| name | wisdom |
| description | Build and query domain-specific wisdom frames — crystallised patterns from accumulated experience. Use to capture high-confidence principles or to consult accumulated wisdom before making decisions. |
| argument-hint | [observation to record, domain to query, or 'synthesize' to find cross-domain patterns] |
| user-invocable | true |
| allowed-tools | Read, Write, Edit, Bash, Glob, Grep |
Manage wisdom frames — domain-specific collections of crystallised behavioral knowledge. Use $ARGUMENTS to record observations, query a domain, or synthesise cross-domain patterns.
Wisdom frames are fed by the /learning skill — individual learnings accumulate, and when patterns emerge (5+ learnings in a category), they crystallise into wisdom frame principles.
What is a Wisdom Frame?
A wisdom frame is a living document for a specific domain (e.g., communication, development, deployment, architecture) that accumulates:
- Core principles: High-confidence patterns verified through repeated observation
- Contextual rules: Rules that apply in specific situations
- Predictive model: "When X happens, Y is usually the right response"
- Anti-patterns: Things that consistently go wrong, with root causes
- Cross-domain connections: Links to related domains
Each principle has a confidence level based on how many times it's been observed and verified. Principles above 85% confidence are considered crystallised — reliable enough to act on without re-verification.
Recording an Observation
When you notice a pattern worth capturing:
1. Classify the domain
Common domains: communication, development, deployment, architecture, content-creation, testing, debugging, design
2. Classify the observation type
- principle: A high-confidence pattern ("When writing PRs, leading with the why gets faster reviews")
- contextual-rule: A rule for specific situations ("In monorepos, always run full CI not just the changed project")
- prediction: A request→response pattern ("When the user says 'clean up', they mean minimal targeted changes, not refactoring")
- anti-pattern: Something that consistently fails ("Mocking the database in tests masked a real migration bug")
3. Write or update the frame
Store wisdom frames in the project's memory directory as wisdom-{domain}.md:
---
name: wisdom-development
description: Development domain wisdom — crystallised patterns for writing and shipping code
type: reference
---
## Core Principles
- [CRYSTAL 92%] Surgical fixes only — never rearchitect as a fix (observed: 12 times)
- [CRYSTAL 87%] Read before modifying — understand existing patterns first (observed: 8 times)
- [68%] One change when debugging — isolate before expanding scope (observed: 4 times)
## Contextual Rules
- In monorepos: run full CI across all projects before pushing
- When touching API endpoints: verify both unit and integration tests
## Predictive Model
| Request pattern | Likely intent | Right response |
|---|---|---|
| "clean up" | Minimal targeted fix | Small diff, no refactoring |
| "make it better" | Improve specific quality | Ask which dimension |
## Anti-Patterns
- **Mocking everything** (severity: high) — masks real integration failures. Root cause: speed over correctness
- **Batch verification** (severity: medium) — marking criteria complete at the end. Root cause: wanting to "just finish"
## Evolution Log
- 2026-04-01: Added "surgical fixes" principle (source: repeated correction)
Querying Wisdom
When starting work in a domain, query the relevant wisdom frame:
- Identify which domains are relevant to the current task
- Load the wisdom frames for those domains
- Surface any principles or anti-patterns that apply
- Flag predictions that match the current request pattern
Cross-Domain Synthesis
When asked to synthesise, or when wisdom frames have grown substantially:
- Scan all frames for principles that appear in 2+ domains
- Identify anti-patterns that recur across domains
- Look for predictions that generalise
- Present cross-domain principles sorted by confidence and occurrence count
Cross-domain principles are especially valuable — they represent fundamental truths about how work should be done, not domain-specific quirks.
Frame Health
Assess each frame periodically:
- Growing: Updated within 7 days, 10+ observations — actively learning
- Stable: Updated within 30 days — mature and reliable
- Stale: Not updated in 30+ days — may need review or retirement
Rules
- Confidence must be earned. A new observation starts at 50%. It takes 5+ consistent observations to reach crystallised (85%+). Never assign high confidence to a single data point.
- Anti-patterns need root causes. "Don't do X" without explaining why is a rule, not wisdom. Always include the root cause: "Don't mock the database because it masks real integration failures."
- Update, don't duplicate. When a new observation matches an existing principle, increment its observation count and confidence — don't create a new entry.
- Cross-domain principles are the most valuable. A principle that appears in 3+ domains is a fundamental truth. Flag and promote these.
- Stale frames are worse than no frames. A wisdom frame that hasn't been updated in 30+ days may be actively misleading. Review or archive.
- Predictions must be falsifiable. "When X happens, Y usually follows" must specify what "usually" means (e.g., 8 of 10 times). Vague predictions are superstition.
Output Format
When recording:
## Wisdom Updated: [domain]
**Observation type:** [principle/contextual-rule/prediction/anti-pattern]
**Added:** "[observation text]"
**Confidence:** [N]% (based on [M] observations)
**Frame status:** [Growing/Stable]
**Saved to:** [file path]
When querying:
## Wisdom: [domain]
### Applicable Principles (sorted by confidence)
| Confidence | Principle | Observations |
|---|---|---|
| [N]% | [principle] | [count] |
### Relevant Anti-Patterns
- [anti-pattern] — root cause: [cause]
### Predictions for Current Context
| Pattern | Likely Response | Confidence |
|---|---|---|
| [request pattern] | [right response] | [N]% |
When synthesising:
## Cross-Domain Synthesis
### Universal Principles (appear in 2+ domains)
| Principle | Domains | Avg Confidence |
|---|---|---|
| [principle] | [domain1, domain2] | [N]% |
### Recurring Anti-Patterns
| Anti-Pattern | Domains | Root Cause |
|---|---|---|
| [pattern] | [domains] | [common cause] |
Related Skills
/learning — individual learnings feed into wisdom frames. When 5+ learnings form a pattern, crystallise them here./health-check — audits wisdom frame health as part of the project health report.
