Bring Down logo

Bring Down

Organization
l-gevity
bring-down

Moves bespoke, duplicated, or over-local code down into reusable capabilities outside the immediate implementation: framework-native features, stack-aware approved libraries, internal platform products, managed services, or external standards. Use when assessing whether custom code should be replaced by a lower-level capability, especially an external library or service; when reducing bespoke wrappers around commodity behavior; or when designing an improvement roadmap for reuse and platform leverage. Skip purely in-codebase subsystem extraction or patternization unless the target is an approved external library, framework capability, platform product, or managed service.

Overview

Publisherl-gevity
Repositoryl-gevity-skills
Skill namebring-down
Stars
72
Forks
13
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 l-gevity on GitHub. Read the source before you install it.

Installation

Install the Bring Down 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/l-gevity/l-gevity-skills/tree/main/.agents/skills/bring-down
  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/l-gevity/l-gevity-skills.git /tmp/l-gevity-skills
mkdir -p .claude/skills
cp -r /tmp/l-gevity-skills/.agents/skills/bring-down .claude/skills/bring-down
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bring Down 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 Bring Down 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 Bring Down 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.

Bring-Down

Purpose: Lower maintenance ownership. Move custom-maintained code toward the lowest responsible reusable capability outside the immediate implementation.

Improvement Trio

  • defect-shift-left: move defect detection earlier.
  • push-out: move recurring operational work outward.
  • bring-down: move bespoke code down into reusable capability.

Core Directives

  1. Prove maintenance burden before replacement. Repetition raises priority, but one costly custom implementation can still be worth outsourcing.
  2. Bring down to the lowest responsible level. Stop when the target level absorbs real duplication without hiding necessary variation.
  3. Do not platformize uncertainty. A premature platform multiplies cost.
  4. Preserve escape hatches. Lower-level capability must not block valid local needs.
  5. Measure adoption and deletion. Bring-down succeeds when custom code retires, not when another abstraction exists.
  6. Search the current stack first. Prefer framework-native features, approved libraries, platform products, and already-approved services before surveying the external market. When a new L3 LIB landing is plausible, search stack-native package indexes and current primary sources.
  7. Name the landing capability. A recommendation is incomplete unless it says where the functionality lands and who maintains that destination.

Scope Boundary

Purely internal moves from local code to shared subsystems, templates, or architecture patterns belong to the architecture skills. Use bring-down for those levels only when the move consumes an external or platform capability, such as a framework feature, approved third-party library, internal self-service platform product, or managed service.

A move that keeps the same maintenance owner is not bring-down. Treat it as architecture, refactoring, CI/CD reliability, push-out, or documentation work.


1. Bring-Down Scale

The scale measures maintenance ownership: high means custom-maintained in this codebase; low means responsibility has moved to an approved library, standard, internal platform product, or external service.

LevelAcronymNameCode lives asEvidence
L4CODECustom codeLocal implementation maintained by this codebase/teamBespoke logic, wrappers, scripts, or repeated copies
L3LIBLibrary/frameworkApproved package, framework/library API, or shared capabilityConsumers import/call one maintained implementation
L2STDStandard pathExternal standard, framework convention, generator, policy, or reference architectureNew instances start from the standard path
L1PLPPlatform productInternal self-service capability maintained outside this codebaseTeams consume it with validation, observability, and support
L0SRVCExternal serviceExternal SaaS, PaaS, managed cloud service, or commodity provider with minimal custom codeLocal implementation is gone or only integration remains
bring-down distance = current level - target level

In reports, name levels as <level> <acronym> (for example, L3 LIB) and reserve the full prose name for narrative explanation.

L1 PLP means an internal platform product: a supported self-service capability owned outside this codebase, such as a reusable deployment workflow, auth gateway, logging pipeline, hosted data module, or policy-enforced cloud resource. It is not just a shared subsystem in the repo.

L0 SRVC means an external service or managed commodity: SaaS, PaaS, managed cloud service, or provider-owned capability where this codebase keeps only the integration.

L2 STD counts only when the standard path is maintained outside the consuming code owner or is imposed by a framework, platform, or external specification. A repo-local template, helper, or manifest maintained by the same team is still L4 CODE for this skill.

Lower is not automatically better. The target is the lowest level that removes real duplication while preserving legitimate variation.


2. Outsourcing Triggers

SignalDefault action
Commodity custom codeSearch for framework/library/platform/service replacement
High maintenance burdenConsider outsourcing even with one instance
2+ repeated copiesRaise priority; document common behavior and variation
3+ repeated copiesStrong signal to replace with library/platform/service, or hand off to architecture skills for internal extraction
High-risk single instanceConsider early standard/platform/service if failure cost is high
Many divergent copiesOutsource the stable commodity core; keep local variation explicit

Repetition is evidence, not the point of the skill. If repeated copies solve materially different domain problems, do not force a shared abstraction; look for commodity sub-capabilities that can be outsourced safely.


3. Target-Level Heuristics

ConditionTarget
Differentiating or feature-specific behaviorL4 CODE
Repeated shape but variation still unclearL4 CODE, or hand off for internal architecture work
Stable commodity logic covered by an approved library/frameworkL3 LIB
Stable creation workflow covered by an externally owned standard/generator/policyL2 STD
Cross-team operational capability with policy needsL1 PLP
Non-differentiating commodity functionL0 SRVC

Use functionality-complexity-tradeoff before replacing code: if the duplicated functionality is unnecessary, delete it instead of bringing it down.

If the target is "shared helper", "subsystem", "template", "manifest", "script", or "pattern" in the same repo/team, exclude it from bring-down recommendations and list it under gaps or handoffs.


4. Current-Stack Survey

The first bring-down move is discovery inside the current technology stack. Look for a lower placement or alternative that already exists before creating or buying anything. If the best move is only a repo-local subsystem or template, stop and use the architecture skills instead.

Search for:

TargetSignals
Approved library/framework capabilityExact package, import path, framework API, SDK feature, plugin, or built-in behavior
Existing standard/pathExact external standard, framework convention, platform-owned generator, reference architecture, or policy
Existing platform productExact internal service, reusable workflow, paved-road module, self-service control, and owning team
Already-approved serviceExact provider/service already used, security-approved, contracted, or supported
Framework-native capabilityExact built-in feature that replaces local wrapper or custom code

Prefer stack-native and locally adopted options over new abstractions. A new external library, template, platform product, or service is justified only when the current stack has no suitable lower placement.

Do not recommend a lower level with a vague landing like "patternize", "extract a subsystem", "extract", "shared subsystem", or "manifest". Name the actual package, API, standard, platform product, or service. If none is known, report the candidate as "no bring-down landing found".


5. Library Survey

Run a library survey when recommending a new or not-yet-approved L3 LIB landing. Do not browse for every assessment; browse when library status, compatibility, maintenance, or security affects the recommendation.

Detect the stack before searching. Use manifests, lockfiles, imports, framework files, and build/deploy config such as package.json, pyproject.toml, requirements.txt, go.mod, Cargo.toml, .csproj, pom.xml, build.gradle, composer.json, Gemfile, Dockerfiles, IaC files, and framework conventions.

Search in this order:

  1. Current repo/org stack. Existing dependencies, imports, framework-native APIs, SDK features, internal approved libraries, and platform catalogs.
  2. Stack-native indexes. npm, PyPI, Go Packages, crates.io, Maven Central, NuGet, Packagist, RubyGems, Docker Hub, Terraform Registry, cloud/provider catalogs, framework plugin registries, or equivalent ecosystem index.
  3. Primary docs. Official documentation, changelog, compatibility matrix, migration guide, security policy, and license.
  4. Source repository. Use GitHub or another source host for release history, maintenance activity, issue health, examples, ownership, and project governance. Do not treat stars as proof of fit.
  5. Broad web search. Use only when stack-native indexes, docs, and source repositories do not identify credible candidates.

Evaluate candidates by exact commodity fit, current repo/org adoption, maintenance activity, release cadence, security advisories, license compatibility, framework/runtime compatibility, API surface size, lock-in, migration cost, rollback path, and custom code retired.

Recommend L3 LIB only when the landing is a named package, framework API, SDK feature, or plugin with current evidence. If no credible library is found, report "no L3 LIB landing found" and compare L4 CODE, L2 STD, L1 PLP, or L0 SRVC as appropriate.


6. Service Survey

Browse only after L0 SRVC is plausible and the current-stack survey has not found a suitable already-approved alternative. Do not use service search to justify replacement, deletion, or abstraction; use it only to compare current external service alternatives.

  1. Confirm commodity shape. The capability is non-differentiating, broadly available as a service, or mostly operational burden.
  2. Define constraints. Record data sensitivity, compliance, latency, region, cost model, lock-in tolerance, operational model, migration needs, and rollback needs.
  3. Search current alternatives. Use primary sources where possible: official docs, pricing pages, SLA pages, security/compliance pages, and migration guides.
  4. Compare adjacent levels. Include "keep local", L3 LIB, L1 PLP, and current-stack alternatives in the comparison.
  5. Recommend only with proof. L0 SRVC wins only when it reduces code, operations, risk, or maintenance without violating constraints.

When the user asks for specific service alternatives, browse. Provider capabilities, pricing, SLAs, regions, and compliance posture are time-sensitive.


7. Bring-Down Protocol

  1. Define scope. Name the repos, subsystems, services, teams, or workflows under review.
  2. Inventory candidates. Find custom-maintained commodity behavior, copy/paste code, repeated scripts, repeated PR shapes, local wrappers, and one-off infra/app patterns.
  3. Question necessity. Delete obsolete or non-problem-solving code first.
  4. Search the current stack. Look for existing lower placements: framework-native capabilities, approved libraries, standards, platform products, or approved services.
  5. Survey libraries when L3 is plausible. Search stack-native indexes and current primary sources before recommending a new or not-yet-approved library.
  6. Compare variation. List what is common, what differs, and why.
  7. Assign current level. Use the scale with evidence.
  8. Choose target level. Pick the lowest responsible level by maintenance burden, commodity fit, risk, and variation.
  9. Name landing capability. Record the exact package/API, standard, platform product, or service and its maintenance owner.
  10. Reject same-owner moves. If the target owner is the same codebase/team, remove it from bring-down recommendations and hand it off.
  11. Compute distance. Current level - target level.
  12. Choose one move. Move down one level unless the intermediate level is already satisfied. If the move is purely internal subsystem extraction or patternization, hand off to architecture skills.
  13. Prove and retire. Migrate at least one real consumer and remove the old duplicate path.

Prioritize by:

priority = bring-down distance x maintenance burden x repetition x churn x blast radius

If the improvement is mainly about human execution rather than code shape, use push-out. If it is mainly about check timing, use defect-shift-left.


8. Move Patterns

MoveUse whenAction
L4 CODE to L3 LIBApproved library/framework capability fitsReplace bespoke code with that capability
L3 LIB to L2 STDNew instances repeat setup and an externally owned standard path existsAdopt generator, policy, framework convention, or reference architecture
L2 STD to L1 PLPTeams need a governed capabilityUse or build a platform product with validation, observability, and support
L1 PLP to L0 SRVCCapability is commodityReplace with external service or managed commodity

Each move must include migration and deletion criteria. A new abstraction with all old copies still alive is inventory, not simplification.

Do not emit L4 CODE to L4 CODE moves. Do not emit L4 CODE to L2 STD unless the named standard path is maintained outside the consuming code owner.


9. Do Not Confuse With Placement

bring-down changes reuse/specificity altitude. It does not assign domain, tier, layer, or dependency direction.

When the best answer is to extract a subsystem or patternize inside the codebase, use morphogenetic-architecture, architecture-guidelines, and architecture-as-code instead. Use structural-simplification to verify any externalization or platform move actually reduces complexity.

Here, "down" means less bespoke and more systemic, not lower in the dependency graph.


10. Anti-Patterns

Anti-patternCorrection
Replacing custom code only because it existsProve commodity fit, maintenance burden, risk reduction, or repetition
Shared abstraction hiding real variationSplit stable core from explicit extension points
Building a new shared thing before looking locallySearch the current stack for existing lower placements first
Library recommendation from stale memorySearch stack-native indexes and current primary sources before recommending new L3 LIB
GitHub stars as library proofRequire exact fit, compatibility, maintenance, security, license, migration, and deletion evidence
Triggering bring-down for repo-local subsystem extractionUse architecture skills unless the target is an external/library/platform capability
Recommendation has no landing capabilityName the exact package/API, standard, platform product, or service before recommending it
Target has the same maintenance ownerExclude it from bring-down and hand off to the relevant skill
Platform product without adoptionMeasure consumers, escape hatches, and support load
Template with no enforcementAdd lint, generator checks, or review gate where feasible
Managed service for differentiating logicKeep local or extracted as a subsystem where domain value lives
Service selection from stale memoryBrowse current primary sources before recommending alternatives
Wrapper around commodity service with no added policyDelete wrapper or state the invariant it enforces
Replacement without deleting custom codeRequire migration and retirement criteria
Treating bring-down as layer movementUse morphogenetic-architecture for placement and dependency direction

11. Output Contract

Emit results in this shape:

Only put valid owner-changing bring-down moves in Candidates and Priorities. Put same-owner refactors, repo-local patternization, and vague ideas without a landing capability in Handoffs or Gaps. If there are no valid moves, say Decision: No bring-down recommendation; hand off candidates. Do not use wide tables. If a section would need more than 5 columns, use labeled bullets instead. Include Library search only when a library survey was needed.

Scope:          <repos/subsystems/services/teams/workflows>
Mode:           Assessment | Improvement | Roadmap
Decision:       <valid bring-down move, or no bring-down recommendation>
Summary:        <2-4 sentences: main duplication, best bring-down move, key risk>

Candidates:
- <candidate>
  - Move: <current level> -> <target level> (distance <n>)
  - Landing: <exact package/API/standard/platform/service>
  - Owners: <current owner> -> <target owner>
  - Evidence: <maintenance burden, repetition, churn, or risk>
  - Variation: <what differs and whether it blocks the move>
  - Confidence: <High | Medium | Low>
  - Next action: <smallest proof or migration step>

Priorities:
1. <candidate>
   - Why now: <priority driver>
   - Move: <current level> -> <target level>
   - Landing: <exact capability>
   - Migration proof: <real consumer or workflow to migrate>
   - Retire: <custom code/path to delete after proof>

Gaps:
- <missing evidence, unclear ownership, unproven repetition, variation risk, or excluded candidate>

Library search:
- Stack: <detected language/framework/runtime/package manager>
- Indexes: <package indexes, docs, and source hosts searched>
- Selected: <exact package/API/plugin, or "no L3 LIB landing found">
- Rejected: <credible alternatives rejected and why>
- Evidence: <current source/date for compatibility, maintenance, security, or license>

Handoffs:
- <candidate>
  - Reason excluded: <same owner, repo-local patternization, vague landing, or non-bring-down issue>
  - Better skill: <architecture/refactor/push-out/defect-shift-left/etc.>

Service comparison:
- <service option>
  - Fit: <why it matches or does not>
  - Gaps: <constraint failures or unknowns>
  - Lock-in: <low/medium/high plus reason>
  - Migration cost: <small/medium/large plus reason>
  - Rollback path: <how to return to current approach>

12. See Also

  • functionality-complexity-tradeoff - decide whether the functionality should exist before replacing it.
  • structural-simplification - verify the externalization actually reduces subsystem kinds, edges, depth, or count.
  • morphogenetic-architecture - place the resulting subsystem and check observed boundary pressure after bring-down chooses the reuse level.
  • push-out - move recurring operational work outward.
  • defect-shift-left - move defect detection earlier.
  • architecture-as-code - enforce accepted patterns as code.

Frequently asked questions

What does the Bring Down AI skill do?

Moves bespoke, duplicated, or over-local code down into reusable capabilities outside the immediate implementation: framework-native features, stack-aware approved libraries, internal platform products, managed services, or external standards. Use when assessing whether custom code should be replaced by a lower-level capability, especially an external library or service; when reducing bespoke wrappers around commodity behavior; or when designing an improvement roadmap for reuse and platform leverage. Skip purely in-codebase subsystem extraction or patternization unless the target is an appr...

Why use Bring Down on TypingMind?

Because you install it once and use it with any model. Bring Down 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 Bring Down in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/l-gevity/l-gevity-skills/tree/main/.agents/skills/bring-down. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bring Down?

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 Bring Down?

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

Is the Bring Down AI skill free?

Yes. It is published on GitHub by l-gevity 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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