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Bounded Code Repair

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Firstp1ck
bounded-code-repair

Repair one focused, inspectable code behavior with a declared file boundary, actual validation, and a bounded retry count. Do not use for broad feature delivery, architecture decisions, security review, or documentation-only work.

Overview

PublisherFirstp1ck
Repositorypi-coding-agent-forge
Skill namebounded-code-repair
Stars
93
Forks
22
Bundled files
3
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.

  • 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 Firstp1ck on GitHub. Read the source before you install it.

Installation

Install the Bounded Code Repair 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/Firstp1ck/pi-coding-agent-forge/tree/main/pi-extension-small-modal-reliability/skills/bounded-code-repair
  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/Firstp1ck/pi-coding-agent-forge.git /tmp/pi-coding-agent-forge
mkdir -p .claude/skills
cp -r /tmp/pi-coding-agent-forge/pi-extension-small-modal-reliability/skills/bounded-code-repair .claude/skills/bounded-code-repair
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bounded Code Repair 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 Bounded Code Repair 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 Bounded Code Repair 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.

Bounded code repair

Make the smallest patch that resolves one defined behavior, then prove the affected behavior with observed checks. A green command, a diff marker, or a model statement alone is not completion evidence.

When to Use

Should trigger

  • “Fix the focused failing test in this named module and add a regression test.”
  • “Make this small behavior-preserving refactor without changing the public API.”
  • “Repair this parser result in the two named files, then run its targeted checks.”

Should not trigger

  • “Design and deliver a new multi-area feature.”
  • “Audit the repository for security issues.”
  • “Update prose only, with no code behavior change.”
  • “Explain an unfamiliar repository without requesting a modification.”

Invocation Design

  • Invocation mode: model-invoked for a focused code patch with an inspectable target.
  • Leading concept: one behavioral objective inside one explicit write boundary.
  • Default scope: at most three files and one behavior; a user or governing workflow must explicitly widen it.
  • Completion criterion: a minimal in-scope diff and fresh, relevant observed validation both support the stated acceptance criteria.

Inputs and Assumptions

Establish the failing or desired behavior, acceptance criteria, allowed write paths, forbidden/shared paths, relevant symbols and callers, existing tests, exact validation commands, and any version-sensitive dependency surface. Repository instructions, user requirements, observed tool results, and model hypotheses remain separate trust sources.

Portable Workflow

  1. State one bug or bounded behavior and its acceptance criteria.
    • Completion criterion: the desired outcome is observable, not merely “improve” or “clean up.”
  2. Inspect the target, callers, tests, and relevant configuration before mutation.
    • Completion criterion: the patch target and its expected effect are evidence-backed.
  3. Declare the write boundary and forbidden/shared paths.
    • Completion criterion: a path outside the boundary has an escalation path rather than an implicit exception.
  4. Check version-sensitive dependencies when the patch relies on a framework, library, runtime, CLI, API, or schema.
    • Completion criterion: advice is tied to the exact installed version and source, or the work stops as unknown.
  5. Implement the smallest patch that satisfies the objective.
    • Completion criterion: the final diff has no unrelated cleanup, weakened tests, or broadened behavior without authorization.
  6. Run targeted checks, then affected broader checks when warranted.
    • Completion criterion: a host-observed result supports each material criterion; unavailable checks remain unknown.
  7. Inspect the final diff and retry at most twice after validation failure.
    • Completion criterion: each retry follows an observed failure; architecture, security, migration, dependency, or multi-subsystem decisions escalate.

Safety and Side Effects

Do not write existing files without a completed fresh inspection of their current content. A partial inspection can authorize only an exact targeted edit entirely inside the inspected span; it never authorizes whole-file overwrite. Do not convert model claims, Markdown checkboxes, test names, or command text into evidence. Do not weaken tests just to obtain green output. Stop for scope drift, an unapproved dependency/version decision, unsafe migration, or exhausted repair attempts.

Scripts, References, and Dependencies

No script or package installation is required. Read code scope and validation before widening a patch or interpreting validation results.

Verification

Confirm the final diff stays in scope, fresh target reads preceded existing-file writes, expected tests/checks were observed and relevant, unrelated tests were not weakened, and failures/unknowns are reported rather than hidden. Record dependency evidence when an external versioned contract materially affects the patch; do not require it for purely local logic.

Pi Adapter

  • Do not repair against unconfirmed ordinary or queued corrections. /reliability input confirm establishes a new native instruction; it does not certify earlier delivery. Revalidate affected old proof after confirmation.

  • Coding completion requires mapped host-observed executable validation; manual-only review is not a replacement. Recursive searches that include forbidden or protected descendants remain blocked: narrow the search root instead of relying on globs.

  • Set an explicit coding scope with reliability_scope; the host enforces allowed paths, tools, budgets, and exact validation commands.

  • Existing-file writes need receipt-bound fresh reads. A bounded partial read may support only a unique exact edit replacement wholly inside that span; write still needs a full current read.

  • All bash and powershell validation use requires a current native-confirmed coding scope plus a single-use native approval for that exact normalized command. /reliability map <criterion_id> bash <exact command> maps evidence coverage only; it grants no shell permission.

  • Use reliability_evidence action record-dependency for an external versioned contract after observing the manifest, optional supported lockfile, installed source, and relevant source passage through host receipts.

  • The coding completion gate independently detects static external imports in changed code. A detected package needs current verified dependency evidence even when no declared criterion names it; ambiguous or non-code changes require an exact-diff local-only review rather than an exemption.

  • Complete a complex canonical plan step only through reliability_record_progress or reliability_submit_worker_result with required host receipts/artifacts. Markdown is audit context, never completion authority.

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 Bounded Code Repair AI skill do?

Repair one focused, inspectable code behavior with a declared file boundary, actual validation, and a bounded retry count. Do not use for broad feature delivery, architecture decisions, security review, or documentation-only work.

Why use Bounded Code Repair on TypingMind?

Because you install it once and use it with any model. Bounded Code Repair 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 Bounded Code Repair in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Firstp1ck/pi-coding-agent-forge/tree/main/pi-extension-small-modal-reliability/skills/bounded-code-repair. 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 Bounded Code Repair?

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 Bounded Code Repair?

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

Is the Bounded Code Repair AI skill free?

Yes. It is published on GitHub by Firstp1ck 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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