AI ToolAI Agents

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.

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.

Situational Fit4.3/10

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.

Situational FitNo WLOpen Source
Seats

5recommended

Est. Hours Saved

90/mo

Net Capacity

No paid plan published

Friction

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.

Situational Fit
Fit43
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Best For Your Team
  • Technical Lead handling agent task specification and handoff
  • Engineering Manager handling acceptance-criteria verification
  • Project Manager handling code-review and rework cycles
Not Ideal If
  • 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

Team Subscription

No paid plan published

Time Saved Monthly

90 hr/mo

5 seats × 18 hr each

Value of Reclaimed Time

$6,750/mo

modeled at $75/hr labor rate

Net Capacity

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 rot

Hand 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

New2026-10-29

Launch 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 SLCR

Pricing

Pricing data not yet available for SLCR.

Reality Check

Trade-offs & Gotchas

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.

Implementation Reality

Moderate effort: standard configuration with some customization needed

Effort: 4/10Time: 4/10

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

  1. 1Create your account and complete setup wizard
  2. 2Configure write software requirements in json, toml, or yaml with acceptance criteria
  3. 3Connect ast-grep
  4. 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.

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Core concepts

The mental model you need to price and scope the work.

  1. 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.

  2. 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.

  3. 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.

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.