feat: display Big O notation in CLI output (#263)

* feat: display Big O notation in CLI for complexity and overhead

Use asymptotic_normal_form to show simplified Big O notation alongside
exact formulas in `pred show` (variant complexity + reduction overhead)
and `pred path` (step overhead + composed overall). Also included in
JSON output as a `big_o` field.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: show only Big O notation, remove raw formulas, DRY helpers

Replace inline overhead display with shared big_o_of(), fmt_overhead_parts(),
and overhead_to_json() helpers. Display only asymptotic notation (no constant
factors) for complexity, reduction overhead, and path overhead.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: always wrap Big O fallback in O(...) for consistent output

When asymptotic_normal_form fails (e.g., variable^variable like
num_vars^num_vars), the fallback now wraps the raw expression in O(...)
instead of displaying it bare. This ensures consistent format for both
human and JSON output.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* normal form

* save

* feat: redesign asymptotic normal form into two-phase pipeline

Split the monolithic asymptotic_normal_form() into two explicit phases:
- canonical_form(): exact symbolic simplification with signed coefficients,
  term merging, polynomial expansion, and transcendental identities
- big_o_normal_form(): asymptotic projection that drops dominated terms
  and constant factors

Also adds:
- pred-sym CLI binary for debugging the symbolic engine
- Display Pow(x, 0.5) as sqrt(x) for cleaner output
- Variable^constant now supports fractional exponents in vars map
  (enables n * n^0.5 → n^1.5 merging)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>

Author: GiggleLiu

.gitignore

@@ -86,3 +86,4 @@ claude-output.log
 *.json
 .claude/worktrees/
 docs/test-reports/
+docs/superpowers/

docs/agent-profiles/FEATURES.md

@@ -0,0 +1,10 @@
+# Features
+
+- [Library API] — Define NP-hard problems, evaluate configurations, and reduce between problem types using Rust traits
+- [CLI Tool (pred)] — Explore the reduction graph, create/inspect/reduce/solve problem instances from the terminal
+- [MCP Server] — AI assistant integration via Model Context Protocol for graph queries and problem manipulation
+- [Reduction Graph] — Automatic shortest-path search through registered reductions between problem types
+- [BruteForce Solver] — Enumerate all configurations to find optimal or satisfying solutions
+- [Variant System] — Graph/weight type parameterization with compile-time complexity registration
+- [Overhead System] — Symbolic expressions describing how target problem size relates to source after reduction
+- [Serialization] — JSON schema export and serde-based serialization for all problem types

docs/agent-profiles/SKILLS.md

@@ -0,0 +1,13 @@
+# Skills
+
+- [issue-to-pr] — Convert a GitHub issue into a PR with an implementation plan
+- [add-model] — Add a new problem model to the codebase
+- [add-rule] — Add a new reduction rule to the codebase
+- [review-implementation] — Review implementation completeness via parallel subagents
+- [fix-pr] — Resolve PR review comments, CI failures, and coverage gaps
+- [check-issue] — Quality gate for Rule and Model GitHub issues
+- [check-rule-redundancy] — Check if a reduction rule is redundant via composite paths
+- [write-model-in-paper] — Write or improve a problem-def entry in the Typst paper
+- [write-rule-in-paper] — Write or improve a reduction-rule entry in the Typst paper
+- [release] — Create a new crate release with version bump
+- [meta-power] — Batch-resolve all open Model and Rule issues autonomously

docs/agent-profiles/cli-tool-dr-sarah-chen.md

@@ -0,0 +1,24 @@
+# cli-tool-dr-sarah-chen
+
+## Target
+CLI Tool (pred)
+
+## Use Case
+End-to-end workflow: create an MIS instance, reduce it to QUBO, solve the QUBO, and verify the mapped-back solution matches the known optimum.
+
+## Expected Outcome
+The full create → reduce → solve → evaluate pipeline completes successfully with correct optimal results. JSON output is well-formed and machine-readable at every stage.
+
+## Agent
+
+### Background
+Dr. Sarah Chen is a computational physicist at a national lab who regularly uses optimization solvers (Gurobi, CPLEX, HiGHS) in her research on quantum annealing benchmarks. She evaluates new tools by running them against problems with known optimal solutions.
+
+### Experience Level
+Expert
+
+### Decision Tendencies
+Tests edge cases proactively — tries empty graphs, disconnected components, and large instances. Expects precise, actionable error messages when things go wrong. Compares solver output against independently computed optima. Will read --help but also try undocumented flag combinations.
+
+### Quirks
+Will try both file-based and piped workflows to check consistency. Inspects JSON output with jq for machine-readability. Gets impatient with vague error messages like "invalid input" — wants to know exactly what's wrong and where.