cost-report

name	cost-report
description	API cost report — computes dollar costs from token usage, flags anomalies, forecasts burn, and prescribes concrete optimizations
var
tags	["meta"]
version	2.0.0

${var} — Number of days to cover (default: 7). Pass "30" for a monthly view.

Today is ${today}. Generate a cost report from Aeon's token usage data. The output must prescribe action, not just describe spend — every section either names an anomaly, forecasts risk, or recommends a concrete move.

Model Pricing (per million tokens)

First read aeon.yml and find the gateway.provider value. Use the matching table.

Direct Anthropic (gateway.provider: direct)

Model	Input	Output	Cache Read	Cache Write
claude-opus-4-7	$15.00	$75.00	$1.50	$18.75
claude-sonnet-4-6	$3.00	$15.00	$0.30	$3.75
claude-haiku-4-5-20251001	$0.80	$4.00	$0.08	$1.00

Bankr Gateway (gateway.provider: bankr)

Model	Input	Output
claude-opus-4-7	$5.00	$25.00
claude-sonnet-4-6	$3.00	$15.00
claude-haiku-4-5-20251001	$0.80	$4.00
gemini-3-pro	$1.25	$10.00
gemini-3-flash	$0.15	$0.60
gpt-5.2	$2.50	$10.00
kimi-k2.5	$1.00	$4.00
qwen3-coder	$0.50	$2.00

Bankr does not expose cache read/write pricing separately. Treat cache columns as $0 for Bankr rows.

If a CSV row references a model not in the active table, treat it as an unknown model: price it at Opus rates (conservative), add it to the "Pricing drift" callout in the report so rates can be updated, and continue. Do not crash.

Steps

1. Determine the report window

• Default: 7 days. If ${var} is a positive integer (e.g. "30"), use that many days.
• Compute CUTOFF_DATE = today − N days. All rows where date >= CUTOFF_DATE are in-window.
• If the CSV has ≥ 2 × N days of history, also compute PRIOR_CUTOFF = today − 2N days for week-over-week.

2. Read token usage data

• File: memory/token-usage.csv
• Columns: date,skill,model,input_tokens,output_tokens,cache_read,cache_creation
• If the file is missing: log COST_REPORT_SKIP: no token-usage.csv yet and stop (no notification).
• If 0 rows in-window: log COST_REPORT_SKIP: no runs in last N days and stop.
• Parse numeric columns defensively — skip malformed rows, count them as csv_malformed for the source-status footer.

3. Compute per-row cost

For each valid in-window row, look up the model's rates and calculate:

input_cost       = input_tokens    / 1e6 × rate_input
output_cost      = output_tokens   / 1e6 × rate_output
cache_read_cost  = cache_read      / 1e6 × rate_cache_read
cache_write_cost = cache_creation  / 1e6 × rate_cache_write
row_cost         = input_cost + output_cost + cache_read_cost + cache_write_cost

4. Core aggregates (ground truth — keep these)

a. Total cost for the window (and break out input/output/cache_read/cache_write dollar shares). b. Per-skill — top 10 by cost. Columns: Skill | Runs | Total Tokens | Cost | Avg Cost/Run. c. Per-model — total runs, total tokens, total cost per model. d. Week-over-week — only if ≥ 2N days of history. delta_pct = (this_window − prior_window) / prior_window.

5. Decision sections (this is the point of the skill)

5a. Verdict line (one sentence, top of report)

Compose one sentence that captures the week. Pattern:

"Spent $X.XX across N runs ({{↑/↓ Y% WoW | no prior-week baseline}}); M anomalies flagged, projected monthly burn ~$Z.ZZ."

5b. Anomaly detection (per-skill, per-model cost spikes)

For each (skill, model) pair with ≥ 3 runs in-window:

• Compute mean µ and std-dev σ of row_cost.
• Flag any run where row_cost > µ + 2σ AND row_cost > $0.10 (ignore sub-cent noise).
• Flag skills whose total cost this window is ≥ 2× the same skill's prior-window total (only if prior window exists and prior total ≥ $0.25).

Output a table: Skill | Model | When | Run Cost | vs µ | Why (tokens_input / tokens_output / cache_write). If no anomalies, write "No anomalies." — do not omit the section.

5c. Monthly burn forecast

• daily_avg_cost = total_cost / N
• projected_monthly = daily_avg_cost × 30
• Show: "At current rate, 30-day spend ≈ $X.XX."
• If projected_monthly > $50, add a "⚠ burn-rate watch" note.

5d. Optimization opportunities (top 3, actionable)

Scan the in-window data and produce up to 3 concrete recommendations. Each must name (i) a specific skill, (ii) a specific change, (iii) estimated weekly savings. Candidate patterns:

• Model downgrade: skill runs on claude-opus-4-7, its median output_tokens / input_tokens ratio across runs is < 0.3, AND its avg run cost > $0.25. → Suggest Sonnet; savings = this_skill_cost × (1 − sonnet_rate_mix / opus_rate_mix).
• Cache underuse (direct gateway only): skill's cache_read / (cache_read + input_tokens) ratio < 0.2 across runs AND avg run cost > $0.10. → "Add a stable prompt prefix so Claude Code can cache it — would move ~X% of input tokens to cache_read at 10× savings."
• Aeon.yml mismatch: aeon.yml sets a model: override for the skill but the CSV shows runs on a different model. → "Model override drift — aeon.yml says X, runs show Y."
• Long-tail waste: a skill with >10 runs in-window where avg cost/run < $0.01 AND it produces no written artifact (no articles/ file, no notification). → "Possible no-op loop."

If fewer than 3 candidates pass the filters, say so — do not pad. If zero candidates, write "No optimization levers found this week."

5e. Pricing drift callout

If any CSV row referenced a model not in the active pricing table, list those model names and the total tokens attributed to them. Note: "Add rates to skills/cost-report/SKILL.md." If all rows matched, omit this block.

6. Write the full report

Path: articles/cost-report-${today}.md. If the file already exists, overwrite it (idempotent).

# Aeon Cost Report — ${today}
*Period: last N days · gateway: {{direct|bankr}}*

> {{verdict line from 5a}}

## Anomalies
{{table from 5b, or "No anomalies."}}

## Burn forecast
- Daily avg: $X.XX
- 30-day projection: $X.XX {{⚠ burn-rate watch if >$50}}

## Optimization opportunities
1. **{{skill}}** — {{action}}. Est. savings: ~$X.XX/week.
2. ...
3. ...
{{or "No optimization levers found this week."}}

## Cost by Skill (Top 10)
| Skill | Runs | Tokens | Cost | Avg/Run |
|-------|------|--------|------|---------|

## Cost by Model
| Model | Runs | Tokens | Cost |
|-------|------|--------|------|

## Composition
- Input: $X.XX · Output: $X.XX · Cache read: $X.XX · Cache write: $X.XX

## Week-over-week
- This window: $X.XX · Prior window: $X.XX · Δ {{+/−}}X% {{or "no prior-week baseline"}}

## Pricing drift
{{list of unknown models, or omit if none}}

---
*Sources: token-usage.csv ({{ok|degraded: M malformed rows skipped}}) · aeon.yml ({{ok|missing}}) · pricing table last reviewed in SKILL.md.*
*Generated by Aeon cost-report skill.*

7. Send notification via ./notify

Lead with the verdict, then the top 3 actions. Keep under ~15 lines.

*Cost Report — ${today} (last N days)*

{{verdict line from 5a}}

Top 3 by cost:
1. skill-a — $X.XX (N runs)
2. skill-b — $X.XX
3. skill-c — $X.XX

{{If any optimization opportunities:}}
Actions this week:
• {{skill}} → {{action}} (~$X.XX/wk)
• ...

{{If any anomalies:}} ⚠ M anomalies flagged — see report.
{{If pricing drift:}} ⚠ unknown models in CSV — see report.

30-day projection: $X.XX
Full: articles/cost-report-${today}.md

8. Log to memory/logs/${today}.md

## Cost Report
- Period: last N days (gateway: {{direct|bankr}})
- Total: $X.XX across N runs
- Verdict: {{copy verdict line}}
- Anomalies flagged: M
- Monthly projection: $X.XX
- Optimization suggestions: {{count}} ({{brief list}})
- Week-over-week: +/-X% (or "no baseline")
- Pricing drift: {{none | list of unknown models}}
- Source status: csv={{ok|degraded}}, aeon.yml={{ok|missing}}
- Article: articles/cost-report-${today}.md
- Notification sent via ./notify

Sandbox note

No outbound network required — this skill only reads local files (memory/token-usage.csv, aeon.yml). If future versions pull the Anthropic Usage/Cost API, use WebFetch as the fallback for sandboxed curl, and cache results to .xai-cache/ via a pre-fetch script (see CLAUDE.md).

Constraints

• Anomaly threshold is intentionally conservative (µ + 2σ AND >$0.10) — cheap runs should not be flagged as noise.
• Optimization recommendations must name a skill and an estimated dollar impact. "Use Sonnet more" without a target skill is not useful — skip the slot instead.
• Do not send a notification if the CSV is missing or the window is empty — silently log and exit.
• Do not change the pricing tables without verifying rates against Anthropic's current published pricing.
• Preserve idempotency: rerunning on the same day overwrites the article, does not append.