Agent skill

Code Forge

by techygarg in techygarg/lattice

Generate implementation code from an approved design blueprint or verbal requirements.

MITAuto-check passedDevelopment

Install Code Forge

skills CLI
$ npx skills add techygarg/lattice --skill code-forge -a claude-code

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

GitHub CLI
$ gh skill install techygarg/lattice code-forge --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/techygarg/lattice.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/code-forge .claude/skills/code-forge && 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
code-forge
GitHub stars
198
Token cost
~3.1k tokens
SKILL.md length
1,560 words
Files
1
Skills in repo
33
Repo updated
First seen
Licence
MIT

At a glance

Generate implementation code from an approved design blueprint or verbal requirements.

  • Works in 5 steps: Establish Implementation Context → Plan Implementation Order → Implement Per Component → …
  • Moving from design to code
  • SKILL.md covers Required Skills and Workflow
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Code Forge is an agent skill from techygarg/lattice. Generate implementation code from an approved design blueprint or verbal requirements. Composes context anchoring, architecture, clean code, DDD, security, and test quality into an inside-out implementation workflow. Use when moving from design to code, implementing approved contracts, or when the user says 'implement', 'code this', 'build it', 'forge the code', or 'generate the code'.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Domain-driven design, Code quality and Design to code. The repository describes itself as: Install engineering discipline into any AI coding assistant. Composable skills for design, implementation, review, and team standards. Better process, not just better prompts. The licence is MIT.

When your agent uses it

  • Moving from design to code
  • Implementing approved contracts
  • The user says implement
  • Generate the code

Example prompts

  • “implement”
  • “code this”
  • “build it”
  • “/code-forge”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Establish Implementation Context
  2. Plan Implementation Order
  3. Implement Per Component
  4. Cross-Component Verification
  5. Enrich Context

What it can do on your machine

Read from SKILL.md and the folder at commit 4d6c35f. 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

Code Forge loads about 3.1k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 1,560 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

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 techygarg/lattice at commit 4d6c35f, republished under its MIT licence (© techygarg). 1,560 words, ~3,081 tokens.

Download SKILL.mdSave it as .claude/skills/code-forge/SKILL.md (or your agent's skills folder).
name
code-forge
description
Generate implementation code from an approved design blueprint or verbal requirements. Composes context anchoring, architecture, clean code, DDD, security, and test quality into an inside-out implementation workflow. Use when moving from design to code, implementing approved contracts, or when the user says 'implement', 'code this', 'build it', 'forge the code', or 'generate the code'.

Code Forge

Required Skills

Read and apply:

  1. framework:knowledge-priming -- Load project context (stack, architecture, conventions) so implementation matches the real project. (always)
  2. framework:context-anchoring -- Find and load the feature's context anchor doc; enrich it as implementation decisions are made (Create / Load / Enrich behaviors). (always)
  3. framework:learning-harvest -- Load prior operational learnings to inform implementation at session start; harvest new ones at session end. (always)
  4. framework:collaborative-judgment -- Surface genuine judgment calls as structured options instead of silently assuming. (always)
  5. framework:architecture -- Layer placement, dependency direction, structural validation. (always)
  6. framework:clean-code -- Craft guardrails: SRP, naming, complexity, error handling. (always)
  7. framework:domain-driven-design -- Aggregates, entities, value objects, domain services. (conditional: domain-layer components only)
  8. framework:secure-coding -- Trust bounds, injection prevention, secrets handling. (conditional: trust-boundary code only)
  9. framework:test-quality -- AAA structure, isolation, assertion quality, naming. (always when writing tests)

Workflow

Step 1: Establish Implementation Context
  1. Run framework:learning-harvest Load behavior. Focus hint: "implementation session — focus: implementation craft, quality signals, reliability".
  2. Run framework:context-anchoring Document Discovery: scan the context base directory (per the atom's Config Resolution) for an existing anchor doc covering this feature's implementation.
    • Found → Load behavior. Present the structured acknowledgment: feature name, status, decision count, open questions, constraints. STOP: Honor every logged decision and constraint as an active commitment.
    • Not found → ask the user: "Is there a design doc or blueprint for this feature, or do we work from what we've discussed?" Accept either answer gracefully:
      • Doc provided → load it and follow it.
      • Proceed without → all atom rails still apply; there is simply no approved design doc to reference. Work from the verbal requirements in conversation.

Design completeness check — run both gates before Step 2 (no context doc exists → skip both, proceed as "Without approved design"):

Check 1 — status: Read the context doc frontmatter status.

  • approved → pass.
  • complete → this feature was already implemented. If the current request is new scope, recommend /design-blueprint for a fresh design pass; if the user confirms proceeding on the existing design, continue as "With approved design" (Check 2 still applies).
  • Anything else (including draft or a missing field) → STOP: "Context doc not approved (status: [value]). Run /design-blueprint first. Proceed anyway?" On confirmation → log it in the Decisions Log and continue as "Without approved design".

Check 2 — levels present: Scan the body for ## Design: Level 3 and ## Design: Level 4.

  • Both present → pass.
  • Either missing → STOP: "Missing [Level 3 / Level 4 / both]. Proceed anyway?" On confirmation → log the absent levels in the Decisions Log and treat them as gaps to fill during implementation.

Both pass → proceed as "With approved design".

Step 2: Plan Implementation Order

With approved design: extract the component list and layer assignments from the context anchor doc. Use the Level 2 (Components) decisions for layer placement and Level 3 (Interactions) for dependency flow.

Without approved design: classify the required components into architecture layers using the layer definitions from framework:architecture. For each component determine:

  • What is its primary responsibility? (business rules, data access, coordination, external I/O)
  • Which layer in the loaded architecture doc matches that responsibility?
  • What dependency constraints apply to that layer?

If framework:architecture resolved no layer definitions (neither defaults nor a custom doc), surface it: "No architecture rules available. Run /architecture-refiner to define your architecture standards. Proceeding without architecture guidance." Continue with the remaining atom rails.

Present the proposed layer assignments to the user for approval before proceeding.

In both cases, plan an inside-out implementation order following the dependency direction from the loaded architecture doc — start at the innermost layer (no outward dependencies) and work outward, so each layer's dependencies already exist when it is built.

Classify each operation per the flow patterns in the loaded architecture doc (e.g., command vs query flows, or the equivalent distinction in your architecture style).

Present the implementation plan — ordered component list, layer assignments, flow classifications — and confirm with the user before writing code. If the user rejects or corrects the plan, revise and re-present it. STOP: Never start coding on an unagreed plan.

After the plan is approved, ask the user to choose a review mode:

"How should we review the implementation?"

  1. Layer-by-layer (recommended) — implement each layer fully, pause for review before the next. One review point per layer.
  2. Full autonomy — implement everything end-to-end, present the complete result. One review point at the end. (If a blueprint exists, still pause on any deviation from the approved design.)
  3. Component-by-component — pause after each individual component for feedback. Maximum review points.

Default to layer-by-layer if the user expresses no preference.

Show full SKILL.md (821 more words)Show less
Step 3: Implement Per Component

For each component in planned order, generate code and tests together — tests are not an afterthought.

Every component:

  • Prefer the simpler path first. Before writing custom code: does a stdlib function, platform built-in, or existing dependency already cover this? If yes, use it. Can it be expressed in shorter code? Use that. Write custom code only when the simpler options genuinely fall short.
  • Place in the correct architecture layer per framework:architecture; dependency direction follows the loaded architecture rules.
  • Apply framework:clean-code self-validation during generation. Inline checks: SRP compliance, meaningful naming, low cyclomatic complexity, proper error handling, no magic values, clean function signatures, no dead code, appropriate abstraction level, clear control flow, minimal comments (the code documents itself).
  • Write tests using framework:test-quality self-validation.
  • Run what you wrote, where the environment allows: execute the component's tests before presenting and include the result in the compliance note. If execution is not available, say so — never imply tests passed when they were only written.

Conditional checks per component:

  • Domain layer → apply framework:domain-driven-design self-validation.
  • Trust boundary (HTTP handler, external API call, user-input processing, file I/O) → apply framework:secure-coding self-validation.
  • Blueprint exists AND Level 4 was confirmed present in Step 1 → verify the component fulfills its L4 (Contracts) specification; flag any deviation from the agreed contract. If the user proceeded without L4 (Check 2 failed), skip this check — there are no contracts to verify against.

Post-generation verification (every component, all review modes):

After generating each component, before presenting it to the user:

  1. Run the Self-Validation Checklist from each applicable atom against every function/class in the component. Atoms use imperative STOP-verify language — follow it literally.
  2. Run the Active Anti-Pattern Scan from each applicable atom; check every box on the scan list.
  3. Violations found → fix before presenting.
  4. Judgment calls flagged (see each atom's Ambiguity Signals) → collect them and present via the framework:collaborative-judgment protocol before showing code. Never silently resolve.
  5. All checks pass with no flagged judgment calls → present with a brief compliance note ("All clean-code, DDD checks pass") — one line when clean; verbose only when reporting violations and fixes.

Pacing — follow the user's chosen review mode:

  • Layer-by-layer: implement all components within a layer, present the full layer (code + tests) for review before starting the next layer.
  • Full autonomy: implement all layers continuously; present the complete implementation (all code + tests) at the end, then skip to Step 4.
  • Component-by-component: present each component with its tests individually; wait for approval before the next.
  • Exception (all modes): a component needs a significant deviation from the plan (new dependency, changed contract, unexpected complexity) → STOP: pause immediately and discuss before continuing, regardless of the chosen review mode.
Step 4: Cross-Component Verification

These checks verify architecture coherence, not code quality (already verified per-component in Step 3). After all components are implemented:

  • With blueprint: verify interaction flows match the L3 (Interactions) design — every designed interaction is traceable in the code.
  • Dependency direction: apply framework:architecture verification across all components — inter-component dependency direction follows the loaded architecture rules; no layer imports from a layer it is not permitted to depend on.
  • Zero Implementation Rule: check that no new components, interactions, or contracts were introduced beyond the Step 2 plan. Something added → flag it — it may be necessary, but it must be a conscious decision, not scope creep.
  • Final security scan: apply framework:secure-coding across component boundaries — data flowing between components crosses trust bounds safely.
  • Learnings check: if operational learnings were loaded in Step 1, verify that previously-flagged patterns did not recur in this implementation.
Step 5: Enrich Context

Throughout Steps 3-4, use framework:context-anchoring Enrich behavior to keep the living doc current:

  • Add key files as created — path + role in the doc's Key Files table (skip a path already listed).
  • Capture implementation decisions — library choices, pattern selections, deviations from the blueprint, tradeoffs made, as Decisions Log entries (decision, reasoning, alternatives considered).
  • Resolve open questions — when a design-phase question gets answered during implementation, log the answer as a decision entry AND remove the question from the Open Questions list.
  • No context doc exists and significant implementation decisions were made → suggest creating one.

Harvest learnings: run framework:learning-harvest Harvest behavior. Session context: "implementation session — code generation from design contracts". Synthesize and propose cross-cutting patterns from this session — implementation gotchas, design-to-reality gaps, library/framework lessons. The user confirms what enters the document. STOP: run this before closing the feature lifecycle below.

Close the feature lifecycle: write status: complete into the context doc frontmatter. STOP: required discrete file edit.

STOP: do not write status to requirement_doc. The requirement's status belongs to whoever manages it — a human, or an external system it may live in. This molecule manages only its own context doc.

After enriching the context doc, recommend review:

"Implementation complete. Recommend running /review on the generated code before considering the feature done — it provides an independent quality assessment against the same atom standards, catches issues the generator may be blind to, and captures learnings for future sessions."

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

Files

Just SKILL.md in skills/code-forge of techygarg/lattice.

Open the folder on GitHubat commit 4d6c35f

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Questions about Code Forge

What does Code Forge do?

Generate implementation code from an approved design blueprint or verbal requirements. Code Forge is an agent skill from techygarg/lattice. Generate implementation code from an approved design blueprint or verbal requirements.

When should I use Code Forge?

Code Forge fits situations like: moving from design to code; implementing approved contracts; the user says implement; generate the code.

How do I install Code Forge in Claude Code?

Run `npx skills add techygarg/lattice --skill code-forge -a claude-code`. Or copy the skill folder (skills/code-forge in techygarg/lattice) into .claude/skills/code-forge in your project. Claude Code loads it when a task matches its description.

How do I install Code Forge in Codex?

Run `npx skills add techygarg/lattice --skill code-forge -a codex`. Or copy the skill folder (skills/code-forge in techygarg/lattice) into .agents/skills/code-forge in your project. Codex loads it when a task matches its description.

Can I use Code Forge 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 techygarg/lattice --skill code-forge -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-forge, .gemini/skills/code-forge, .github/skills/code-forge and .opencode/skills/code-forge in your project.

What does Code Forge need to run?

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

Does Code Forge 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 Code Forge 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 Code Forge use?

Code Forge 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 Code Forge use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Code Forge?

Skills that share tags, products or a category with Code Forge: Nerdzao Elite (aiskillstore/marketplace, 430 stars), Brooks Review (hyhmrright/brooks-lint, 1.5k stars), Dotnet Csharp (novotnyllc/dotnet-artisan, 233 stars) and 111 Java Maven Dependencies (jabrena/plinth, 445 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Forge?

techygarg (a GitHub user) maintains it in techygarg/lattice, which has 198 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 6, 2026.

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