/cost-projection
Forward-looking spend extrapolation. Computes a USD-per-day rate from the recent measurement window, projects to 7d/30d/90d/365d horizons, and surfaces "days until budget exhausted" when a budget is configured. Predictive counterpart to `cost-budget-check` (reactive).
$ npx -y skills add ruvnet/claude-flow --skill cost-projection --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/cost-projection
Context preview
The summary Claude sees to decide when to auto-load this skill.
Forward-looking spend extrapolation. Computes a USD-per-day rate from the recent measurement window, projects to 7d/30d/90d/365d horizons, and surfaces "days until budget exhausted" when a budget is configured. Predictive counterpart to `cost-budget-check` (reactive).
SKILL.md
cost-projection.SKILL.mdname: cost-projection
description: Forward-looking spend extrapolation. Computes a USD-per-day rate from the recent measurement window, projects to 7d/30d/90d/365d horizons, and surfaces "days until budget exhausted" when a budget is configured. Predictive counterpart to `cost-budget-check` (reactive).
argument-hint: "[--window 7d] [--horizons 7d,30d,90d,365d] [--format table|json]"
allowed-tools: Bash
Forward-looking cost projection that pairs with `cost-budget-check`:
- **`cost-budget-check`** answers "have we crossed the line?" — reactive
- **`cost-projection`** answers "when will we cross the line?" — predictive
Algorithm
1. Read all `session-*` records from the `cost-tracking` namespace (same source `cost-budget-check` uses). 2. Filter to a measurement window (default last 7 days on `capturedAt`/`startedAt`). 3. Compute the per-day burn rate: `windowSpend / windowDays`. 4. Linear-extrapolate to each requested horizon (default 7d/30d/90d/365d). 5. If a budget is configured (`budget-config` key set by `cost-budget set`):
- Compute "days until 75% / 90% / 100% consumed" at the current rate.
- Surface as a separate table block with `ALREADY REACHED` markers for thresholds already exceeded.
Flags
--window <duration> Measurement window. `Nh|Nd|Nw|Nm`. Default `7d`. --horizons <csv> Projection horizons. Default `7d,30d,90d,365d`. --format table|json Default `table` (markdown).
Env: `PROJECTION_NAMESPACE` override (default `cost-tracking`), `PROJECTION_QUIET=1` (alias for `--format json`).
Smoke transcript (3 sessions × $1 over 7d, $20 budget)
| Sessions in window | 3 |
| Window spend | $3.000000 |
| **USD per day** | **$0.428571** |
| All-time spend | $3.000000 across 3 sessions |
## Projected spend (linear extrapolation)
| Horizon | Days | Projected spend |
|---|---:|---:|
| 7d | 7 | $3.0000 |
| 30d | 30 | $12.8571 |
## Budget exhaustion ($20.00 configured)
| Threshold | Target | Remaining | Time at current rate |
|---|---:|---:|---|
| 75% | $15.00 | $12.00 | 28.0 days |
| 90% | $18.00 | $15.00 | 35.0 days |
| 100% | $20.00 | $17.00 | 39.7 days |
When to use
- **Finance / SRE planning**: hand the JSON output to a budget dashboard for "are we on track for the quarter?".
- **CI gates**: `cost-projection --format json | jq '.budget.exhaustion[2].daysUntilReached < 7'` → fail builds when 100% exhaustion is < 1 week away.
- **Post-workload-shift sanity check**: after a big feature lands, re-run to verify the rate hasn't accelerated past expectations.
Stationarity assumption
Linear extrapolation assumes the current rate holds. The footer reminds operators to re-run after workload shifts. For drift detection over MULTIPLE windows, pair with `cost-trend` (which already covers benchmark-drift).
Read more
name: cost-projection description: Forward-looking spend extrapolation. Computes a USD-per-day rate from the recent measurement window, projects to 7d/30d/90d/365d horizons, and surfaces "days until budget exhausted" when a budget is configured. Predictive counterpart to `cost-budget-check` (reactive). argument-hint: "[--window 7d] [--horizons 7d,30d,90d,365d] [--format table|json]" allowed-tools: Bash
Forward-looking cost projection that pairs with `cost-budget-check`:
- **`cost-budget-check`** answers "have we crossed the line?" — reactive
- **`cost-projection`** answers "when will we cross the line?" — predictive
Algorithm
1. Read all `session-*` records from the `cost-tracking` namespace (same source `cost-budget-check` uses). 2. Filter to a measurement window (default last 7 days on `capturedAt`/`startedAt`). 3. Compute the per-day burn rate: `windowSpend / windowDays`. 4. Linear-extrapolate to each requested horizon (default 7d/30d/90d/365d). 5. If a budget is configured (`budget-config` key set by `cost-budget set`):
- Compute "days until 75% / 90% / 100% consumed" at the current rate.
- Surface as a separate table block with `ALREADY REACHED` markers for thresholds already exceeded.
Flags
--window <duration> Measurement window. `Nh|Nd|Nw|Nm`. Default `7d`. --horizons <csv> Projection horizons. Default `7d,30d,90d,365d`. --format table|json Default `table` (markdown).
Env: `PROJECTION_NAMESPACE` override (default `cost-tracking`), `PROJECTION_QUIET=1` (alias for `--format json`).
Smoke transcript (3 sessions × $1 over 7d, $20 budget)
| Sessions in window | 3 | | Window spend | $3.000000 | | **USD per day** | **$0.428571** | | All-time spend | $3.000000 across 3 sessions | ## Projected spend (linear extrapolation) | Horizon | Days | Projected spend | |---|---:|---:| | 7d | 7 | $3.0000 | | 30d | 30 | $12.8571 | ## Budget exhaustion ($20.00 configured) | Threshold | Target | Remaining | Time at current rate | |---|---:|---:|---| | 75% | $15.00 | $12.00 | 28.0 days | | 90% | $18.00 | $15.00 | 35.0 days | | 100% | $20.00 | $17.00 | 39.7 days |
When to use
- **Finance / SRE planning**: hand the JSON output to a budget dashboard for "are we on track for the quarter?".
- **CI gates**: `cost-projection --format json | jq '.budget.exhaustion[2].daysUntilReached < 7'` → fail builds when 100% exhaustion is < 1 week away.
- **Post-workload-shift sanity check**: after a big feature lands, re-run to verify the rate hasn't accelerated past expectations.
Stationarity assumption
Linear extrapolation assumes the current rate holds. The footer reminds operators to re-run after workload shifts. For drift detection over MULTIPLE windows, pair with `cost-trend` (which already covers benchmark-drift).
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/claude-flow
Other skills on claude-flow.
- /agentdb-advanced
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Open skill - /agentdb-learning
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Open skill - /agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Open skill - /agentdb-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
Open skill - /agentdb-vector-search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
Open skill - /agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Open skill

