fabioc-aloha Token Waste Elimination
Reduce active context without weakening behavior. Token cost matters only when the content is loaded; optimize high-frequency surfaces first and preserve specialized detail in on-demand skill resources.
Measure Before Editing
Run your project's brain-QA muscles first (Alex ACT ships two — scripts/brain-qa.cjs for structural checks + scripts/brain-semantic-qa.cjs for semantic checks):
node scripts/brain-qa.cjs --json
node scripts/brain-semantic-qa.cjs --json
If your project doesn't ship equivalents, measure by listing active applyTo: "**" and applyTo: "**/*" instruction files and their line counts by hand.
Then measure active instructions by scope and line count. Treat applyTo: "**"
and applyTo: "**/*" as the highest-frequency group. Skill and prompt bodies
are on-demand; their metadata is always discoverable, but their full bodies do
not compete with every turn.
Prioritize Waste
| Signal | Action |
|---|---|
| Two active artifacts give the same rule | Keep one owner; make the other a routing pointer or remove it |
| Instruction carries a long procedure | Move procedure to a skill; retain condition and action only |
| Prompt repeats its skill body | Reduce prompt to invocation, branches, and output contract |
| Hardcoded count or version in living guidance | Derive it or remove it |
| Active artifact references absent paths/tools | Repair or retire; warnings do not make unsafe behavior inert |
| Skill exceeds 500 lines | Move reference material to one-level-deep resources or split responsibility |
| Historical rationale dominates active rule | Move history to ADR/ledger; keep current behavior in the brain |
Protect Load-Bearing Content
Do not remove content merely because it is long. Keep content that:
- changes decisions under realistic conditions
- establishes a safety or privacy boundary
- supplies examples needed to avoid recurring failures
- records the only current contract for a workflow
Before trimming, name the behavior the text protects and the executable check that would expose accidental loss. If neither can be named, the text is a strong removal candidate.
Workflow
Capture the structural and semantic QA baseline.
Rank candidates by load frequency, duplication, and stale-contract risk.
Change one ownership cluster at a time.
Rerun the same focused checks after each cluster.
Run the full brain gate before finishing (if your project ships one; Alex ACT uses):
node scripts/brain-qa.cjs node scripts/brain-semantic-qa.cjs node --test scripts/test-brain-semantic-qa.cjs scripts/test-coherence-check.cjs scripts/test-fleet-inventory.cjsReport artifact-count and high-frequency-line deltas. Do not claim token savings unless measured by the actual loader or a documented approximation.
Anti-Patterns
| Anti-pattern | Correction |
|---|---|
| Trimming every file to one arbitrary line ceiling | Optimize by load frequency and behavior value |
| Treating skill bodies as always-loaded | Distinguish discovery metadata from on-demand bodies |
| Removing examples without a regression check | Preserve examples that encode known failure prevention |
| Moving prose to a file that the skill always reads | That relocates cost; it does not reduce it |
| Reporting word counts as exact model tokens | Label estimates and name the tokenizer when used |
Would Revise If
Revisit by 2026-10-28 or sooner if loader semantics change, a trimming pass removes behavior that later regresses, the semantic muscle misses a stale contract class it declares, or measured context cost does not improve after a pass that claimed savings.