Use case experiments
Where judgment lives and compounds.
Cognition is not a search index. It is a spectral-adaptive memory system that captures the moment of resolution, keeps the author and their reasoning, tracks freshness over time, and routes every future agent to executable judgment, not a pile of notes.
The five-step proof loop
Capture
A real workflow, decision, or lesson is observed, from a coding session, a call, a postmortem. Cognition drafts a structured skill: trigger, steps, checks, why it works.
Approve
A human reviews the draft before it reaches the team brain. Unapproved drafts stay in the author's personal workspace. Nothing leaks to teammates without an explicit yes.
Retrieve
When a future agent encounters a matching trigger, it loads the skill before reasoning. Not a search result, an executable context with steps, checks, and provenance.
Apply
The agent uses the memory in the actual task. Attribution is preserved, it knows whose judgment it's applying and when that judgment was last confirmed.
Report
Outcome receipt: did the skill help? This signal feeds back into the retention model. Skills with more confirmation drift slower. Skills used once decay faster.
What makes this different
Not a search index. A retention system with provable bounds.
Spectral retention
Decay derived from operator theory on your knowledge graph, not a fitted average.
Confirm-first capture
Skills save only on a human yes and reach the team only on proven reuse. No auto-indexed noise.
Executable judgment
Structured skills with steps, checks, and provenance. Not a similarity match.
Scoped, revocable keys
Personal and org-scoped access. Nothing crosses orgs without consent.
Use case → workflow
Where your team’s judgment lives, and how to make it retrievable.
Seven workflows, each mapped to its real use cases, the skill patterns that fire, and a prompt you can drop into your agent right now.
Engineering
Turn tribal knowledge into team infrastructure.
The best engineers on your team are debugging the same classes of bugs every month. Cognition captures the fix once, attributes it to the person who found it, and routes every future agent to it before they guess.
Recurring build failures
Agent sees a Vercel/CI error it hasn't seen before
Fix retrieved in 4s. Previously took 45 min of bisecting.
Auth edge cases
OAuth callback race condition on mobile
Two engineers avoided re-diagnosing; one blocked PR caught pre-merge.
Code review taste
PR opens touching the data layer
New teammates review like seniors. Taste travels with the repo.
Flaky test diagnoses
CI fails inconsistently on timing-sensitive tests
Test suite green. Root cause documented, not rediscovered.
Turn what we just did into a Cognition skill with: When to fire, Steps, Checks, and Why it works. Only include reusable workflow knowledge. Do not include secrets, raw logs, or one-off details.
Deploy & Ops
Recover faster. Stop relearning the same incidents.
Production incidents produce the most valuable institutional knowledge and the least durable documentation. Cognition converts postmortem insight into agent-ready retrieval before the next incident.
Deploy recovery sequences
Deployment fails in production
MTTR down. Junior engineers run recovery without escalation.
Env variable scoping
Build passes locally, fails in CI
Environment mismatch caught before deploy, not after.
Rollback decision criteria
Error rate > 2% for 5 minutes
Decision made without a war room. Pattern established from 3 incidents.
Incident retrospectives
Post-incident review complete
Postmortem knowledge survives the engineer who wrote it.
Use Cognition as the orchestrator for this incident or deploy goal. Create a run plan with: sensor layer, policy layer, skill memory assignments, tool assignments, quality gates, and learning-loop receipts. Ask before external side effects and record execution receipts as you work.
Onboarding
First agent install → productive in hours, not weeks.
Onboarding is a lossy process. The knowledge that made your team fast lives in Slack history and senior engineers' heads. Cognition makes it retrievable from day one.
Repository conventions
New engineer opens any file
Day-1 PRs don't need style corrections. Code review is about logic, not formatting.
Tool and access setup
First day, new machine
Setup takes 2h instead of 2 days. IT tickets drop 80% in first week.
Who owns what
Bug in unfamiliar part of codebase
New engineers don't interrupt the wrong person. Response time drops.
Codebase gotchas
Touching a legacy service
Breaking changes caught before they're written, not in review.
Look at the project or workflow context you can see. Find 5 places where new teammates repeat work, rediscover decisions, or rely on knowledge in a senior's head. For each one, say what Cognition should capture, when a future agent should retrieve it, and how we would know it helped.
Research & Knowledge
Orchestrate context retrieval without pretending to replace search.
Large wikis and Obsidian vaults fail when agents retrieve the wrong cluster or trust stale context. Cognition remembers the query strategy, source boundary, and whether the retrieved context actually changed the answer.
Obsidian / wiki retrieval
Agent needs context from a large personal or team knowledge base
Better retrieval path and auditable source boundary; source search still performs raw recall.
RAG comparison
Team asks whether Cognition improves retrieval
Clear evidence without overclaiming. Cognition is measured as orchestration, not a vector database replacement.
Resume tailoring workflow
User repeatedly adapts a resume for roles
Reusable skill development. Cognition should not claim to magically improve the resume without user examples and feedback.
Use Cognition to evaluate this wiki retrieval workflow. Freeze 10 questions, list expected source notes, run baseline retrieval, run Cognition-guided retrieval, then compare source-hit rate, answer correctness, token spend, latency, and whether each result changed the final answer.
Decisions & Product
Never relitigate an architectural choice.
Engineering teams make 5–10 significant architectural decisions per quarter. Within 18 months, the people who made them have left, been promoted, or forgotten the context. Cognition preserves the reasoning, not just the outcome.
Architecture decisions
Similar problem surfaces again
Decision re-evaluated consciously, not re-made from scratch.
Rejected feature directions
Someone proposes a feature that was killed
Teams don't waste sprints re-building rejected work. Context survives the team.
Customer commitments
Renewal call or feature request
No missed commitments. Agents surface the right constraint before a call.
Competitive positioning
Competitor comes up in a call
Every rep sounds like the founder. No outdated battlecards.
Look at this plan and check whether it conflicts with any prior decision, product direction, architecture choice, or customer promise we should remember. Tell me whether to follow, revisit, or explicitly override the decision, and what Cognition skill should capture the reasoning either way.
GTM & Sales
Objection memory that compounds across every rep.
The best sales call responses die in call recordings. Cognition captures what landed, attributes it to who discovered it, and surfaces it when the next rep faces the same objection.
Objection handling
Prospect raises the 'we already use CLAUDE.md' objection
Objection converted to differentiation. 3 reps using the same frame.
Account-specific context
Call with target account
Every call sounds like a warm, informed conversation. No briefing docs.
Pricing rationale
Prospect asks about pricing logic
Deals close faster. Pricing feels fair because the outcome is defined.
Look at the last 5 deals we lost or stalled. What objection, context gap, or knowledge failure caused each one? For each, say what Cognition should have captured, when a future agent should retrieve it, and what outcome would prove it helped.
Education & Research
Spectral retention that neuroscience validates.
Cognition is the first learning system to derive forgetting curves from operator theory, not fit them to empirical averages. In educational settings, this means precision-targeted review at the spectral gap of the learner's knowledge graph.
Adaptive spaced review
Learner completes a concept module
Cabarrus County pilot: 32K students. Review intervals derived per-concept, not per-student average.
Knowledge gap detection
Agent sees learner stall on a problem
Intervention at the right layer, not a guess. Failure attribution is mathematical, not heuristic.
Curriculum sequencing
Designing a new learning path
Sequencing grounded in graph topology, not pedagogical convention.
Explain what context you are using for this learning or research task. Separate it into: learner or researcher request, concept graph context, prior assessment or experiment outputs, Cognition memory, and hidden curriculum context. Then tell me which pieces should be pinned, refreshed, or flagged for spectral-adaptive review.
Why Cognition is different
Memory that decays, consolidates, and teaches back.
Cognition treats work as a learning signal. Skills carry freshness and outcome history, so stale guidance asks first instead of misleading the next agent.
// spectral retention: decay from the graph Laplacian export function retrievability( node: SkillNode, now = Date.now(),): number { const S = 1 / spectralGap(node.L) // S = 1/λ₁(L) const dt = (now - node.reviewedAt) / DAY return Math.exp(-(dt / S) ** node.beta)} // weakest layer bottlenecks the whole manifoldconst spectralGap = (L: Laplacian) => Math.min(...L.layers.map(eigGap))// spectral retention: decay from the graph Laplacian export function retrievability( node: SkillNode, now = Date.now(),): number { const S = 1 / spectralGap(node.L) // S = 1/λ₁(L) const dt = (now - node.reviewedAt) / DAY return Math.exp(-(dt / S) ** node.beta)} // weakest layer bottlenecks the whole manifoldconst spectralGap = (L: Laplacian) => Math.min(...L.layers.map(eigGap))Prompts, files, tool calls, decisions, and outcomes become typed learning events.
Shortest path to value
The second agent is better because the first one learned.
Start with one real workflow. Capture the skill, retrieve it in the next session, apply it, report whether it helped. That is the full loop.