Assess tasks before execution to identify transformations eligible to skip LLM calls. Report the eligible share, recommended models for remaining tasks, and estimated upper costs, then save the routing results for later cost reports.
Route tasks through hooks_route, partition by Agent Booster availability, and report Tier 1 bypass utilization with $0 cost
This skill sends task descriptions to hooks_route and separates transformations classified as able to run without LLM calls from tasks routed to an LLM. It counts the Agent Booster candidates, calculates their share, and reports their LLM call cost as $0. For the remaining tasks, it reports recommended models and estimated upper costs based on reference pricing. Results are saved to cost-tracking for later cost reports.
Use it before processing a batch of similar tasks, or when simple code transformations are incurring Sonnet or Opus costs. Examples include changing variable declarations, adding types, and removing logs.
The skill uses ruflo MCP tools and REFERENCE.md for cost calculations. Batches are limited to 50 tasks. Treat the eligible share as a lower bound because the router may miss candidates. The $0 figure applies to processing without LLM calls; it does not mean every task runs free. Upstream speed multiplier claims are unverified.
This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.
AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.
The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 21, 2026. Treat it as a guide and read the original before installing.
Review the instructions the agent will receive before you install.
Wraps mcp__plugin_ruflo-core_ruflo__hooks_route and reports how many tasks the 3-tier router classified as Agent Booster (Tier 1) eligible. Tier 1 bypasses run as WASM transforms — no LLM call, structurally $0 cost.
Before a batch of similar tasks, or when cost-report shows Sonnet/Opus spend on descriptions that look like simple transforms (var-to-const, add-types, add-error-handling, async-await, add-logging, remove-console).
Collect tasks — single arg or recent entries from cost-tracking via memory_search. Cap batch at 50.
Route each — call hooks_route with the description; capture the full response string.
Partition — group by whether the response contains the literal [AGENT_BOOSTER_AVAILABLE] (Tier 1) vs. not (Tier 2/3). Extract [TASK_MODEL_RECOMMENDATION] Use model="X" when present.
Compute spend — Tier 1 partition: $0. Tier 2/3 partition: per-task upper-bound from REFERENCE.md pricing × recommended model.
Report
=== Booster bypass report ===
Tasks analyzed: 50
Tier 1 (booster): 18 (36%) — $0.00
Tier 2/3 (LLM): 32 (64%) — $X.XX (upper-bound)
Booster intents: var-to-const (8), add-types (5), remove-console (5)
Persist — memory_store --namespace cost-tracking --key "booster-route-$(date +%Y%m%d-%H%M%S)" --value '{"tier1": N, "tier2_or_3": M, ...}' so cost-report picks up the tier signal.
[AGENT_BOOSTER_AVAILABLE] fires only when the upstream router populates routeResult.agentBoosterIntent.type (v3/@claude-flow/cli/src/mcp-tools/hooks-tools.ts:1228). The published CLI's semantic-VectorDb path does not always trigger the classifier — treat the partition as a lower bound on Tier 1 eligibility.<1ms latency and 352× faster than LLM. <1ms and $0 are structural; 352× is claimed upstream, not yet verified here. Report what the router actually returns.docs/benchmarks/0002-baseline.md for the full upstream-claims-vs-measured table.ADR-0002 Decision #1 · ruflo-intelligence ADR-0001 §"Neutral" (closes the routing-outcomes loop via cost-optimize step 8) · CLAUDE.md root §"3-Tier Model Routing (ADR-026)".
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Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Create an Architecture Decision Record, a document for recording important design choices, with the next sequential number. It generates a Markdown template, registers the record in AgentDB, and links related decisions found through search.
Build a searchable index and relationship graph from ADRs, records of architectural decisions. Import two supported document formats, refresh existing metadata without duplicate relationships, and review summaries of statuses, missing references, and inconsistencies.
Rebuild the index of ADRs, records of architectural decisions, after files or relationship lines are deleted. It clears stored records and relationships, indexes the files still present, and checks that the final record count matches.
Review code changes against accepted Architecture Decision Records, documents that capture agreed design choices. Report violations, outdated references, compliant changes, and areas without ADR coverage, then suggest code fixes or a new decision record where appropriate.
Check saved architecture decision records (ADRs) for broken references, circular replacement relationships, and inconsistent statuses. Use the results to verify imported records or stop a CI check when selected integrity rules fail.