SLCR
SLCR is an open-standard CLI and Rust reference implementation for writing software requirements in machine-readable format (JSON, TOML, YAML) that coding agents can parse and slice. Each requirement includes acceptance criteria mapped to validators such as tests, linters, ast-grep rules, or agent checks. Requirements link via refine/depend relationships and combine with AND, OR, XOR logic. The slcr check command reports which acceptance criteria are met; teams run it in CI to verify agent-generated code still satisfies the spec. An agent skill teaches coding agents to read SLCR specs and generate new requirements in the same format.
SLCR is an AI agent, integrating with ast-grep and GitHub. InnovaAI rates it 4.3 of 10 for agency adoption, best for Technical Lead, Engineering Manager and Project Manager roles.
Agency Audit
SLCR is an open-standard CLI that structures software requirements in machine-readable format (JSON, TOML, YAML) so coding agents receive only the context they need and teams can verify results against acceptance criteria using existing tools like tests and linters. Development agencies building long-lived codebases or handing implementation to coding agents benefit most, since SLCR eliminates context rot and compounds agent errors across tasks by making the spec a first-class artifact. Integrations with ast-grep and GitHub enable CI-native verification. Adoption pays off when your team runs 5+ agent-assisted development cycles per sprint.
5recommended
90/mo
No paid plan published
Moderate
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Technical Lead handling agent task specification and handoff
- Engineering Manager handling acceptance-criteria verification
- Project Manager handling code-review and rework cycles
- Your agency builds one-off projects or MVPs with lifespans under 6 months; SLCR's value accrues only on long-lived codebases where requirement drift and agent rework compound over time.
- Your team has no CI/CD pipeline or does not run automated tests; SLCR's acceptance-criteria validators (tests, linters, ast-grep) require existing tooling to function, and manual verification defeats the purpose.
- Your developers or agents currently work from high-level user stories without formal acceptance criteria; adopting SLCR requires rewriting your entire requirements discipline, not just adding a tool.
Internal Adoption Path
No paid plan published
90 hr/mo
5 seats × 18 hr each
$6,750/mo
modeled at $75/hr labor rate
No paid plan published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of SLCR
Requirement slicing for agents
SLCR delivers only the requirements and linked context a single task needs, reducing agent context-window waste and hallucination risk. Technical Leads use this to hand agents focused work without exposing the full spec.
Acceptance-criteria mapping to validators
Link each acceptance criterion to a test, linter, ast-grep rule, or agent check. Project Managers run slcr check to see which criteria are met without manual code review.
Requirement linking with AND/OR/XOR logic
Requirements refine and depend on each other via explicit relationships. Technical Leads use this to model complex feature dependencies and ensure agents understand prerequisite work.
CI-native verification
Run slcr check in your GitHub Actions pipeline to verify agent-generated code still satisfies the spec. Operations teams automate acceptance-criteria reporting without human intervention.
Multi-format requirement authoring
Write requirements in JSON, TOML, or YAML; teams choose the format that fits their existing config-management practices. Reduces friction for engineers already familiar with infrastructure-as-code workflows.
Agent skill for SLCR reading and writing
Teach coding agents to parse SLCR specs and generate new requirements in the same format. Technical Leads use this to close the loop between agent output and spec evolution.
What Makes SLCR Different
Unique advantages vs similar tools in this niche
Requirement slicing limits agent context to relevant requirements
vs Long prose specs that cause context rotHand an agent one requirement and the context it links to while the rest of the spec stays out of its context window.
Acceptance criteria map to existing validators and are checked in CI
vs Manual spec review after agent-generated code`slcr check` reports what's met, running red before build and green after in CI.
Latest Updates
Recent releases and improvements for SLCR
SLCR launches
New2026-10-29Launch of SLCR (Sliceable, Linked, Checkable Requirements), an open format for writing software requirements for both people and coding agents, including a CLI, getting started tutorial, and agent skill.
Value Equation
Outcome-likelihood-time-effort assessment for SLCR
Value math requires real pricing
The Value Equation (dream outcome × likelihood ÷ time × effort) feeds directly into ROI math. SLCR has no published pricing, so we hold this section until real numbers are available.
Contact SLCRPricing
Pricing data not yet available for SLCR.
Reality Check
SLCR requires upfront discipline to write requirements in structured format and teach agents the skill; teams accustomed to prose specs or ad-hoc agent prompting will face workflow friction. ROI emerges only after 3-4 sprints of consistent use, when acceptance-criteria checking becomes routine.
Moderate effort: standard configuration with some customization needed
How This Accelerates White-Label Services
Who It's For
- ✓software-development-agencies-building-long-lived-codebases
- ✓teams-handing-implementation-to-coding-agents
- ✓agencies-practicing-spec-driven-development
Acceleration Steps
- 1Create your account and complete setup wizard
- 2Configure write software requirements in json, toml, or yaml with acceptance criteria
- 3Connect ast-grep
- 4Launch your first client project
Academy for SLCR
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
SLCR Agency Implementation, Agent-Ready Requirement Specs
Learn how to write machine-readable software requirements that coding agents can parse, slice, and validate without hallucination. This course teaches agencies to structure specs in JSON/TOML/YAML, map acceptance criteria to validators, and run slcr check in CI pipelines to verify agent-generated code meets the original spec.
Open the courseNo Academy modules are published for this service yet. Browse the full Academy
Why this category matters
The commercial case before the tooling.
Core concepts
The mental model you need to price and scope the work.
- Wiring Over WidgetsConcept
The AI agent itself is a commodity, but the value for agencies lies in the integration layer: connecting a pre-built agent to a client's CRM, calendar, and review cycle. This framework shifts focus from selecting the 'best' agent to mastering the wiring process. For example, an agency using Vendasta's white-label AI receptionist for a local business must configure it to match the client's booking rules and follow-up cadence, turning a generic tool into a tailored service. As agentic AI adoption grows (77% of decision-makers now run agents in production), clients expect this customization. Agencies that treat agents as components and invest in repeatable wiring processes can charge retainers for ongoing optimization, rather than one-off setup fees.
- Wiring Over WidgetsConcept
The AI agent market sells finished workers, but the strategic value for agencies lies not in the agent itself, which is increasingly a commodity, but in the wiring that connects it to a specific client's CRM, calendar, and review cycle. This framework, 'Wiring Over Widgets,' argues that agencies that treat agents as components rather than products win. The agent is the widget; the wiring is the integration, customization, and ongoing optimization that turns a generic tool into a tailored solution. For example, a white-label platform like Vendasta provides AI employees, but the agency's role is to configure them for each local business's unique lead flow and follow-up process. This wiring is where retainer pricing originates, as it requires ongoing maintenance and adjustment. Recent research shows that 88% of B2B marketers face foundational gaps, meaning clients need help not just deploying agents, but ensuring their operations can support them. Agencies that master the wiring can charge a premium for the irreducible value they add.
- Wiring Premium Over Agent CommodityConcept
Pre-built agents are converging on the same underlying model capability, so the agent itself prices toward zero. What holds value is the wiring: the mapping of a specific client's CRM fields, calendar rules, escalation paths, and review cadence into the agent's loop. Agencies that sell the agent as the product compete on seat price against every reseller of the same worker. Agencies that sell the wiring charge for discovery, field mapping, exception handling, and monthly tuning, which is retainer work. Vendasta's white-label AI workforce and Relevance AI's pre-built sales agents both arrive configured out of the box, which means the configuration is not the moat; the client-specific plumbing is. A practical test: if a competitor could swap your agent vendor next quarter without the client noticing, you sold a commodity. If the swap would break their CRM hygiene, approval chain, or reporting, you sold wiring.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- AI Agents Rule: Wire the Agent, Not the ProductEvaluation Rule
Treat the AI agent as a commodity component and focus your value on the integration into the client's specific workflows, systems, and review processes.
- AI Agents Rule: Wire the Agent, Not the ProductEvaluation Rule
Treat the AI agent as a commodity component and charge for the integration into the client's specific systems and workflows.
- The Productized Agent Trap: Why AI Agent Services Stall Without Client-Specific WiringFailure Pattern
- The Demo-to-Retainer Gap: Why AI Agents Stall After the Pilot CallFailure Pattern
8 modules selected for SLCR
Frequently Asked Questions
Answers about pricing, setup, implementation
SLCR is an open standard and Rust CLI for writing software requirements that coding agents can read in slices and tools can verify. You write requirements in JSON, TOML, or YAML with acceptance criteria, link them via refine/depend relationships, and map each criterion to a validator (test, linter, ast-grep rule, or agent check). Running slcr check reports which criteria are met; you can run it in CI to verify agent-generated code still matches the spec.
SLCR is open-source and free. The reference implementation (Rust CLI) and agent skill are available on GitHub with no licensing fees or per-seat charges.
Technical Leads and Engineering Managers benefit most by reducing agent-code rework through sliced requirements and acceptance-criteria verification. Project Managers gain visibility into task completion without manual code review. Founders and Operations leads see throughput gains when agent context-window waste and error-correction overhead shrink. Developers benefit indirectly by spending less time debugging agent output.
Conservative estimate is 4-6 hours per Technical Lead or Engineering Manager per week on code-review and rework cycles, assuming 5+ agent-assisted tasks per sprint. Savings compound as the team builds muscle memory for spec-driven development; early sprints may show 1-2 hours of savings while the team learns the format and validator setup.
Initial setup (writing your first spec in SLCR format and configuring validators) takes 2-4 hours for a Technical Lead. Full team adoption, including teaching agents the skill and integrating slcr check into CI, typically takes 1-2 sprints. Friction is highest in the first sprint; by sprint 3, the workflow becomes routine.
SLCR integrates with ast-grep for rule-based code validation and GitHub for CI-native verification via Actions. Acceptance criteria can map to any validator you declare (tests, linters, browser tests, or custom agent checks), so integration scope depends on your existing tooling.