Analyzes Claude session data for behavioral dimensions, extracts strengths and their structural costs (shadows), and generates attitude principles with practice matrix reports.
From epistemic-cooperativenpx claudepluginhub jongwony/epistemic-protocols --plugin epistemic-cooperativeThis skill uses the workspace's default tool permissions.
references/report-template.mdEnables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Designs and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
Discover the structural costs hidden in your strengths.
Every strength casts a shadow. The shadow is not a flaw — it is the structural cost of a capability. Understanding the cost transforms a curse into a conscious trade-off.
Invoke this skill when:
Skip when:
| Phase | What | Mode |
|---|---|---|
| 1. Collect | Gather behavioral data | dimension-profiler agent |
| 2. Analyze | Strength-Shadow extraction | AI + user dialogue |
| 3. Recommend | Attitude principles + practice matrix | AI proposes |
| 4. Report | Generate HTML report | Automated |
If the user provides a specific question (e.g., "What are my curses?"), orient the analysis toward that question.
Same-session reuse: If dimension-profiler output is already available in this
conversation (from a prior /sophia or /curses run), skip Phase 1 entirely and
reuse that output. Both skills produce identical profiler results.
Two-step delegation (same pipeline as /sophia):
Step 1: Run coverage-scanner agent (see agents/coverage-scanner.md) to get
pre-aggregated session data (protocol counts, friction, session types, tools).
Step 2: Pass coverage output to dimension-profiler agent (see agents/dimension-profiler.md):
Analyze this user's behavioral dimensions from their session data.
coverage_data: [paste coverage-scanner output here]
data_sources:
rules_dir: ~/.claude/rules/
claude_md: ~/.claude/CLAUDE.md
settings_json: ~/.claude/settings.json
data_context: session-enriched
Return the dimension profile table with scores, confidence, and raw signals.
When coverage_data is provided, omit sample_size — the profiler derives
dimensions from aggregate data and does not sample raw files.
If a dimension's confidence is "low", include it in the analysis but mark it as provisional and note this in the report.
From the dimension profile, identify strengths and their structural costs.
For each dimension scoring above 65 (or below 35 — extremes in either direction):
These are heuristic starting points, not fixed outputs. Adapt based on actual data.
| Dimension extreme | Strength | Shadow |
|---|---|---|
| D2 high (Doubt) | Catches errors early | Verification depth becomes opportunity cost |
| D4 high (Systematic) | Consistent governance | Rule accumulation creates complexity |
| D5 high (UU) | Discovers new patterns | May defer KK maintenance |
| D6 high (Extended Mind) | Effective delegation | Curse activates on delegation failure |
| D1 high (Abductive) | Creative hypothesis | May skip systematic validation |
| D3 high (Dialogical) | Deep understanding | Extended exchanges consume time |
Look for patterns that emerge from dimension COMBINATIONS:
When presenting strength-shadow pairs, some combinations may be either a curse OR a deliberate strategy. In context-rich sessions, the user may have already articulated this distinction. In cold-start sessions, the AI must proactively surface both interpretations before the user validates.
Patterns that require dual-interpretation:
When presenting these, frame as: "This pattern admits two readings — [curse interpretation] or [strategy interpretation]. Which better describes your experience?" The user's response determines downstream recommendations.
When presenting dimensions to the user, always include the human-readable explanation from the dimension-profiler output (e.g., "D4 Rule Orientation — how you govern work") so users unfamiliar with the framework understand what each dimension measures.
Present the top 3-4 strength-shadow pairs and ask the user to validate. The user may:
User counter-evidence that changes the structural category (e.g., flaw to strategy) requires re-derivation of downstream recommendations.
From validated strength-shadow pairs, derive:
Each principle addresses a specific shadow:
Principle N — [Title]
[2-3 sentence explanation of the principle and why it addresses this shadow]
Application:
[Concrete, actionable guidance for daily practice]
Rank by ROI — which principle would have the highest impact if adopted? Mark the highest-ROI principle explicitly.
Map principles to concrete situations:
| Situation | Principle | Action | Trigger |
|---|---|---|---|
| When X happens | Principle N | Do Y | Z condition |
Include 4-6 rows covering the most common situations.
Generate an HTML report following the cooperative's design system.
Read one of:
~/.claude/usage-data/report.html — extract CSSskills/report/references/html-template.md — use as template basisskills/curses/references/report-template.md — curses-specific componentsCheck the dimension-profiler's Data Context field:
Mark the report subtitle with the context tier (e.g., "708 sessions | data-only" or "708 sessions | session-enriched").
If the dimension profile and philosopher match are available from a prior
/sophia run in this session, include:
Save to ~/.claude/usage-data/curses-profile.html
Open in browser: open <filepath>