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Repo Bootstrap

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YuxiaoWang-520
repo-bootstrap

Initialize and continuously maintain repository-level agent context under `.harness/` using a state-backed memory system. Keeps `memory.md`, `prompt.md`, `repowiki.md`, `plan.md`, and `checklist.md` synchronized from `.harness/state.json`, and keeps `/.harness/` ignored by git. Use when the user asks for repository bootstrap, durable project memory, rolling context updates, or plan/checklist-driven implementation review.

Overview

PublisherYuxiaoWang-520
Repositoryharness-craft
Skill namerepo-bootstrap
Stars
73
Forks
5
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by YuxiaoWang-520 on GitHub. Read the source before you install it.

Installation

Install the Repo Bootstrap 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/YuxiaoWang-520/harness-craft/tree/main/skills/repo-bootstrap
  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/YuxiaoWang-520/harness-craft.git /tmp/harness-craft
mkdir -p .claude/skills
cp -r /tmp/harness-craft/skills/repo-bootstrap .claude/skills/repo-bootstrap
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Repo Bootstrap 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 Repo Bootstrap 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 Repo Bootstrap 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.

Repo Bootstrap

Create and maintain a .harness/ workspace in the repository root so future turns keep stable project memory, structured plan state, and reviewable execution artifacts.

This skill now uses .harness/state.json as a local machine-readable source of truth. The five markdown files are rendered from that state so the agent can keep updating them without losing prior knowledge.

When To Use It

  • First-time repository bootstrap
  • Ongoing work where the agent should persist memory across turns
  • Non-trivial implementation that needs plan/checklist tracking
  • Periodic repo-wiki refresh after architecture or workflow discovery
  • Long-running sessions where prompt history and decisions should survive context-window loss

Core Model

The durable memory model has two layers:

  1. .harness/state.json
    • structured source of truth for current memory, prompt log, repo facts, plan, and checklist state
    • preserves rolling history and allows deterministic markdown regeneration
  2. Rendered markdown views
    • .harness/memory.md
    • .harness/prompt.md
    • .harness/repowiki.md
    • .harness/plan.md
    • .harness/checklist.md

/.harness/ must remain gitignored.

Quick Start

  1. Resolve repository root.
  2. Run a bootstrap or sync command:

Claude Code:

bash
python3 ~/.claude/skills/repo-bootstrap/scripts/init_docs.py \
  --repo-root <repo-root> \
  --latest-prompt "Summarize and improve this repo bootstrap skill." \
  --intent "Persist durable repo memory and update the harness docs." \
  --objective "Upgrade repo-bootstrap to support rolling updates." \
  --workstream "skill enhancement" \
  --work-summary "Synced harness state for the current task." \
  --next-action "Run verification"

Codex:

bash
python3 "$CODEX_HOME/skills/repo-bootstrap/scripts/init_docs.py" \
  --repo-root <repo-root> \
  --latest-prompt "Summarize and improve this repo bootstrap skill." \
  --intent "Persist durable repo memory and update the harness docs." \
  --objective "Upgrade repo-bootstrap to support rolling updates." \
  --workstream "skill enhancement" \
  --work-summary "Synced harness state for the current task." \
  --next-action "Run verification"
  1. For richer updates, write a JSON payload and pass --context-file:

Claude Code:

bash
python3 ~/.claude/skills/repo-bootstrap/scripts/init_docs.py \
  --repo-root <repo-root> \
  --context-file /tmp/harness-context.json

Codex:

bash
python3 "$CODEX_HOME/skills/repo-bootstrap/scripts/init_docs.py" \
  --repo-root <repo-root> \
  --context-file /tmp/harness-context.json
  1. Confirm these files exist:
    • .harness/state.json
    • .harness/memory.md
    • .harness/prompt.md
    • .harness/repowiki.md
    • .harness/plan.md
    • .harness/checklist.md
  2. Confirm .gitignore contains /.harness/.

Required Turn Workflow

Apply this workflow every time the skill is invoked.

  1. Read .harness/memory.md and .harness/prompt.md before analysis.
  2. For code or architecture tasks, also read .harness/repowiki.md before planning.
  3. Before finishing the turn, sync the latest task context back into .harness/state.json.
  4. Let the script re-render the five markdown files from state.
  5. If facts changed, update repowiki.md through the structured state instead of editing only the markdown view.

The skill is only doing its job if the state is refreshed continuously. Bootstrap alone is not enough.

Non-Negotiable Rules

  1. .harness/state.json is the canonical local memory store.
  2. memory.md and prompt.md must be refreshed on every invocation.
  3. repowiki.md must be refreshed whenever repository facts evolve, or at minimum receive a review entry.
  4. plan.md and checklist.md must stay aligned through shared step IDs or explicit mapping.
  5. /.harness/ must stay ignored in .gitignore.
  6. Do not wipe history just to keep docs tidy; summarize or trim old entries instead.
  7. Do not rely on placeholder-only content after bootstrap.

First-Run Baseline Expectations

The first run should produce useful baseline content automatically:

  • repo name and summary from README when available
  • stack/toolchain guesses from manifests and lockfiles
  • detected run/build/test/lint commands
  • top-level directory map placeholders with concrete paths
  • initial memory objective, risks, next actions, and decisions
  • initial prompt record even when no explicit prompt was supplied

Unknown facts are allowed, but they must be framed as explicit gaps or discovery actions, not empty stubs.

Structured Update Payload

The script accepts structured context through --context-file or --context-json.

Supported top-level fields:

  • latest_prompt
  • prompt_summary
  • intent
  • constraints
  • success_criteria
  • requested_output_format
  • objective
  • definition_of_done
  • workstream
  • why_now
  • work_summary
  • repo_facts
  • assumptions
  • decisions
  • blockers
  • risks
  • open_questions
  • next_actions
  • repo_name
  • repo_summary
  • repo_purpose
  • stakeholders
  • non_goals
  • directory_map
  • architecture
  • data_flow
  • required_env_vars
  • test_strategy
  • deployment_notes
  • observability_notes
  • security_notes
  • known_gaps
  • repowiki_review
  • plan
  • checklist

Recommended shapes:

  • decisions: array of {summary, reason, tradeoff}
  • blockers: array of {item, owner, next_action}
  • risks: array of {item, impact, mitigation}
  • open_questions: array of {question, discovery_action, expected_source}
  • plan.steps: array of {id, step, why, owner, status}
  • plan.files: array of {path, change_type, purpose, linked_step}
  • checklist.plan_mapping: array of {plan_step, item, status, evidence}
  • checklist.files: array of {path, purpose, linked_step, status}
  • checklist.validation_results: array of {check, result, notes}

If plan is present and checklist is omitted, the script auto-derives a checklist skeleton from the plan.

Five-Document Responsibility Contract

FilePrimary responsibilityMinimum useful contentUpdate trigger
memory.mdDurable working memory across turns/sessionscurrent objective, decisions, blockers, risks, open questions, next actions, change logevery interaction
prompt.mdPrompt and intent historylatest prompt, interpretation, constraints, success criteria, prompt timelineevery interaction
repowiki.mdRepository operational wikirepo purpose, stack, commands, directory map, repo facts, known gapswhenever facts are learned or reviewed
plan.mdExecution design for non-trivial workrequest scope, assumptions, steps, file plan, validation, rollbackwhen planning is requested or work is non-trivial
checklist.mdExecution ledger and validation statusplan mapping, file ledger, validation results, post-implementation statuswhen code change is planned or executed

RepoWiki Depth Standard

.harness/repowiki.md must remain a practical wiki, not a summary paragraph. It should stay useful for future sessions by including:

  1. repository purpose and non-goals
  2. architecture notes and control/data-flow hints
  3. module or directory responsibility map
  4. local development commands
  5. runtime/config notes
  6. test strategy and quality gates
  7. operational/security notes when they become known
  8. known gaps and explicit discovery actions

Plan and Checklist Contract

When plan.md is active:

  • record assumptions, dependencies, risks, mitigations, and rollback intent
  • include explicit file-level change plans
  • define validation before implementation

When checklist.md is active:

  • track execution progress honestly
  • include every modified file and one-line purpose
  • record validation outcomes as pass, fail, skip, or pending
  • stay aligned with plan step IDs

Migration Behavior

If legacy markdown files exist (from either .harness/ or the older codex/ directory) but .harness/state.json does not, the script captures those markdown files into state archives before rendering the new state-backed versions. This prevents silent knowledge loss during upgrade.

Anti-Patterns

  • Running bootstrap once and never syncing again
  • Editing markdown views while ignoring state.json
  • Letting memory.md or prompt.md go stale
  • Allowing plan.md and checklist.md to drift apart
  • Replacing explicit unknowns with generic TODO-only stubs
  • Removing historical context without a summary

Resources

scripts/

  • scripts/init_docs.py: bootstraps and continuously syncs .harness/state.json plus the rendered markdown files.

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 Repo Bootstrap AI skill do?

Initialize and continuously maintain repository-level agent context under `.harness/` using a state-backed memory system. Keeps `memory.md`, `prompt.md`, `repowiki.md`, `plan.md`, and `checklist.md` synchronized from `.harness/state.json`, and keeps `/.harness/` ignored by git. Use when the user asks for repository bootstrap, durable project memory, rolling context updates, or plan/checklist-driven implementation review.

Why use Repo Bootstrap on TypingMind?

Because you install it once and use it with any model. Repo Bootstrap 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 Repo Bootstrap in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/YuxiaoWang-520/harness-craft/tree/main/skills/repo-bootstrap. 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 Repo Bootstrap?

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 Repo Bootstrap?

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

Is the Repo Bootstrap AI skill free?

It is published on GitHub by YuxiaoWang-520. Check the repository for licensing terms. 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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