirmed one for the Impact pane. Trigger when a project has no .better-harness/architecture/model.json, when the Impact pane shows an "Auto-generated" model, or when a reader asks to name, merge, split, re-kind, or describe the derived Container/Component structure and propose external systems before saving it as declared.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/model-schema.md`).

The repository describes itself as: An open-source Harness Engineering platform for coding agents—define harnesses as code, run controlled experiments, inspect evidence, and compare outcomes. Turn task evidence… The licence is MIT.

When your agent uses it

  • Refine a generated Better Harness architecture model into a confirmed one for the Impact pane
  • A project has no .better-harness/architecture/model.json
  • The Impact pane shows an Auto-generated model
  • A reader asks to name

Example prompts

  • “Auto-generated”
  • “/architecture-model-bootstrap”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. The generated candidate. Open the Impact pane on any commit, or read the
  2. Workspace manifests. package.json (root and nested), plus any
  3. Directory evidence. The tracked path list and the top-level source layout,
  4. Prose evidence. README.md, AGENTS.md, docs/, ADRs — for names,

What it can do on your machine

Read from SKILL.md and the folder at commit 34899f3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Architecture Model Bootstrap loads about 1.3k tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 106 tokens; SKILL.md has 647 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~106
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from QoderAI/better-harness at commit 34899f3, republished under its MIT licence (© QoderAI). 647 words, ~1,273 tokens.

Download SKILL.mdSave it as .claude/skills/architecture-model-bootstrap/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
architecture-model-bootstrap
description
Use to refine a generated Better Harness architecture model into a confirmed one for the Impact pane. Trigger when a project has no .better-harness/architecture/model.json, when the Impact pane shows an "Auto-generated" model, or when a reader asks to name, merge, split, re-kind, or describe the derived Container/Component structure and propose external systems before saving it as declared.

Architecture Model Bootstrap

Turn the deterministic candidate the Impact pane generates into a model a reader can confirm. The server already derives a bounded, grounded skeleton from the worktree (one SoftwareSystem, a Container per workspace, Components for source directories). This skill does the semantic half a static analyzer cannot: naming, grouping, kinding, describing, and proposing the external systems the code only hints at — without inventing structure the evidence does not support.

Default to the repository's reply language. Keep edits grounded and reversible: the output is a proposal a human saves, never a fact you assert.

When to use

  • A project declares no model and the Impact pane falls back to a generated one.
  • A reader wants the generated skeleton refined before saving it as declared.
  • An existing declared model needs elements renamed, merged, split, re-kinded, or described against current code.

Do not use it to invent business capabilities, runtime traffic, or external systems that no evidence supports. L1 system context and external systems stay proposed and human-confirmed, per the parent spec's non-goal.

Inputs (the evidence pack)

Gather these before proposing anything:

  1. The generated candidate. Open the Impact pane on any commit, or read the model the server would generate — one SoftwareSystem, Containers per workspace manifest, Components per source directory, every element tagged generated, plus bindings mapping path globs to element ids.
  2. Workspace manifests. package.json (root and nested), plus any Cargo.toml, go.mod, pyproject.toml, or similar boundary markers.
  3. Directory evidence. The tracked path list and the top-level source layout, so a proposed boundary points at real paths.
  4. Prose evidence. README.md, AGENTS.md, docs/, ADRs — for names, ownership, and declared external dependencies only, never for structure the code contradicts.

Workflow

  1. Read the candidate and the manifests. Establish the real boundaries the generator found before changing any of them.
  2. Name and describe. Replace generic ids/names (packages-api, src) with the project's own vocabulary. Give each kept element a one-line description grounded in what its files do.
  3. Re-kind where evidence warrants. A deployable/independently owned unit is a Container; an internal building block inside one is a Component. Do not promote a directory to a Container without a boundary marker (a manifest, an entrypoint, a deploy target).
  4. Merge and split. Fold sibling directories that are one component; split a directory that clearly hosts two. Every resulting element keeps at least one real path glob.
  5. Propose relationships you can ground in imports, HTTP clients, or declared dependencies. Mark each with its kind (Contains, Imports, ResolvedCall, DeclaredHttp, DeclaredRelationship). Leave an unresolved edge out rather than guessing a target.
  6. Propose external systems and people only as clearly-labelled candidates, with the evidence (a client, an env var, a README claim) named in the description. These are the L1 scale the parent spec keeps human-confirmed.
  7. Validate the shape against Model Schema, then hand the model and bindings back for the reader to save through the Impact pane's Save as declared model action. Never write the file yourself unless the reader asks.
Show full SKILL.md (148 more words)Show less

Grounding rules (hard constraints)

  • Every element you keep must have at least one binding whose path glob matches a real tracked path. No element without evidence.
  • Never invent a binding to a path that does not exist in the worktree.
  • Keep the arch-core shape exactly: elements (snake_case parent_id) + relationships (source_id/target_id). This is what the Impact host consumes and what discovery validates.
  • A directory is not a component and a repository is not a container. Boundaries need a marker, not just a folder.
  • Proposed external systems, people, and L1 context are candidates a human confirms, labelled as such in their descriptions.

Output

Return two artifacts, ready for Save as declared model:

  • model.json — the refined { elements, relationships } in arch-core's shape.
  • bindings.json — [{ "path_glob": "...", "element_id": "..." }], every glob resolvable in the worktree.

State briefly what you renamed, merged, split, re-kinded, and proposed, and which elements remain low-confidence candidates a reader should check first.

© QoderAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in .agents/skills/architecture-model-bootstrap of QoderAI/better-harness.

  • SKILL.md
  • references/model-schema.md

Open the folder on GitHubat commit 34899f3

Compare with similar skills

Architecture Model Bootstrap next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Architecture Model Bootstrap compared with similar skills
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Architecture Model Bootstrap this skillQoderAI/better-harness2.4k—~1.3kAutomated safety check: PassMIT
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Bootstrap Soulbytedance/deer-flow83k2 repos~1.2kAutomated safety check: PassMIT
Orch Refine Codeaffaan-m/ECC274k1 repos~448Automated safety check: PassMIT
Refinewindmill-labs/windmill18k—~420Automated safety check: PassCustom licence
Refiner AutomationComposioHQ/awesome-claude-skills77k3 repos~730Automated safety check: PassNone

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Questions about Architecture Model Bootstrap

What does Architecture Model Bootstrap do?

A skill your agent uses to refine a generated Better Harness architecture model into a confirmed one for the Impact pane. Architecture Model Bootstrap is an agent skill from QoderAI/better-harness. Use to refine a generated Better Harness architecture model into a confirmed one for the Impact pane.

When should I use Architecture Model Bootstrap?

Architecture Model Bootstrap fits situations like: refine a generated Better Harness architecture model into a confirmed one for the Impact pane; A project has no .better-harness/architecture/model.json; the Impact pane shows an Auto-generated model; A reader asks to name.

How do I install Architecture Model Bootstrap in Claude Code?

Run `npx skills add QoderAI/better-harness --skill architecture-model-bootstrap -a claude-code`. Or copy the skill folder (.agents/skills/architecture-model-bootstrap in QoderAI/better-harness) into .claude/skills/architecture-model-bootstrap in your project. Claude Code loads it when a task matches its description.

How do I install Architecture Model Bootstrap in Codex?

Run `npx skills add QoderAI/better-harness --skill architecture-model-bootstrap -a codex`. Or copy the skill folder (.agents/skills/architecture-model-bootstrap in QoderAI/better-harness) into .agents/skills/architecture-model-bootstrap in your project. Codex loads it when a task matches its description.

Can I use Architecture Model Bootstrap in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add QoderAI/better-harness --skill architecture-model-bootstrap -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/architecture-model-bootstrap, .gemini/skills/architecture-model-bootstrap, .github/skills/architecture-model-bootstrap and .opencode/skills/architecture-model-bootstrap in your project.

What does Architecture Model Bootstrap need to run?

SKILL.md names no scripts, command-line tools or credentials: Architecture Model Bootstrap is instructions for the agent only.

Does Architecture Model Bootstrap access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Architecture Model Bootstrap safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Architecture Model Bootstrap use?

Architecture Model Bootstrap is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Architecture Model Bootstrap use?

About 1.3k tokens (SKILL.md is roughly 5.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 830 tokens, read only when the agent opens those files.

What are the alternatives to Architecture Model Bootstrap?

Skills that share tags, products or a category with Architecture Model Bootstrap: Agent Refinement (ruvnet/ruflo, 74k stars), Bootstrap Soul (bytedance/deer-flow, 83k stars), Orch Refine Code (affaan-m/ECC, 274k stars) and Refine (windmill-labs/windmill, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Architecture Model Bootstrap?

QoderAI (a GitHub organization) maintains it in QoderAI/better-harness, which has 2,363 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 28, 2026.

Source: QoderAI/better-harness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.