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Cost Counterfactual

Agent Building
cost-counterfactual

Cost Counterfactual is an Agent Skill for agent building published in the ruvnet/ruflo repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Multi-baseline counterfactual cost analysis. Compares actual session spend to hypothetical always-haiku / always-sonnet / always-opus routing baselines. Answers "is the routing earning its keep?" Negative savings flag over-escalation; positive savings quantify the router's win.

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How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/ruvnet/ruflo/blob/HEAD/plugins/ruflo-cost-tracker/skills/cost-counterfactual/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/cost-counterfactual, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

Multi-baseline counterfactual cost analysis. Pairs with the existing observability surface:

  • cost-budget-check — "have we crossed a threshold?" (reactive)
  • cost-projection — "when will we cross a threshold?" (predictive)
  • cost-counterfactual — "is the routing earning its keep?" (comparative) ← this one

Algorithm

  1. Read all session-* records from the cost-tracking namespace.
  2. Apply --since window filter (default all-time).
  3. Sum tokens across byModel[*] entries for each session.
  4. For each requested baseline (default: all three):
    • counterfactualUsd = (input × tier.input + output × tier.output + cache_write × tier.cache_write + cache_read × tier.cache_read) / 1M
  5. Compute savings = counterfactualUsd − actualUsd.
  6. Emit per-baseline totals + savings % across the comparison set.

Smoke transcript (2 sessions: 50K haiku tokens + 50K sonnet tokens)

| Sessions considered | 2 |
| Total input tokens  | 100,000 |
| Actual spend        | $0.162500 |

| Baseline           | Hypothetical | Actual    | Savings    | %       |
| `always-haiku`     | $0.025000    | $0.162500 | -$0.137500 | -550.00% |
| `always-sonnet`    | $0.300000    | $0.162500 | +$0.137500 |   45.83% |
| `always-opus`      | $1.500000    | $0.162500 | +$1.337500 |   89.17% |

How to read negative savings

A negative always-haiku result means the router chose more-expensive models than haiku on tasks haiku could have handled. That's an over-escalation signal:

  • Maybe qualityBar is set too high
  • Maybe the sonnet/opus session was warranted by complexity but the baseline doesn't know that
  • Run cost optimize (or inspect specific sessions via cost conversation) to investigate

Positive savings quantify the router's win against that baseline. The most informative number is usually always-sonnet — it's the standard "safe default" baseline most teams would pick if they didn't have routing.

When to use

  • Quarterly cost review: "We saved $X vs always-Sonnet — here's the proof."
  • CI gate: cost counterfactual --format json | jq '.baselines[1].savingsPct > 30' — fail builds if routing isn't saving ≥30% vs sonnet baseline (workload-shift detector).
  • Routing-config validation: When introducing a new qualityBar or cost-ceiling, re-run counterfactual to confirm savings didn't regress.

Stationarity caveat

Like all counterfactual analyses, this assumes the same tokens at the same complexity would have produced the same outcome from the baseline model. That's an upper bound — the baseline might have failed and required retries, which the math doesn't capture. Treat the numbers as a quality-blind ceiling.