Product context for humans and agents
Capture what the product should do, then use codemix to guide tasks, agent briefs, and PR reviews. Evolve the model over time to evolve the product itself.
Keep your existing stack. codemix provides the context layer.
The problem
Speed is not the problem. Shared understanding is. When context is fragmented, humans and agents make different decisions and behavior drifts from product intent.
The fix
codemix unifies specs, decisions, and rules so humans and agents build against the same source of truth.
One context. Fewer wrong builds.
Write in markdown in any human language.
Model features, flows, rules, constraints, and decisions.
Stay high-level where you can, get precise where it matters.
Generate semantic tasks and scoped agent briefs tied to meaning.
Check PRs against intent, not just syntax.
One source of truth
Generate implementation-ready briefs for any coding agent.
Create issue-ready tasks for Linear, Jira, or GitHub Issues.
Keep specs queryable, current, and connected to decisions.
Get clear reasoning when behavior diverges from intent.
Progressive detail
Start with intent. Add constraints as decisions become real. codemix supports how product teams actually work: iterative, not all-or-nothing.
# Invite teammates
- Team admins can invite teammates by email
- Only admins can invite
- Invite expires after 7 days
- If user already exists, add directly (no invite email)## Edge cases
- Blocked domain -> show "Domain not allowed"
- Existing access -> show "Already a member"
- Expired invite -> show "Invite expired" + resend actionNatural-language specs
Teams write specs in the language they naturally use. codemix treats that natural-language spec as authoritative product context for tasks, agent briefs, and verification.
# Flujo: Restablecer contraseña
- El usuario solicita restablecimiento por correo
- El enlace expira en 15 minutos
- Si el correo no existe, mostrar el mismo mensaje (no filtrar usuarios)How codemix works
Connect Notion, Confluence, Linear, Jira, support signals, and GitHub so codemix pulls only the slices that matter.
codemix asks the questions strong teams ask early:
Generate semantic tasks, scoped agent briefs, and acceptance criteria tied to declared rules.
Review changed behavior against relevant model boundaries, explain conflicts, and propose spec updates for approval when product reality changed.
Why this is different from docs + AI
codemix keeps context authoritative and operational: a shared model with provenance, governance, and verification.
This is not documentation polish. It is build-time alignment.
Who gets value immediately
Explore changes without re-explaining the product on every handoff.
Give agents clear intent instead of vague prompts.
Design against explicit constraints and compatibility rules with less drift.
Query current truth instead of stale pages.
Integrates with your stack, no migration required
Inputs
Notion, Confluence, Linear, Jira, support tools
Outputs
Linear/Jira/GitHub Issues, PR comments, agent briefs
Execution
Cursor, Claude Code, Codex, Copilot (agent-agnostic)
FAQ
No. codemix does not stop at writing intent. It compiles and verifies intent against code changes.
No. codemix is built for progressive detail. Specify what matters now, then refine over time.
codemix is diff- and intent-slice aware. It checks changed behavior against relevant model boundaries.
codemix proposes an intent update for human approval, so model and code stay aligned.
No. codemix connects your tools and keeps their outputs behaviorally consistent.
codemix gives agents and humans one authoritative product context.