Devforge-ai
CLAUDE.md4 starsChanged 5 months agoApache-2.0View on GitHub
A multi-agent SDLC orchestration system: 10 agents × 5 phases → plan, build, verify, ship, operate. Tracer bullet development with Ralph Loop self-correction and Linear issue integration. Frontmatter required: name, description, tools (comma-separated), model (haiku/sonnet/opus) Model tiers: Haiku (templated), Sonnet (development), Opus (architecture/security decisions) Tools: Include only what the agent uses — Read, Write, Edit, Bash, Glob, Grep, WebFetch - Note: When querying codebase, prefer code-review-graph (bundled integration) over grep for structural analysis. See "Codebase Querying Strategy" below. Embed book…
In this repository48 other files it gives its agents.
- api-designSkill
- improve-codebase-architectureSkill
- architectureSkill
- cicdSkill
- cloud-infraSkill
- codebase-navigatorSkill
- code-qualitySkill
- code-reviewSkill
- code-standardsSkill
- configuration-managementSkill
- contract-testingSkill
- dependency-managementSkill
- diagnoseSkill
- documentationSkill
- dynamic-routingSkill
- enterprise-architectureSkill
- enterprise-test-suitesSkill
- git-guardrails-claude-codeSkill
- grill-meSkill
- handoffSkill
- intent-clarificationSkill
- triageSkill
- legacy-modernizationSkill
- llm-evalsSkill
- observabilitySkill
- ops-sreSkill
- performance-optimizationSkill
- to-issuesSkill
- playwrightSkill
- to-prdSkill
- setup-pre-commitSkill
- prototypeSkill
- pr-reviewSkill
- ralph-loopSkill
- requirementsSkill
- root-cause-analysisSkill
- security-auditSkill
- security-patchingSkill
- startup-lifecycleSkill
- tddSkill
- testingSkill
- threat-modelingSkill
- to-issuesSkill
- token-efficiencySkill
- to-prdSkill
- ux-designSkill
- write-a-skillSkill
- zoom-outSkill
The file
We split this CLAUDE.md into the rules it gives an agent
11 rules. About 0 are common, 5 are distinctive, and 6 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
6
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 32
Note: When querying codebase, prefer code-review-graph (bundled integration) over grep for structural analysis.
- Distinctive: line 46
All 10 agents are auto-wired to phase-specific skills: When an agent is invoked:
- Very specific: line 77
Create agent file with frontmatter skills: [...]
- Very specific: line 81
Create skills/skill-name/SKILL.md with frontmatter + methodology
- Very specific: line 82
Add to agent skills: field where relevant
- Very specific: line 89
Add agents with skills: field
- Distinctive: line 90
Create phase skill with output standards
- Distinctive: line 97
Agent not invoked: Check command references, verify description matches trigger, review tools field
- Very specific: line 98
Permission prompts: Add patterns to ~/.claude/settings.local.json under permissions.allow
- Distinctive: line 102
Read only when relevant:
- Very specific: line 116
Default label strings: needs-triage, needs-info, ready-for-agent, ready-for-human, wontfix.
≈ “Common” is an estimate: at least 1 in 5 of the 26,980 CLAUDE.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
SDLC Workflow Plugin
A multi-agent SDLC orchestration system: 10 agents × 5 phases → plan, build, verify, ship, operate. Tracer bullet development with Ralph Loop self-correction and Linear issue integration.
Project
- Type: Claude Code plugin
- Version: 2.0.0
- Repo: github.com/saitarrun/devforge-ai
Repository Map
agents/— 10 role-specific agents (product-manager, ux-designer, fullstack-engineer, qa-engineer, security-engineer, performance-engineer, devops-engineer, sre-engineer, data-engineer, technical-writer)skills/— 34 knowledge skills (architecture, testing, code-review, ralph-loop, grill-me, etc.)commands/— 10 orchestrator commands (/sdlc, /sdlc-plan, /sdlc-build, /sdlc-verify, /sdlc-ship, /sdlc-operate, /sdlc-review, /sdlc-implement, /to-prd, /to-issues)scripts/— CLI, install, uninstall, validation helpersREADME.md— User documentation and feature overview
Commands
- Install:
npm run install-localormake install-local - Validate:
make validate - Uninstall:
make uninstall
Agent Conventions
Frontmatter required: name, description, tools (comma-separated), model (haiku/sonnet/opus)
Model tiers: Haiku (templated), Sonnet (development), Opus (architecture/security decisions)
Tools: Include only what the agent uses — Read, Write, Edit, Bash, Glob, Grep, WebFetch
- Note: When querying codebase, prefer code-review-graph (bundled integration) over grep for structural analysis. See "Codebase Querying Strategy" below.
Embed book principles (QUANTS/INVEST/Testing Pyramid, Architecture: ADR/fitness functions, Pragmatic Programmer DRY/ETC, Clean Code naming/SOLID) in agent bodies.
Skill Conventions
Structure: skills/skill-name/SKILL.md (no tools, no model — pure knowledge)
Frontmatter: name, description, version
Content: Methodology, checklists, reference patterns. Load on-demand, not always-on.
Agent-Skill Auto-Wiring
All 10 agents are auto-wired to phase-specific skills. When an agent is invoked:
- Agent frontmatter declares
skills: [skill-a, skill-b, ...] - Command loads all phase skills into agent context (pure methodology, not tools)
- Agent system prompt says: "You have access to these skills: X, Y, Z. Apply them."
- Agent applies skill principles → polished industry-standard outputs
Codebase Querying Strategy
All agents and skills should query the codebase using this priority order:
- code-review-graph (preferred)
- Provides structural understanding, dependency graphs, coupling analysis
- More accurate than text search (understands relationships, not just text)
- Usage:
code_review_graph analyze <path>orcode_review_graph query <symbol> - Best for: "What depends on this function?", "Show me the call chain", "What's the coupling?"
- Grep (fallback when code-review-graph unavailable)
- Fast text search when structural analysis not needed
- Used for simple pattern matching, file location, quick searches
- Usage:
grep -r "pattern" <path>orGreptool - Acceptable for: "Find all usages of X", "Locate error message", "Find TODO comments"
Why this order?
- code-review-graph understands codebase structure and dependencies
- grep is fast but misses semantic relationships and context
- Fallback ensures agents work even if code-review-graph is unavailable
- Layered approach gives best results with graceful degradation
Extending
New agent:
- Create agent file with frontmatter
skills: [...] - Wire into command or
/sdlcorchestrator
New skill:
- Create
skills/skill-name/SKILL.mdwith frontmatter + methodology - Add to agent
skills:field where relevant
New command:
- Edit
/commands/sdlc.mdor create/commands/sdlc-phase-name.md - Command injects all phase skills before spawning agents
New phase:
- Add agents with
skills:field - Create phase skill with output standards
- Create command
/sdlc-phase-name.md - Wire into
/sdlcorchestrator
Troubleshooting
- Plugin not loading: Validate frontmatter (name/description/tools/model required), run
make validate, restart session - Agent not invoked: Check command references, verify description matches trigger, review tools field
- Permission prompts: Add patterns to
~/.claude/settings.local.jsonunderpermissions.allow
References
Read only when relevant:
README.md— Feature overview, usage examplesdocs/INTEGRATIONS.md— code-review-graph integration guide and codebase querying best practicesINSTALLATION.md— Setup and distribution
Agent skills
Issue tracker
Issues are tracked in Linear; agents interact via the Linear API or CLI. See docs/agents/issue-tracker.md.
Triage labels
Default label strings: needs-triage, needs-info, ready-for-agent, ready-for-human, wontfix. See docs/agents/triage-labels.md.
Domain docs
Single-context layout — one CONTEXT.md and docs/adr/ at the repo root. See docs/agents/domain.md.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

