Dark Architecture Diagram Builder
Cocoon-AI/architecture-diagram-generator
Creates dark-themed system, cloud, security and network architecture diagrams as self-contained HTML files with inline SVG and CSS.
Agent skill
Master methodology for reverse-engineering a codebase into behavioral specs with cited evidence, reading every line across source, binaries, docs, runtime and git history.
$ npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install prime-radiant-inc/greenfield analysis-pipeline --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/prime-radiant-inc/greenfield.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis-pipeline .claude/skills/analysis-pipeline && rm -rf skills-srcUse ~/.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/
Install the "analysis-pipeline" agent skill from https://github.com/prime-radiant-inc/greenfield/tree/main/skills/analysis-pipeline into .claude/skills/analysis-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-pipeline", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/prime-radiant-inc/greenfield/tree/main/skills/analysis-pipelineType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install prime-radiant-inc/greenfield analysis-pipeline --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prime-radiant-inc/greenfield.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/analysis-pipeline .agents/skills/analysis-pipeline && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analysis-pipeline" agent skill from https://github.com/prime-radiant-inc/greenfield/tree/main/skills/analysis-pipeline into .agents/skills/analysis-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-pipeline", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install prime-radiant-inc/greenfield analysis-pipeline --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prime-radiant-inc/greenfield.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/analysis-pipeline .cursor/skills/analysis-pipeline && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "analysis-pipeline" agent skill from https://github.com/prime-radiant-inc/greenfield/tree/main/skills/analysis-pipeline into .cursor/skills/analysis-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-pipeline", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/prime-radiant-inc/greenfield.git --path skills/analysis-pipeline--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install prime-radiant-inc/greenfield analysis-pipeline --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prime-radiant-inc/greenfield.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/analysis-pipeline .gemini/skills/analysis-pipeline && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "analysis-pipeline" agent skill from https://github.com/prime-radiant-inc/greenfield/tree/main/skills/analysis-pipeline into .gemini/skills/analysis-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-pipeline", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install prime-radiant-inc/greenfield analysis-pipelineInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/prime-radiant-inc/greenfield.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/analysis-pipeline .github/skills/analysis-pipeline && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "analysis-pipeline" agent skill from https://github.com/prime-radiant-inc/greenfield/tree/main/skills/analysis-pipeline into .github/skills/analysis-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-pipeline", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install prime-radiant-inc/greenfield analysis-pipeline --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/prime-radiant-inc/greenfield.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/analysis-pipeline .opencode/skills/analysis-pipeline && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "analysis-pipeline" agent skill from https://github.com/prime-radiant-inc/greenfield/tree/main/skills/analysis-pipeline into .opencode/skills/analysis-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analysis-pipeline", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
analysis-pipelineMaster methodology for reverse-engineering a codebase into behavioral specs with cited evidence, reading every line across source, binaries, docs, runtime and git history.
The skill sets the core principle for a family of analysis agents: analyze deeply, output in behavioral language with no source identifiers, cite every behavioral claim to its evidence, and analyze completely rather than skipping sections marked lower priority. It insists on exhaustive reading: every function, method, class and module must actually be read and understood, not inferred from grep matches alone.
Work runs through a seven-layer pipeline (described as a dot graph) that starts by auto-discovering whichever intelligence sources are available and consuming all of them by default, unless specific ones are excluded. Those sources include raw sources such as source code, decompiled binaries, runtime observation, visual exploration, binary analysis and git history, and public sources such as documentation, SDK and ecosystem material, community discussion and machine-readable contracts, each handled by a named agent role and written to its own workspace folder.
The goal is a provenance-tracked set of behavioral specifications, test vectors and acceptance criteria that a fresh team could use to reimplement the target without inheriting its internal structure.
7 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 6e6d4b4. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are dot and markdown).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Reverse Engineering Analysis Pipeline loads about 3.6k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 833 words of instructions outside code blocks.
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.
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.
The full file from prime-radiant-inc/greenfield at commit 6e6d4b4, republished under its Apache-2.0 licence (© prime-radiant-inc). 833 words, ~3,565 tokens.
.claude/skills/analysis-pipeline/SKILL.md (or your agent's skills folder).You analyze targets from multiple perspectives. You output behavioral specifications with provenance.
Analyze deeply. Output behaviorally. Track provenance. Analyze COMPLETELY.
<!-- cite: --> annotations.You MUST read every single line of code. You MUST identify every single routine.
digraph pipeline {
rankdir=TB;
"Start analysis" [shape=doublecircle];
"Layer 1: Gather intelligence" [shape=box];
"Layer 2: Synthesize and map modules" [shape=box];
"Layer 3: Write deep behavioral specs" [shape=box];
"Gate 1: Verification passed?" [shape=diamond];
"Gate 1b: Source-to-spec completeness" [shape=diamond];
"Layer 4: Generate test vectors and acceptance criteria" [shape=box];
"Gate 2: Spec review passed?" [shape=diamond];
"Layer 5: Sanitize raw specs to output/" [shape=box];
"Layer 6: Second-pass review passed?" [shape=diamond];
"Layer 7: Fidelity validation passed?" [shape=diamond];
"Analysis complete" [shape=doublecircle];
"Remediate Gate 1 findings" [shape=box];
"Remediate Gate 2 findings" [shape=box];
"Remediate Layer 6 findings" [shape=box];
"Remediate Layer 7 findings" [shape=box];
"STOP: Gate 1 failed after 3 attempts" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];
"STOP: Gate 2 failed after 3 attempts" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];
"STOP: Layer 6 failed after 3 attempts" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];
"STOP: Layer 7 failed after 3 attempts" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];
"Start analysis" -> "Layer 1: Gather intelligence";
"Layer 1: Gather intelligence" -> "Layer 2: Synthesize and map modules";
"Layer 2: Synthesize and map modules" -> "Layer 3: Write deep behavioral specs";
"Layer 3: Write deep behavioral specs" -> "Gate 1: Verification passed?";
"Gate 1: Verification passed?" -> "Gate 1b: Source-to-spec completeness" [label="yes"];
"Gate 1: Verification passed?" -> "Remediate Gate 1 findings" [label="no"];
"Remediate Gate 1 findings" -> "Gate 1: Verification passed?" [label="attempt < 3"];
"Remediate Gate 1 findings" -> "STOP: Gate 1 failed after 3 attempts" [label="attempt >= 3"];
"Gate 1b: Source-to-spec completeness" -> "Layer 4: Generate test vectors and acceptance criteria" [label="pass"];
"Gate 1b: Source-to-spec completeness" -> "Remediate Gate 1 findings" [label="gaps found"];
"Layer 4: Generate test vectors and acceptance criteria" -> "Gate 2: Spec review passed?";
"Gate 2: Spec review passed?" -> "Layer 5: Sanitize raw specs to output/" [label="yes"];
"Gate 2: Spec review passed?" -> "Remediate Gate 2 findings" [label="no"];
"Remediate Gate 2 findings" -> "Gate 2: Spec review passed?" [label="attempt < 3"];
"Remediate Gate 2 findings" -> "STOP: Gate 2 failed after 3 attempts" [label="attempt >= 3"];
"Layer 5: Sanitize raw specs to output/" -> "Layer 6: Second-pass review passed?";
"Layer 6: Second-pass review passed?" -> "Layer 7: Fidelity validation passed?" [label="yes"];
"Layer 6: Second-pass review passed?" -> "Remediate Layer 6 findings" [label="no"];
"Remediate Layer 6 findings" -> "Layer 6: Second-pass review passed?" [label="attempt < 3"];
"Remediate Layer 6 findings" -> "STOP: Layer 6 failed after 3 attempts" [label="attempt >= 3"];
"Layer 7: Fidelity validation passed?" -> "Analysis complete" [label="yes"];
"Layer 7: Fidelity validation passed?" -> "Remediate Layer 7 findings" [label="no"];
"Remediate Layer 7 findings" -> "Layer 7: Fidelity validation passed?" [label="attempt < 3"];
"Remediate Layer 7 findings" -> "STOP: Layer 7 failed after 3 attempts" [label="attempt >= 3"];
}Layer 1 auto-discovers available intelligence sources and consumes all of them by default:
| Source Type | Agent Roles | Output Location | Origin |
|---|---|---|---|
| Source Code | bundle-splitter, chunk-analyzer, function-analyzer | workspace/raw/source/ | RAW |
| Decompiled Binaries | decompiler → chunk-analyzer | workspace/raw/source/decompiled/ | RAW |
| Public Docs | doc-researcher | workspace/public/docs/ | PUBLIC |
| SDK/Ecosystem | sdk-analyzer, integration-test-miner | workspace/public/ecosystem/ | PUBLIC |
| Community | community-analyst | workspace/public/community/ | PUBLIC |
| Runtime Observation | cli-explorer, web-ui-explorer, behavior-observer, ux-documenter | workspace/raw/runtime/ | RAW |
| Visual Exploration | visual-explorer | workspace/raw/runtime/visual/ | RAW |
| Binary Analysis | binary-surveyor, binary-deep-analyzer | workspace/raw/binary/ | RAW |
| Git History | git-archaeologist | workspace/raw/project-history/ | RAW |
| Test Suites | test-reader, test-runner | workspace/raw/test-evidence/ | RAW |
| Machine-Readable Contracts | contract-parser | workspace/public/contracts/ | PUBLIC |
Sources are auto-discovered. Use --exclude to skip specific source types.
Sources are excluded only by --exclude flag or user request during discovery negotiation.
More independent source types covering the same behaviors = higher-confidence specs.
Minimum for high-confidence: 2+ independent source types covering the same behaviors. When a behavioral claim is supported by only one source, it gets confidence=inferred at best. When 2+ independent sources agree, it gets confidence=confirmed.
Every behavioral claim MUST have a provenance citation:
Sessions expire after 30 minutes of inactivity.
<!-- cite: source=source-code, ref=workspace/raw/source/analysis/chunk-0046.md:23, confidence=confirmed, agent=deep-dive-analyzer, corroborated_by=runtime-observation -->Confidence levels:
confirmed — 2+ independent sources agreeinferred — single source, direct evidenceassumed — reasoning from indirect evidenceReference the provenance-methodology skill for complete format details.
Layer 4 (test vector generation) is NOT optional. Test vectors are the bridge between "spec describes it" and "implementer actually builds it."
digraph test_vectors {
rankdir=TB;
"Layer 3 specs complete" [shape=doublecircle];
"Generate test vectors for P0 claims" [shape=box];
"Generate edge case vectors" [shape=box];
"Generate dependency contract vectors" [shape=box];
"All P0 behaviors have vectors?" [shape=diamond];
"Gate 1.5 PASS" [shape=doublecircle];
"STOP: Add missing vectors" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];
"Layer 3 specs complete" -> "Generate test vectors for P0 claims";
"Generate test vectors for P0 claims" -> "Generate edge case vectors";
"Generate edge case vectors" -> "Generate dependency contract vectors";
"Generate dependency contract vectors" -> "All P0 behaviors have vectors?";
"All P0 behaviors have vectors?" -> "Gate 1.5 PASS" [label="yes"];
"All P0 behaviors have vectors?" -> "STOP: Add missing vectors" [label="no"];
}Each vector follows Given/When/Then:
### TV-LOADER-001: TypeScript file execution
GIVEN: A file `test.ts` containing `interface Foo { x: number }; console.log("ok")`
WHEN: Run through tsx
THEN: Output is "ok", exit code 0
### TV-LOADER-002: JSON import without explicit attribute
GIVEN: A file importing `./data.json` without `with { type: 'json' }`
WHEN: Run through tsx on Node >= 18.19
THEN: Import succeeds (hook auto-adds the attribute)
### TV-LOADER-003: Invalid custom tsconfig
GIVEN: `--tsconfig nonexistent.json` flag specified
WHEN: Run through tsx
THEN: Error reported, non-zero exit codeworkspace/
├── public/ # Public origin
│ ├── docs/ # doc-researcher output
│ ├── ecosystem/ # sdk-analyzer output
│ └── contracts/ # contract-parser output
├── raw/ # RAW - requires sanitization
│ ├── source/ # Source code analysis
│ │ ├── chunks/
│ │ ├── analysis/
│ │ ├── functions/
│ │ ├── manifests/
│ │ └── exploration/
│ ├── runtime/ # Runtime observation
│ │ ├── cli/
│ │ ├── web/
│ │ ├── behaviors/
│ │ ├── ux-flows/
│ │ └── visual/ # visual-explorer output
│ ├── binary/ # Binary analysis
│ ├── project-history/ # git-archaeologist output
│ ├── test-evidence/ # test-reader, test-runner output
│ ├── synthesis/ # Layer 2 output
│ │ ├── features/
│ │ ├── architecture/
│ │ ├── api/
│ │ ├── behavioral-summaries/
│ │ └── module-map.md
│ └── specs/ # Layer 3/4 output
│ ├── modules/
│ ├── journeys/
│ ├── contracts/
│ ├── test-vectors/
│ └── validation/
├── output/ # Sanitized specs
│ └── specs/
├── provenance/ # Session logs
│ └── sessions/
└── workspace.json # Metadata| Type | Examples | Why |
|---|---|---|
| Environment variables | DATABASE_URL, DEBUG | External API |
| CLI flags | --format, --workers | External API |
| Config keys | upstreams, log_level | External API |
| API fields | request_id, batch_size | Protocol spec |
| User-facing paths | ~/.app/config.toml | Behavioral contract |
| Protocol names | SSE, gRPC, OAuth | Behavioral contract |
| Error messages | Exact text | UX contract |
| Type | Examples | Why |
|---|---|---|
| Function names | parseArgs(), handleRequest() | Internal code |
| Variable names | requestId, configMap | Internal code |
| Minified identifiers | sp, r0, Ab2() | Internal code |
| Line numbers | "Line 1234" | Internal code |
| Source file paths | "in src/cli.ts" | Internal code |
| Code structure | "calls X then Y" | Internal code |
/analyze [path] # Discover and consume everything
/analyze [path] --exclude source # Black-box analysis
/analyze [path] --exclude git-history # Skip commit mining/sanitize workspace/ to produce output specsThe output specs in workspace/output/ are the input for implementation. Greenfield stops here. The implementation team reads:
workspace/output/specs/ — per-domain behavioral specsworkspace/output/test-vectors/ — concrete input/output pairs to drive TDDworkspace/output/validation/acceptance-criteria/ — Given/When/Then acceptance criteriaThe test vectors and acceptance criteria give the implementation pipeline a head start on proof obligations. How the implementation is carried out — walking skeletons, iteration planning, TDD cadence, review protocols — is outside Greenfield's scope.
Greenfield's job is to produce specs good enough to implement from. Implementation is a separate concern with its own methodology.
© prime-radiant-inc, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/analysis-pipeline of prime-radiant-inc/greenfield.
Open the folder on GitHubat commit 6e6d4b4
Reverse Engineering Analysis Pipeline 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Reverse Engineering Analysis Pipeline this skillprime-radiant-inc/greenfield | 292 | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| Dark Architecture Diagram BuilderCocoon-AI/architecture-diagram-generator | 7.4k | 1 repos | ~2.1k | Automated safety check: Pass | MIT | |
| Deepwiki Rssopaco/deepwiki-rs | 3.1k | — | ~748 | Automated safety check: Pass | MIT | |
| Mermaid Diagramsjjmartres/opencode | 133 | 6 repos | ~1.9k | Automated safety check: Pass | MIT | |
| C4 Architecture Diagramslexler/skill-factory | 239 | — | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| MVP Technical DesignKhazP/vibe-coding-prompt-template | 3.1k | — | ~512 | Automated safety check: Pass | MIT |
Cocoon-AI/architecture-diagram-generator
Creates dark-themed system, cloud, security and network architecture diagrams as self-contained HTML files with inline SVG and CSS.
sopaco/deepwiki-rs
AI-powered Rust documentation generation engine for comprehensive codebase analysis, C4 architecture diagrams, and automated technical documentation.
jjmartres/opencode
Helps an agent pick the right Mermaid diagram type and write the syntax for class, sequence, flow, ER, C4, state and other software diagrams.
lexler/skill-factory
Creates C4 model diagrams at every zoom level, from system landscape to code, in ASCII, Mermaid or Structurizr, for designing or documenting software architecture.
KhazP/vibe-coding-prompt-template
Writes an MVP technical design from agreed requirements, covering architecture, data ownership, integration contracts, deployment and tradeoffs, then hands off to the next stage.
shift-editor/shift
Updates or creates DOCS.md files for Shift subsystems, recording the architecture invariants and constraints that cannot be learned from reading the source.
prime-radiant-inc/greenfield
Mines tutorials, forums, reviews, issues and changelogs for observed product behavior, using six search channels and consensus analysis.
prime-radiant-inc/greenfield
Runs untrusted analysis targets inside Docker or Podman containers with memory, CPU and process limits, covering image builds, lifecycle, command execution and cleanup.
prime-radiant-inc/greenfield
Finds OpenAPI, GraphQL, Protobuf and JSON Schema files in a codebase and extracts behavioral claims from them as part of a reverse-engineering workflow.
prime-radiant-inc/greenfield
Method for extracting behavioral specifications from a product's public documentation: tiered search order, claim extraction rules, output structure, stop criteria and gap analysis.
prime-radiant-inc/greenfield
Layer 1 skill for SDK and ecosystem analysis. An agent skill from prime-radiant-inc/greenfield.
prime-radiant-inc/greenfield
Cross-validates sanitized output specs against raw source specs to detect lost behavioral detail, dropped constants, missing features, or diluted precision.
Categories
Master methodology for reverse-engineering a codebase into behavioral specs with cited evidence, reading every line across source, binaries, docs, runtime and git history. The skill sets the core principle for a family of analysis agents: analyze deeply, output in behavioral language with no source identifiers, cite every behavioral claim to its evidence, and analyze completely rather than skipping sections marked lower priority. It insists on exhaustive reading: every function, method, class and module must actually be read and understood, not inferred from grep matches alone.
Reverse Engineering Analysis Pipeline fits situations like: reverse-engineering a codebase into a clean behavioral specification; producing test vectors and acceptance criteria from an existing system; analyzing a product across source, binaries, docs and runtime behavior with full provenance.
Run `npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a claude-code`. Or copy the skill folder (skills/analysis-pipeline in prime-radiant-inc/greenfield) into .claude/skills/analysis-pipeline in your project. Claude Code loads it when a task matches its description.
Run `npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a codex`. Or copy the skill folder (skills/analysis-pipeline in prime-radiant-inc/greenfield) into .agents/skills/analysis-pipeline in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add prime-radiant-inc/greenfield --skill analysis-pipeline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analysis-pipeline, .gemini/skills/analysis-pipeline, .github/skills/analysis-pipeline and .opencode/skills/analysis-pipeline in your project.
SKILL.md names no scripts, command-line tools or credentials: Reverse Engineering Analysis Pipeline is instructions for the agent only.
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.
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.
Reverse Engineering Analysis Pipeline is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.6k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Reverse Engineering Analysis Pipeline: Dark Architecture Diagram Builder (Cocoon-AI/architecture-diagram-generator, 7.4k stars), Deepwiki Rs (sopaco/deepwiki-rs, 3.1k stars), Mermaid Diagrams (jjmartres/opencode, 133 stars) and C4 Architecture Diagrams (lexler/skill-factory, 239 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
prime-radiant-inc (a GitHub organization) maintains it in prime-radiant-inc/greenfield, which has 292 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on August 6, 2026.
Source: prime-radiant-inc/greenfield on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.