Skip to content
cccskills
FreePublished on GitHub

product-capability

Turn product requests and planning documents into a concrete development plan. Document business rules, data ownership, service interfaces, and unresolved decisions so teams can see what must be settled before implementation begins.

OriginalShow the original description

Translate PRD intent, roadmap asks, or product discussions into an implementation-ready capability plan that exposes constraints, invariants, interfaces, and unresolved decisions before multi-service work starts. Use when the user needs an ECC-native PRD-to-SRS lane instead of vague planning prose.

See how to install

Good for

  • Turning a product requirements document into development requirements
  • Aligning requirements for a feature spanning multiple services
  • Documenting assumptions that repeatedly surface in reviews
  • Identifying decisions needed before development starts

Files included (1)

  • SKILL.md4.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Product Capability

This skill turns product intent into explicit engineering constraints.

Use it when the gap is not "what should we build?" but "what exactly must be true before implementation starts?"

When to Use

  • A PRD, roadmap item, discussion, or founder note exists, but the implementation constraints are still implicit
  • A feature crosses multiple services, repos, or teams and needs a capability contract before coding
  • Product intent is clear, but architecture, data, lifecycle, or policy implications are still fuzzy
  • Senior engineers keep restating the same hidden assumptions during review
  • You need a reusable artifact that can survive across harnesses and sessions

Canonical Artifact

If the repo has a durable product-context file such as PRODUCT.md, docs/product/, or a program-spec directory, update it there.

If no capability manifest exists yet, create one using the template at:

  • docs/examples/product-capability-template.md

The goal is not to create another planning stack. The goal is to make hidden capability constraints durable and reusable.

Non-Negotiable Rules

  • Do not invent product truth. Mark unresolved questions explicitly.
  • Separate user-visible promises from implementation details.
  • Call out what is fixed policy, what is architecture preference, and what is still open.
  • If the request conflicts with existing repo constraints, say so clearly instead of smoothing it over.
  • Prefer one reusable capability artifact over scattered ad hoc notes.

Inputs

Read only what is needed:

  1. Product intent
    • issue, discussion, PRD, roadmap note, founder message
  2. Current architecture
    • relevant repo docs, contracts, schemas, routes, existing workflows
  3. Existing capability context
    • PRODUCT.md, design docs, RFCs, migration notes, operating-model docs
  4. Delivery constraints
    • auth, billing, compliance, rollout, backwards compatibility, performance, review policy

Core Workflow

1. Restate the capability

Compress the ask into one precise statement:

  • who the user or operator is
  • what new capability exists after this ships
  • what outcome changes because of it

If this statement is weak, the implementation will drift.

2. Resolve capability constraints

Extract the constraints that must hold before implementation:

  • business rules
  • scope boundaries
  • invariants
  • trust boundaries
  • data ownership
  • lifecycle transitions
  • rollout / migration requirements
  • failure and recovery expectations

These are the things that often live only in senior-engineer memory.

3. Define the implementation-facing contract

Produce an SRS-style capability plan with:

  • capability summary
  • explicit non-goals
  • actors and surfaces
  • required states and transitions
  • interfaces / inputs / outputs
  • data model implications
  • security / billing / policy constraints
  • observability and operator requirements
  • open questions blocking implementation

4. Translate into execution

End with the exact handoff:

  • ready for direct implementation
  • needs architecture review first
  • needs product clarification first

If useful, point to the next ECC-native lane:

  • project-flow-ops
  • workspace-surface-audit
  • api-connector-builder
  • dashboard-builder
  • tdd-workflow
  • verification-loop

Output Format

Return the result in this order:

CAPABILITY
- one-paragraph restatement

CONSTRAINTS
- fixed rules, invariants, and boundaries

IMPLEMENTATION CONTRACT
- actors
- surfaces
- states and transitions
- interface/data implications

NON-GOALS
- what this lane explicitly does not own

OPEN QUESTIONS
- blockers or product decisions still required

HANDOFF
- what should happen next and which ECC lane should take it

Good Outcomes

  • Product intent is now concrete enough to implement without rediscovering hidden constraints mid-PR.
  • Engineering review has a durable artifact instead of relying on memory or Slack context.
  • The resulting plan is reusable across Claude Code, Codex, Cursor, OpenCode, and ECC 2.0 planning surfaces.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

accessibility

FreeSummary

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC283.3KUpdated Oct 16, 2026

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC283.3KUpdated Oct 16, 2026

agent-eval

FreeSummary

Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.

  • Comparing coding agents on real development tasks
  • Evaluating a new tool or model before adoption
  • Checking performance after agent updates
affaan-m/ECC283.3KUpdated Oct 16, 2026

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC283.3KUpdated Oct 16, 2026

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

  • Diagnosing repeated tool calls with no progress
  • Investigating quality loss as context grows
  • Checking file paths and environment mismatches
affaan-m/ECC283.3KUpdated Oct 16, 2026

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC283.3KUpdated Oct 5, 2026

See all skills by affaan-m

Report a problem with this skill