@cryptotaxi247 / SquadScope-1 / commits / b7dff9b

PRD: Cost estimation and optimization for token-based Copilot billing (#54)

* docs: PRD for cost estimation and optimization under token-based Copilot billing Comprehensive analysis of SquadScope's token consumption under GitHub Copilot's AI Credits pricing model. Includes per-run cost calculations, context growth projections, optimization strategies, and budget control mechanisms. Key findings: - Weekly analysis: ~$0.30/run (Claude Sonnet 4, ~90K input tokens) - Annual cost: ~$16/year for 52 pages ($0.31/page) - Context growth adds only 2-5% annually to weekly runs - Multiple optimization levers available if costs grow Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * feat: log per-run token usage and estimated costs Agent-Logs-Url: https://github.com/jmservera/SquadScope/sessions/d71ae0a0-794a-4717-a41e-de58d0a04745 Co-authored-by: jmservera <8036360+jmservera@users.noreply.github.com> * chore: revert unintended pycache artifacts Agent-Logs-Url: https://github.com/jmservera/SquadScope/sessions/d71ae0a0-794a-4717-a41e-de58d0a04745 Co-authored-by: jmservera <8036360+jmservera@users.noreply.github.com> * fix: avoid duplicate reskill invocation in workflow tracking Agent-Logs-Url: https://github.com/jmservera/SquadScope/sessions/d71ae0a0-794a-4717-a41e-de58d0a04745 Co-authored-by: jmservera <8036360+jmservera@users.noreply.github.com> * fix: reconcile cost figures and rephrase assumptions per review Addresses all 4 review comments: - Align Executive Summary cost estimate with annual table - Rephrase Models API usage metadata as assumption to validate - Align decision threshold with PRD budget alerts - Rephrase same-rate claim as hypothesis pending OQ6 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <copilot@github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>

Juan Manuel Servera committed May 19, 2026 at 13:52 UTC b7dff9bf107db5434c14569eb550b9a6a452e93a
23 files changed +806 -14
.github/workflows/crawl-and-publish.yml
+43 -14
@@ -228,9 +228,13 @@ jobs:
228 set -euo pipefail
229 OUTPUT_FILE="${{ steps.analysis-context.outputs.output_file }}"
230 WEEK_FILE="${{ steps.analysis-context.outputs.week_file }}"
231 + WEEK="${{ steps.analysis-context.outputs.week }}"
232 CURRENT_DATETIME="${{ steps.analysis-context.outputs.current_datetime }}"
233 + mkdir -p data/metrics
234 + PROMPT_FILE=$(mktemp)
235 + python3 scripts/analyze_fallback.py --raw-json "$WEEK_FILE" --output "$OUTPUT_FILE" --current-datetime "$CURRENT_DATETIME" --print-prompt > "$PROMPT_FILE"
236
233 - if command -v copilot >/dev/null 2>&1 && copilot -p "$(python3 scripts/analyze_fallback.py --raw-json "$WEEK_FILE" --output "$OUTPUT_FILE" --current-datetime "$CURRENT_DATETIME" --print-prompt)" \
237 + if command -v copilot >/dev/null 2>&1 && copilot -p "$(cat "$PROMPT_FILE")" \
238 -s \
239 --no-ask-user \
240 --model claude-sonnet-4 \
@@ -239,15 +243,27 @@ jobs:
243 --allow-tool=glob \
244 --allow-tool=grep \
245 > "$OUTPUT_FILE"; then
242 - echo "analysis_source=copilot-cli" >> "$GITHUB_OUTPUT"
246 + ANALYSIS_SOURCE="copilot-cli"
247 + ANALYSIS_MODEL="claude-sonnet-4"
248 else
249 echo "Copilot CLI unavailable or failed; falling back to GitHub Models API."
250 python3 scripts/analyze_fallback.py \
251 --raw-json "$WEEK_FILE" \
252 --output "$OUTPUT_FILE" \
253 --current-datetime "$CURRENT_DATETIME"
249 - echo "analysis_source=github-models" >> "$GITHUB_OUTPUT"
254 + ANALYSIS_SOURCE="github-models"
255 + ANALYSIS_MODEL="${GITHUB_MODELS_MODEL:-openai/gpt-4.1}"
256 fi
257 + python3 scripts/track_token_usage.py \
258 + --stage analysis \
259 + --source "$ANALYSIS_SOURCE" \
260 + --model "$ANALYSIS_MODEL" \
261 + --current-datetime "$CURRENT_DATETIME" \
262 + --week "$WEEK" \
263 + --prompt-file "$PROMPT_FILE" \
264 + --output-file "$OUTPUT_FILE"
265 + rm -f "$PROMPT_FILE"
266 + echo "analysis_source=$ANALYSIS_SOURCE" >> "$GITHUB_OUTPUT"
267
268 - name: quality-check
269 env:
@@ -269,18 +285,18 @@ jobs:
285 run: |
286 git config user.name "github-actions[bot]"
287 git config user.email "github-actions[bot]@users.noreply.github.com"
272 - if ! git status --short -- data/analyzed | grep -q .; then
273 - echo "No analyzed data changes to commit."
288 + if ! git status --short -- data/analyzed data/metrics | grep -q .; then
289 + echo "No analyzed data or token usage changes to commit."
290 exit 0
291 fi
276 - git stash push --include-untracked --message analyzed-data -- data/analyzed
292 + git stash push --include-untracked --message analyzed-data -- data/analyzed data/metrics
293 git fetch origin "$DEFAULT_BRANCH"
294 git checkout -B "$DEFAULT_BRANCH" "origin/$DEFAULT_BRANCH"
295 git stash pop || {
296 echo "Failed to reapply analyzed data after syncing $DEFAULT_BRANCH."
297 exit 1
298 }
283 - git add data/analyzed/
299 + git add data/analyzed/ data/metrics/
300 git diff --cached --quiet || git commit -m "analysis: weekly summary $WEEK"
301 git push origin "HEAD:$DEFAULT_BRANCH" || {
302 echo "Push failed after syncing with $DEFAULT_BRANCH."
@@ -579,28 +595,41 @@ jobs:
595 git config user.email "github-actions[bot]@users.noreply.github.com"
596 COUNTER=$(cat .squad/run-counter.txt 2>/dev/null || echo 0)
597 CURRENT_DATETIME=$(date -u +%Y-%m-%dT%H:%M:%SZ)
582 - mkdir -p .squad/skills .squad/reskill
583 -
584 - if python3 scripts/reskill.py --current-datetime "$CURRENT_DATETIME"; then
598 + WEEK=$(date -u +%G-W%V)
599 + RESKILL_OUTPUT=".squad/reskill/${WEEK}.md"
600 + RESKILL_PROMPT=$(mktemp)
601 + mkdir -p .squad/skills .squad/reskill data/metrics
602 +
603 + if python3 scripts/reskill.py --current-datetime "$CURRENT_DATETIME" --output "$RESKILL_OUTPUT" --prompt-output "$RESKILL_PROMPT"; then
604 + python3 scripts/track_token_usage.py \
605 + --stage reskill \
606 + --source github-models \
607 + --model "${GITHUB_MODELS_MODEL:-openai/gpt-4.1}" \
608 + --current-datetime "$CURRENT_DATETIME" \
609 + --week "$WEEK" \
610 + --prompt-file "$RESKILL_PROMPT" \
611 + --output-file "$RESKILL_OUTPUT"
612 + rm -f "$RESKILL_PROMPT"
613 echo "🔄 Reskill report generated for run #$COUNTER"
614 else
615 + rm -f "$RESKILL_PROMPT"
616 echo "Reskill script failed; writing placeholder trigger log."
617 echo "Reskill triggered at run #$COUNTER ($CURRENT_DATETIME)" >> .squad/reskill/trigger-log.txt
618 fi
619
591 - if ! git status --short -- .squad | grep -q .; then
592 - echo "No .squad changes to commit."
620 + if ! git status --short -- .squad data/metrics | grep -q .; then
621 + echo "No .squad or token usage changes to commit."
622 exit 0
623 fi
624
596 - git stash push --include-untracked --message reskill-state -- .squad
625 + git stash push --include-untracked --message reskill-state -- .squad data/metrics
626 git fetch origin "$DEFAULT_BRANCH"
627 git checkout -B "$DEFAULT_BRANCH" "origin/$DEFAULT_BRANCH"
628 git stash pop || {
629 echo "Failed to reapply .squad changes after syncing $DEFAULT_BRANCH."
630 exit 1
631 }
603 - git add .squad/
632 + git add .squad/ data/metrics/
633 git diff --cached --quiet || git commit -m "chore: reskill state update"
634 git push origin "HEAD:$DEFAULT_BRANCH" || {
635 echo "Warning: Failed to push .squad updates to $DEFAULT_BRANCH, but continuing."
.squad/agents/leela/history.md
+17
@@ -93,3 +93,20 @@
93 - **PR #26 outcome:** Acceptable and merged after validation. The hardened crawler delivered the expected Phase 1 improvements: caching, star snapshots, stronger low-signal filtering, bounded retry/rate-limit behavior, partial-failure metadata, and regression tests for the new query and payload behavior.
94 - **PR #25 outcome:** I flagged a blocker against the dry-run artifact: the checked-in file under `data/analyzed/` does not match the approved Analyze → Generate contract in `.squad/decisions.md` (`Signal` / `Noise` / `Gaps`). By the time I verified final PR state, GitHub already showed PR #25 as merged, so the blocker was recorded as review commentary and follow-up guidance rather than an enforceable lockout.
95 - **Operational constraint:** Because the authenticated GitHub account is also the PR author, GitHub blocked formal approve/request-changes reviews. Outcome had to be recorded by comment, and only PR #26 could be actively merged during this pass.
96 +
97 +### 2026-05-19T05:17:53.102+02:00 — Cost Estimation PRD
98 +
99 +- **Deliverable:** `docs/PRD-cost-estimation.md` — comprehensive PRD for token-based Copilot billing cost estimation and optimization
100 +- **Key findings:**
101 + - Weekly analysis cost: ~$0.30/run (Claude Sonnet 4, ~90K input tokens dominated by 301KB raw JSON)
102 + - Reskill cost: ~$0.036/run (GPT-4.1, much smaller context, runs every 5th week)
103 + - Annual all-in cost: ~$16/year for 52 weekly pages — $0.31/page
104 + - Context growth is modest (2-5%/year on weekly runs) because raw JSON dominates and is stable
105 + - Reskill grows faster (24-49%/year) due to accumulating history, but runs infrequently
106 +- **Optimization levers identified (ordered by ROI):**
107 + 1. Pre-process raw JSON to reduce tokens (40-60% savings on input)
108 + 2. Model downgrade for routine analysis (GPT-4.1 or Haiku saves 33-67%)
109 + 3. Prompt caching if available (77% savings on JSON portion)
110 + 4. Token budget with tiered degradation
111 +- **Pricing model hypothesis (pending OQ6 validation):** GitHub Models API and Copilot CLI are assumed to use the same per-token rates, with the difference being auth mechanism and agentic capabilities rather than cost per token. This assumption needs empirical validation — see PRD OQ6.
112 +- **Open risk:** Whether Copilot CLI transcript exposes actual token usage (needed for monitoring)
.squad/decisions/inbox/leela-cost-estimation.md new
+32
@@ -0,0 +1,32 @@
1 +# Decision Proposal: Cost Estimation Framework for SquadScope
2 +
3 +**Date:** 2026-05-19T05:17:53.102+02:00
4 +**Author:** Leela (Lead/Architect)
5 +**Status:** Proposed
6 +**PRD:** docs/PRD-cost-estimation.md
7 +
8 +## Summary
9 +
10 +SquadScope's automated Copilot CLI and GitHub Models API usage has a quantifiable cost under token-based billing. At current configuration (~$0.30/week), annual cost is ~$16 — well within Copilot Pro's 300 credits/month allowance. However, proactive monitoring and budget controls are needed before context growth or model upgrades change the picture.
11 +
12 +## Decisions Proposed
13 +
14 +1. **Accept current cost profile as sustainable** — $16/year is economically trivial; no immediate action required on model downgrade.
15 +2. **Implement token usage tracking (Phase A)** — Add `scripts/track_token_usage.py` and `data/metrics/token-usage.jsonl` to establish baselines before optimizing.
16 +3. **Set budget alert thresholds** — Warn at $0.50/run, fail at $1.00/run, email alert at $5/month cumulative, auto-switch to cheaper model at $10/month cumulative (aligned with PRD budget alerts table).
17 +4. **Defer raw JSON pre-processing** — The 40-60% savings is significant but adds pipeline complexity; implement only if costs grow beyond $30/year.
18 +5. **Wisdom.md cap at 5 KB** — Reskill should retire obsolete heuristics, not only append.
19 +
20 +## Rationale
21 +
22 +The dominant cost driver (raw JSON at 86K tokens) is stable and bounded by crawl scope. Growth comes from wisdom/skills/history accumulation, which is slow. Premature optimization would add complexity without meaningful savings at current scale.
23 +
24 +## Risks
25 +
26 +- OQ5/OQ6: Billing mechanics for Copilot CLI vs Models API may differ in ways not yet visible
27 +- Credit exhaustion mid-month would disrupt the weekly pipeline if no degradation path exists
28 +
29 +## Next Steps
30 +
31 +- Create implementation issues per PRD Phase A (tracking)
32 +- Validate actual token counts against estimates after 4 weeks of data
data/metrics/.gitkeep
docs/PRD-cost-estimation.md new
+439
@@ -0,0 +1,439 @@
1 +# PRD: Cost Estimation and Optimization for Token-Based Copilot Billing
2 +
3 +**Author:** Leela (Lead/Architect)
4 +**Date:** 2026-05-19
5 +**Status:** Draft
6 +**Relates to:** docs/PRD.md, .squad/decisions.md (CI Architecture Decision)
7 +
8 +---
9 +
10 +## Executive Summary
11 +
12 +SquadScope runs automated AI analysis weekly using GitHub Copilot CLI and GitHub Models API inside GitHub Actions. With GitHub Copilot's shift to token-based consumption billing (AI Credits at $0.01/credit), every pipeline run has a measurable cost. This PRD quantifies per-run and annual costs, projects context growth over 6–12 months, and defines optimization strategies and budget controls to keep SquadScope economically sustainable as a zero-revenue open-source project.
13 +
14 +**Key finding:** A weekly analysis run costs approximately **$0.27–$0.35** in AI credits. The annual projection table totals **$15.96/year** at current configuration. Accounting for context growth (5–10% over 12 months), occasional fallback runs (~$0.20 each), and variance in weekly token counts, the realistic annual range is **$16–$20/year** — comparable to a cheap newsletter service, and orders of magnitude cheaper than a human analyst.
15 +
16 +---
17 +
18 +## Problem Statement
19 +
20 +### Why Cost Matters for Automated Copilot Usage
21 +
22 +1. **Predictability:** Unlike interactive Copilot chat (included in subscription), automated CI invocations consume tokens that count against plan allowances and incur overage charges.
23 +2. **Context growth:** SquadScope's wisdom, skills, and history accumulate over time, making each run progressively more expensive unless managed.
24 +3. **Budget transparency:** As a personal open-source project, jmservera needs clear visibility into the marginal cost of each published page.
25 +4. **Plan selection:** Understanding token consumption informs whether Copilot Pro ($10/month, 300 credits included) or Copilot Pro+ ($39/month, 1500 credits) is the right tier.
26 +5. **Graceful degradation:** If token budgets are exhausted, the pipeline must degrade gracefully (use cheaper models, skip optional enrichment) rather than fail silently.
27 +
28 +---
29 +
30 +## Token Pricing Model Summary
31 +
32 +*(Source: [GitHub Copilot Models and Pricing](https://docs.github.com/en/copilot/reference/copilot-billing/models-and-pricing), fetched 2026-05-19)*
33 +
34 +### Core Concepts
35 +
36 +- **1 AI Credit = $0.01 USD**
37 +- Tokens are consumed for: input (prompt + context), cached input (reused context), and output (generated text)
38 +- Plans include monthly credit allowances; overage billed at per-token rates
39 +- Code completions remain unlimited on paid plans (not relevant to SquadScope CI)
40 +
41 +### Relevant Model Pricing (per 1M tokens)
42 +
43 +| Model | Category | Input | Cached Input | Output | Cache Write |
44 +|-------|----------|-------|--------------|--------|-------------|
45 +| **Claude Sonnet 4** (primary) | Versatile | $3.00 | $0.30 | $15.00 | $3.75 |
46 +| **GPT-4.1** (fallback) | Versatile | $2.00 | $0.50 | $8.00 | — |
47 +| GPT-5 mini | Lightweight | $0.25 | $0.025 | $2.00 | — |
48 +| Claude Haiku 4.5 | Versatile | $1.00 | $0.10 | $5.00 | $1.25 |
49 +| Gemini 3 Flash | Lightweight | $0.50 | $0.05 | $3.00 | — |
50 +
51 +### Plan Allowances
52 +
53 +| Plan | Monthly Credits | Equivalent $ | Notes |
54 +|------|----------------|--------------|-------|
55 +| Copilot Free | ~13.33 | $0.13 | Very limited |
56 +| Copilot Pro | 300 | $3.00 | Likely sufficient for SquadScope alone |
57 +| Copilot Pro+ | 1500 | $15.00 | Generous headroom |
58 +| Copilot Business | 200/user pooled | $2.00/user | Org billing |
59 +
60 +---
61 +
62 +## Cost Breakdown per Pipeline Stage
63 +
64 +### Tokenization Assumptions
65 +
66 +- Average ~4 characters per token (English text/markdown)
67 +- JSON is less efficient: ~3.5 characters per token due to structural characters
68 +- 1 KB of prose ≈ 250 tokens; 1 KB of JSON ≈ 285 tokens
69 +
70 +### Stage 1: Weekly Analysis (Copilot CLI — Claude Sonnet 4)
71 +
72 +| Component | Size | Estimated Tokens |
73 +|-----------|------|-----------------|
74 +| Analyze prompt template | 6.5 KB | ~1,625 |
75 +| Raw weekly JSON (`data/raw/2026-W21.json`) | 301 KB | ~86,000 |
76 +| Wisdom file (`.squad/identity/wisdom.md`) | 3.2 KB | ~800 |
77 +| Skills directory (currently empty) | 0 KB | 0 |
78 +| Previous week summary | ~5 KB | ~1,250 |
79 +| System/tool overhead (Copilot CLI framing) | ~2 KB | ~500 |
80 +| **Total input tokens** | **~318 KB** | **~90,175** |
81 +
82 +| Output Component | Size | Estimated Tokens |
83 +|------------------|------|-----------------|
84 +| Analyzed summary markdown | ~5 KB | ~1,250 |
85 +| Tool calls/internal reasoning overhead | ~3 KB | ~750 |
86 +| **Total output tokens** | **~8 KB** | **~2,000** |
87 +
88 +**Weekly analysis cost (Claude Sonnet 4):**
89 +
90 +```
91 +Input: 90,175 tokens × $3.00/1M = $0.2705
92 +Output: 2,000 tokens × $15.00/1M = $0.0300
93 +─────────────────────────────────────────────
94 +Total per weekly run: ≈ $0.30
95 +```
96 +
97 +**In AI Credits: ~30 credits per weekly run.**
98 +
99 +### Stage 2: Reskill (Every 5th Week — GitHub Models API — GPT-4.1)
100 +
101 +The reskill run is larger because it reads 5 weeks of history plus snapshot data.
102 +
103 +| Component | Size | Estimated Tokens |
104 +|-----------|------|-----------------|
105 +| Reskill prompt template | 2.9 KB | ~725 |
106 +| Last 5 analyzed summaries (5 × ~5 KB) | ~25 KB | ~6,250 |
107 +| Wisdom file | 3.2 KB | ~800 |
108 +| Skills directory (growing) | ~2 KB (month 6) | ~500 |
109 +| Star snapshot context (5 weeks) | ~15 KB | ~4,285 |
110 +| Quality trend report | ~2 KB | ~500 |
111 +| **Total input tokens** | **~50 KB** | **~13,060** |
112 +
113 +| Output Component | Size | Estimated Tokens |
114 +|------------------|------|-----------------|
115 +| Reskill report | ~4 KB | ~1,000 |
116 +| Wisdom updates | ~1 KB | ~250 |
117 +| **Total output tokens** | **~5 KB** | **~1,250** |
118 +
119 +**Reskill cost (GPT-4.1 via GitHub Models):**
120 +
121 +```
122 +Input: 13,060 tokens × $2.00/1M = $0.0261
123 +Output: 1,250 tokens × $8.00/1M = $0.0100
124 +─────────────────────────────────────────────
125 +Total per reskill run: ≈ $0.036
126 +```
127 +
128 +**In AI Credits: ~4 credits per reskill run.**
129 +
130 +### Stage 3: Fallback Analysis (GitHub Models API — GPT-4.1)
131 +
132 +When Copilot CLI fails and the fallback triggers:
133 +
134 +```
135 +Input: 90,175 tokens × $2.00/1M = $0.1804
136 +Output: 2,000 tokens × $8.00/1M = $0.0160
137 +─────────────────────────────────────────────
138 +Total per fallback run: ≈ $0.20
139 +```
140 +
141 +**Fallback is ~33% cheaper than primary** due to GPT-4.1's lower rates vs Claude Sonnet 4.
142 +
143 +### Stage 4: GitHub Actions Compute
144 +
145 +| Job | Runner | Duration (est.) | Cost |
146 +|-----|--------|-----------------|------|
147 +| Crawl | ubuntu-latest | ~3 min | Free (public repo) |
148 +| Analyze | ubuntu-latest | ~2 min | Free (public repo) |
149 +| Generate | ubuntu-latest | ~1 min | Free (public repo) |
150 +| Deploy | ubuntu-latest | ~2 min | Free (public repo) |
151 +| Reskill | ubuntu-latest | ~2 min | Free (public repo) |
152 +
153 +**GitHub Actions is free for public repositories.** If the repo becomes private, estimate ~10 minutes/run × $0.008/min = $0.08/run.
154 +
155 +---
156 +
157 +## Context Growth Projections
158 +
159 +### Growth Vectors
160 +
161 +| Component | Current Size | Growth Rate | 6-Month Projection | 12-Month Projection |
162 +|-----------|-------------|-------------|--------------------|--------------------|
163 +| Wisdom.md | 3.2 KB | +0.5 KB per reskill (~every 5 weeks) | 5.8 KB | 8.4 KB |
164 +| Skills directory | 0 KB | +1 KB per reskill (new skill files) | 5.2 KB | 10.4 KB |
165 +| Raw JSON (per file) | 301 KB | Stable (weekly crawl scope fixed) | 301 KB | 301 KB |
166 +| Star snapshots (per file) | ~3 KB | Stable per file, linear file count | 78 KB total | 156 KB total |
167 +| Previous summary | 5 KB | Stable (only last week sent) | 5 KB | 5 KB |
168 +| Analyzed archive | 5 KB × weeks | Linear growth | 130 KB (26 files) | 260 KB (52 files) |
169 +
170 +### Token Cost Trajectory
171 +
172 +| Timeframe | Weekly Input Tokens | Weekly Cost | Reskill Input Tokens | Monthly Cost (4.3 weeks + 0.86 reskill amortized) |
173 +|-----------|--------------------:|------------:|---------------------:|---:|
174 +| Month 1 (now) | 90,175 | $0.30 | 13,060 | $1.32 |
175 +| Month 6 | 92,300 (+2.4%) | $0.31 | 16,200 (+24%) | $1.36 |
176 +| Month 12 | 94,500 (+4.8%) | $0.32 | 19,500 (+49%) | $1.40 |
177 +
178 +**Key insight:** Context growth is modest because the dominant cost driver (raw JSON at 86K tokens) is stable. Wisdom and skills growth adds only ~2-5% annually to weekly runs. Reskill grows faster (24-49%) because it accumulates more historical context, but it runs infrequently.
179 +
180 +---
181 +
182 +## Cost per Page Calculation
183 +
184 +### Annual Cost Projection (Year 1)
185 +
186 +| Line Item | Frequency | Unit Cost | Annual Cost |
187 +|-----------|-----------|-----------|-------------|
188 +| Weekly analysis (Claude Sonnet 4) | 52/year | $0.30 | $15.60 |
189 +| Reskill (GPT-4.1) | ~10/year | $0.036 | $0.36 |
190 +| GitHub Actions compute | 52/year | $0.00 (public) | $0.00 |
191 +| **Total annual AI cost** | | | **$15.96** |
192 +
193 +### Cost per Published Page
194 +
195 +```
196 +Annual AI cost / 52 pages = $15.96 / 52 = $0.307 per page
197 +```
198 +
199 +Including amortized reskill:
200 +```
201 +($15.60 + $0.36) / 52 = $0.307 per page (reskill is negligible)
202 +```
203 +
204 +### Comparative Analysis
205 +
206 +| Approach | Annual Cost | Cost per Page | Quality |
207 +|----------|-------------|---------------|---------|
208 +| **SquadScope (automated)** | **~$16/year** | **$0.31** | Consistent, opinionated, improving |
209 +| Human analyst (freelance) | $5,200–$10,400/year | $100–$200 | High but variable |
210 +| Newsletter service (Substack Pro) | $600/year | $11.50 | Platform cost only, still need writer |
211 +| Manual GPT-4 chat (copy-paste) | ~$50/year | ~$1 | Inconsistent, no learning loop |
212 +
213 +**SquadScope is 300× cheaper than a human analyst and offers compounding quality via reskill.**
214 +
215 +---
216 +
217 +## Cost Optimization Strategies
218 +
219 +### Strategy 1: Model Selection by Task Criticality
220 +
221 +| Task | Current Model | Optimized Model | Savings |
222 +|------|---------------|-----------------|---------|
223 +| Weekly analysis | Claude Sonnet 4 ($0.30) | GPT-5 mini ($0.02 input + $0.004 output) | **92%** |
224 +| Reskill | GPT-4.1 ($0.036) | Keep (already cheap, quality matters) | 0% |
225 +| Fallback analysis | GPT-4.1 ($0.20) | GPT-5 mini ($0.024) | **88%** |
226 +
227 +**Recommendation:** Start with Claude Sonnet 4 for quality. If quality_score consistently ≥ 75, experiment with GPT-4.1 or Claude Haiku 4.5 for weekly analysis. Reserve premium models for reskill where judgment quality matters most.
228 +
229 +### Strategy 2: Context Window Management
230 +
231 +1. **Summarize raw JSON before sending:** Instead of sending 301 KB of raw JSON, pre-process to extract only the fields used by the prompt (~50 KB, saving ~60% of input tokens).
232 +2. **Cap wisdom.md:** Establish a 5 KB soft limit. During reskill, retire obsolete heuristics rather than only appending.
233 +3. **Compress star snapshots:** For reskill, send only delta summaries rather than full snapshot JSON.
234 +
235 +**Potential savings:** 40-60% reduction in input tokens = ~$0.12–$0.18 savings per weekly run.
236 +
237 +### Strategy 3: Skip-If-Unchanged (Caching)
238 +
239 +If the crawled data has fewer than N significant changes from the prior week (e.g., <5 new repos, <10% topic shift), skip analysis and republish last week's summary with an "unchanged" note.
240 +
241 +**Potential savings:** 5-15% of annual runs skipped = $0.80–$2.40/year.
242 +
243 +**Risk:** Breaks the "every week has a page" contract. Implement as opt-in only.
244 +
245 +### Strategy 4: Token Budget per Run
246 +
247 +Set a hard cap on total tokens per invocation:
248 +
249 +```yaml
250 +env:
251 + SQUADSCOPE_TOKEN_BUDGET: 150000 # tokens
252 + SQUADSCOPE_COST_CAP: 0.50 # USD per run
253 +```
254 +
255 +If pre-calculated token estimate exceeds budget:
256 +1. Truncate raw JSON to top 50 repos by stars_gained
257 +2. Omit skills context
258 +3. Shorten previous summary to frontmatter-only
259 +
260 +### Strategy 5: Prompt Optimization
261 +
262 +| Optimization | Token Savings | Effort |
263 +|--------------|---------------|--------|
264 +| Remove output template (model knows format) | ~500 tokens | Low |
265 +| Shorten editorial stance to bullet points | ~200 tokens | Low |
266 +| Inline wisdom into prompt (skip file read) | ~100 tokens | Medium |
267 +| Use structured JSON output instead of markdown | ~300 output tokens | Medium |
268 +
269 +**Combined prompt optimization: ~1,100 tokens saved = ~$0.004/run (marginal).**
270 +
271 +Prompt optimization has low ROI because the raw JSON dominates input cost. Focus on Strategy 2 (context window management) first.
272 +
273 +### Strategy 6: Cached Input Optimization
274 +
275 +If the Copilot CLI supports prompt caching (reusing context across calls), the 86K raw JSON tokens could be served at cached rates:
276 +
277 +```
278 +Cached: 86,000 × $0.30/1M = $0.026 (vs $0.258 uncached)
279 +Savings: $0.232 per run = 77% reduction on the JSON portion
280 +```
281 +
282 +**Status:** Copilot CLI caching behavior is not yet documented for CI invocations. Monitor for updates.
283 +
284 +---
285 +
286 +## Monitoring & Alerting Design
287 +
288 +### Per-Run Token Tracking
289 +
290 +1. **Copilot CLI transcript:** The `--share=PATH` flag exports a session transcript. Parse it post-run to extract actual token counts.
291 +2. **GitHub Models API response headers (assumption to validate):** The API is expected to return `x-ratelimit-remaining` headers and usage metadata in response JSON. Current scripts only parse the JSON body and discard headers; implementation will need to explicitly capture response headers and extract usage fields. This assumption requires validation against live API responses.
292 +3. **Workflow annotations:** Log token estimates and actuals as workflow summary annotations.
293 +
294 +### Implementation
295 +
296 +```yaml
297 +- name: Log token usage
298 + if: always()
299 + run: |
300 + # Parse Copilot CLI transcript for usage data
301 + if [ -f copilot-session.md ]; then
302 + python3 scripts/track_token_usage.py \
303 + --transcript copilot-session.md \
304 + --stage analysis \
305 + --week "$WEEK"
306 + fi
307 +```
308 +
309 +### Usage Dashboard
310 +
311 +Store per-run metrics in `data/metrics/token-usage.jsonl`:
312 +
313 +```json
314 +{"week": "2026-W21", "stage": "analysis", "model": "claude-sonnet-4", "input_tokens": 90175, "output_tokens": 2000, "cost_usd": 0.30, "timestamp": "2026-05-19T08:00:00Z"}
315 +```
316 +
317 +Render a simple chart on the SquadScope site (Hugo shortcode or static SVG) showing:
318 +- Weekly cost trend
319 +- Cumulative annual spend
320 +- Context size growth
321 +
322 +### Budget Alerts
323 +
324 +| Threshold | Action |
325 +|-----------|--------|
326 +| Single run > $0.50 | Warning annotation in workflow summary |
327 +| Single run > $1.00 | Fail the run, open issue |
328 +| Monthly cumulative > $5.00 | Email alert via GitHub Actions notification |
329 +| Monthly cumulative > $10.00 | Auto-switch to GPT-5 mini for remaining month |
330 +
331 +---
332 +
333 +## Budget Controls (Hard Limits, Graceful Degradation)
334 +
335 +### Tiered Degradation Strategy
336 +
337 +```
338 +Normal Mode (cost < $0.50/run)
339 + └─ Full analysis with Claude Sonnet 4
340 + └─ Full context (raw JSON + wisdom + skills + prior week)
341 +
342 +Budget Mode (cost would exceed $0.50/run)
343 + └─ Switch to GPT-4.1 (saves ~33%)
344 + └─ Truncate raw JSON to top 100 repos
345 + └─ Omit skills context
346 +
347 +Minimal Mode (monthly budget exhausted)
348 + └─ Switch to GPT-5 mini (saves ~92%)
349 + └─ Truncate raw JSON to top 30 repos
350 + └─ Omit all optional context
351 + └─ Quality gate threshold lowered to 50
352 +
353 +Emergency Mode (all credits exhausted)
354 + └─ Skip AI analysis entirely
355 + └─ Publish raw data summary (stats only, no editorial)
356 + └─ Open issue for manual intervention
357 +```
358 +
359 +### Pre-flight Cost Estimation
360 +
361 +Before invoking the model, estimate cost:
362 +
363 +```python
364 +def estimate_cost(input_tokens: int, output_estimate: int, model: str) -> float:
365 + rates = {
366 + "claude-sonnet-4": {"input": 3.00, "output": 15.00},
367 + "openai/gpt-4.1": {"input": 2.00, "output": 8.00},
368 + "openai/gpt-5-mini": {"input": 0.25, "output": 2.00},
369 + }
370 + r = rates[model]
371 + return (input_tokens * r["input"] + output_estimate * r["output"]) / 1_000_000
372 +```
373 +
374 +---
375 +
376 +## Implementation Plan
377 +
378 +### Issues to Create
379 +
380 +| # | Title | Priority | Effort | Dependencies |
381 +|---|-------|----------|--------|--------------|
382 +| 1 | Add pre-flight token estimation to analyze workflow | High | S | None |
383 +| 2 | Implement `scripts/track_token_usage.py` for post-run metrics | High | M | None |
384 +| 3 | Create `data/metrics/token-usage.jsonl` schema and writer | Medium | S | #2 |
385 +| 4 | Add budget alerts to workflow (annotations + issue creation) | Medium | M | #2 |
386 +| 5 | Implement tiered degradation (model downgrade on budget) | Medium | M | #1 |
387 +| 6 | Pre-process raw JSON to reduce token count (Strategy 2) | Medium | M | None |
388 +| 7 | Add wisdom.md size cap and retirement policy to reskill | Low | S | None |
389 +| 8 | Create cost dashboard Hugo shortcode | Low | L | #3 |
390 +| 9 | Investigate Copilot CLI caching for CI (Strategy 6) | Low | S | None |
391 +| 10 | Document model selection decision matrix | Low | S | None |
392 +
393 +### Phasing
394 +
395 +- **Phase A (immediate):** Issues 1–3 — visibility into actual costs
396 +- **Phase B (month 2):** Issues 4–6 — active cost management
397 +- **Phase C (month 3+):** Issues 7–10 — optimization and documentation
398 +
399 +---
400 +
401 +## Open Questions
402 +
403 +| # | Question | Impact | Proposed Resolution |
404 +|---|----------|--------|---------------------|
405 +| OQ1 | Does Copilot CLI expose actual token usage in transcript or exit metadata? | High — needed for accurate tracking | Spike: parse `--share` output for usage data |
406 +| OQ2 | Does `copilot-requests: write` permission on GITHUB_TOKEN consume from org pool or personal allowance? | High — affects billing entity | Test in workflow with usage monitoring |
407 +| OQ3 | Is prompt caching available for Copilot CLI in non-interactive mode? | Medium — could save 77% on JSON input | Monitor GitHub changelog |
408 +| OQ4 | What's the actual token count for the raw JSON? (estimated 86K, need actuals) | Medium — calibration | Add tokenizer count in pre-flight step |
409 +| OQ5 | How does GitHub bill for the Copilot CLI invocation itself vs. the underlying model tokens? | High — may have additional overhead | Review billing after first month |
410 +| OQ6 | Are GitHub Models API calls billed differently from Copilot CLI calls against the same model? | Medium — affects fallback cost comparison | Compare billing line items |
411 +| OQ7 | What happens when the Copilot Pro credit allowance is consumed mid-month? | High — operational risk | Set up overage alerts, test degradation path |
412 +
413 +---
414 +
415 +## Appendix: Token Estimation Methodology
416 +
417 +### Tokenization Rules of Thumb
418 +
419 +- English prose: ~4 characters/token (or ~0.75 words/token)
420 +- JSON with short keys: ~3.5 characters/token
421 +- Markdown with formatting: ~3.8 characters/token
422 +- Code: ~3.2 characters/token
423 +
424 +### Validation Approach
425 +
426 +Once `scripts/track_token_usage.py` is live, compare estimates against actuals for 4 weeks. Adjust multipliers if estimates deviate by >20%.
427 +
428 +### Raw JSON Breakdown (2026-W21.json = 301 KB)
429 +
430 +Estimated token distribution:
431 +- Structural JSON characters (`{}[],:"`): ~20% = ~17K tokens
432 +- Repository names, URLs, descriptions: ~50% = ~43K tokens
433 +- Numeric fields (stars, dates): ~15% = ~13K tokens
434 +- Topic arrays: ~15% = ~13K tokens
435 +- **Total: ~86K tokens** (at 3.5 chars/token)
436 +
437 +---
438 +
439 +*This PRD will be updated with actuals once token tracking is implemented (Phase A, Issues 1–3).*
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+9
@@ -71,6 +71,11 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
71 action="store_true",
72 help="Render the prompt to stdout without calling GitHub Models.",
73 )
74 + parser.add_argument(
75 + "--prompt-output",
76 + type=Path,
77 + help="Optional path to write the rendered prompt while running the script.",
78 + )
79 return parser.parse_args(argv)
80
81
@@ -279,6 +284,10 @@ def main(argv: list[str] | None = None) -> int:
284 print(prompt)
285 return 0
286
287 + if args.prompt_output:
288 + args.prompt_output.parent.mkdir(parents=True, exist_ok=True)
289 + args.prompt_output.write_text(prompt, encoding="utf-8")
290 +
291 markdown = call_github_models(prompt)
292 output_path.parent.mkdir(parents=True, exist_ok=True)
293 output_path.write_text(markdown, encoding="utf-8")
scripts/track_token_usage.py new
+109
@@ -0,0 +1,109 @@
1 +#!/usr/bin/env python3
2 +from __future__ import annotations
3 +
4 +import argparse
5 +import json
6 +import math
7 +from datetime import UTC, datetime
8 +from pathlib import Path
9 +
10 +ROOT = Path(__file__).resolve().parent.parent
11 +DEFAULT_USAGE_FILE = ROOT / "data" / "metrics" / "token-usage.jsonl"
12 +CHARS_PER_TOKEN = 4
13 +MODEL_RATES = {
14 + "claude-sonnet-4": {"input": 3.00, "output": 15.00},
15 + "openai/gpt-4.1": {"input": 2.00, "output": 8.00},
16 + "gpt-4.1": {"input": 2.00, "output": 8.00},
17 + "openai/gpt-5-mini": {"input": 0.25, "output": 2.00},
18 + "gpt-5-mini": {"input": 0.25, "output": 2.00},
19 + "claude-haiku-4.5": {"input": 1.00, "output": 5.00},
20 +}
21 +
22 +
23 +def parse_args(argv: list[str] | None = None) -> argparse.Namespace:
24 + parser = argparse.ArgumentParser(description="Track token usage and estimated cost per pipeline run.")
25 + parser.add_argument("--stage", required=True, help="Pipeline stage (for example: analysis, reskill).")
26 + parser.add_argument("--source", required=True, help="Execution source (for example: copilot-cli, github-models).")
27 + parser.add_argument("--model", required=True, help="Model name used for cost rates.")
28 + parser.add_argument("--current-datetime", required=True, help="ISO-8601 timestamp for the run.")
29 + parser.add_argument("--week", help="Week slug (YYYY-WNN). If omitted, inferred from current datetime.")
30 + parser.add_argument("--prompt-file", type=Path, help="Prompt file used to estimate input tokens.")
31 + parser.add_argument("--output-file", type=Path, help="Output file used to estimate output tokens.")
32 + parser.add_argument("--input-tokens", type=int, help="Explicit input token count.")
33 + parser.add_argument("--output-tokens", type=int, help="Explicit output token count.")
34 + parser.add_argument("--usage-file", type=Path, default=DEFAULT_USAGE_FILE, help="JSONL path for usage ledger.")
35 + return parser.parse_args(argv)
36 +
37 +
38 +def parse_datetime(value: str) -> datetime:
39 + candidate = value.strip()
40 + if candidate.endswith("Z"):
41 + candidate = f"{candidate[:-1]}+00:00"
42 + parsed = datetime.fromisoformat(candidate)
43 + return parsed if parsed.tzinfo else parsed.replace(tzinfo=UTC)
44 +
45 +
46 +def week_slug(value: datetime) -> str:
47 + year, week, _ = value.isocalendar()
48 + return f"{year}-W{week:02d}"
49 +
50 +
51 +def estimate_tokens_from_text(text: str) -> int:
52 + stripped = text.strip()
53 + if not stripped:
54 + return 0
55 + return max(1, math.ceil(len(stripped) / CHARS_PER_TOKEN))
56 +
57 +
58 +def estimate_tokens_from_path(path: Path | None) -> int:
59 + if path is None or not path.exists():
60 + return 0
61 + return estimate_tokens_from_text(path.read_text(encoding="utf-8"))
62 +
63 +
64 +def estimate_cost_usd(model: str, input_tokens: int, output_tokens: int) -> float | None:
65 + rates = MODEL_RATES.get(model)
66 + if not rates:
67 + return None
68 + total = (input_tokens * rates["input"] + output_tokens * rates["output"]) / 1_000_000
69 + return round(total, 6)
70 +
71 +
72 +def build_record(args: argparse.Namespace) -> dict[str, object]:
73 + parsed_datetime = parse_datetime(args.current_datetime).astimezone(UTC)
74 + input_tokens = args.input_tokens if args.input_tokens is not None else estimate_tokens_from_path(args.prompt_file)
75 + output_tokens = args.output_tokens if args.output_tokens is not None else estimate_tokens_from_path(args.output_file)
76 + week = args.week or week_slug(parsed_datetime)
77 + cost = estimate_cost_usd(args.model, input_tokens, output_tokens)
78 + return {
79 + "timestamp": parsed_datetime.isoformat().replace("+00:00", "Z"),
80 + "month": parsed_datetime.strftime("%Y-%m"),
81 + "week": week,
82 + "stage": args.stage,
83 + "source": args.source,
84 + "model": args.model,
85 + "input_tokens": input_tokens,
86 + "output_tokens": output_tokens,
87 + "total_tokens": input_tokens + output_tokens,
88 + "cost_usd": cost,
89 + "estimated": args.input_tokens is None or args.output_tokens is None,
90 + }
91 +
92 +
93 +def append_record(path: Path, record: dict[str, object]) -> None:
94 + path.parent.mkdir(parents=True, exist_ok=True)
95 + with path.open("a", encoding="utf-8") as handle:
96 + handle.write(json.dumps(record, ensure_ascii=True))
97 + handle.write("\n")
98 +
99 +
100 +def main(argv: list[str] | None = None) -> int:
101 + args = parse_args(argv)
102 + record = build_record(args)
103 + append_record(args.usage_file, record)
104 + print(json.dumps(record, indent=2))
105 + return 0
106 +
107 +
108 +if __name__ == "__main__":
109 + raise SystemExit(main())
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tests/test_pipeline.py
+11
@@ -194,9 +194,20 @@ class WorkflowConfigTests(unittest.TestCase):
194 self.assertEqual(reskill_step["env"]["COPILOT_GITHUB_TOKEN"], "${{ secrets.COPILOT_GH_TOKEN }}")
195 reskill_run = reskill_step["run"]
196 self.assertIn("python3 scripts/reskill.py --current-datetime", reskill_run)
197 + self.assertIn("--prompt-output", reskill_run)
198 + self.assertIn("python3 scripts/track_token_usage.py", reskill_run)
199 self.assertIn("mkdir -p .squad/skills .squad/reskill", reskill_run)
200 + self.assertIn("data/metrics", reskill_run)
201 self.assertIn("trigger-log.txt", reskill_run)
202 self.assertIn("git add .squad/", reskill_run)
203 + self.assertIn("data/metrics/", reskill_run)
204 +
205 + analyze = workflow["jobs"]["analyze"]
206 + run_analysis_step = next((s for s in analyze["steps"] if s.get("name") == "Run analysis"), None)
207 + self.assertIsNotNone(run_analysis_step)
208 + run_analysis = run_analysis_step["run"]
209 + self.assertIn("python3 scripts/track_token_usage.py", run_analysis)
210 + self.assertIn("mkdir -p data/metrics", run_analysis)
211
212 def test_generate_workflow_runs_rollups_and_commits_all_content(self) -> None:
213 workflow_path = Path(".github/workflows/crawl-and-publish.yml")
tests/test_reskill.py
+55
@@ -115,6 +115,61 @@ class ReskillTests(unittest.TestCase):
115 output_path = base / ".squad" / "reskill" / "2026-W21.md"
116 self.assertEqual(output_path.read_text(encoding="utf-8"), "# Reskill Report\n")
117
118 + def test_main_can_write_prompt_output_sidecar(self) -> None:
119 + tests_root = Path(__file__).resolve().parent
120 + with tempfile.TemporaryDirectory(dir=tests_root) as tmpdir:
121 + base = Path(tmpdir)
122 + analyzed_dir = base / "data" / "analyzed"
123 + snapshots_dir = base / "data" / "snapshots"
124 + wisdom_path = base / ".squad" / "identity" / "wisdom.md"
125 + skills_dir = base / ".squad" / "skills"
126 + prompt_template = base / "reskill.md"
127 + output_path = base / ".squad" / "reskill" / "2026-W21.md"
128 + prompt_output_path = base / "tmp" / "reskill-prompt.txt"
129 + analyzed_dir.mkdir(parents=True)
130 + snapshots_dir.mkdir(parents=True)
131 + wisdom_path.parent.mkdir(parents=True)
132 + skills_dir.mkdir(parents=True)
133 + output_path.parent.mkdir(parents=True)
134 + (analyzed_dir / "2026-W21-summary.md").write_text(
135 + "---\nweek: 2026-W21\nquality_score: 76\n---\n\nBody\n",
136 + encoding="utf-8",
137 + )
138 + wisdom_path.write_text("# Wisdom\n\nPrefer durable signals.", encoding="utf-8")
139 + prompt_template.write_text("{{WISDOM}}\n{{QUALITY_TREND}}", encoding="utf-8")
140 +
141 + response = _FakeHTTPResponse(
142 + json.dumps({"choices": [{"message": {"content": "# Reskill Report\n"}}]}).encode("utf-8")
143 + )
144 +
145 + with mock.patch.dict("os.environ", {"GITHUB_TOKEN": "token"}, clear=False), mock.patch.object(
146 + reskill.request, "urlopen", return_value=response
147 + ):
148 + exit_code = reskill.main(
149 + [
150 + "--current-datetime",
151 + "2026-05-18T15:22:25.067+02:00",
152 + "--prompt-template",
153 + str(prompt_template),
154 + "--analyzed-dir",
155 + str(analyzed_dir),
156 + "--snapshots-dir",
157 + str(snapshots_dir),
158 + "--wisdom-file",
159 + str(wisdom_path),
160 + "--skills-dir",
161 + str(skills_dir),
162 + "--output",
163 + str(output_path),
164 + "--prompt-output",
165 + str(prompt_output_path),
166 + ]
167 + )
168 +
169 + self.assertEqual(exit_code, 0)
170 + self.assertTrue(prompt_output_path.exists())
171 + self.assertIn("Prefer durable signals.", prompt_output_path.read_text(encoding="utf-8"))
172 +
173
174 if __name__ == "__main__":
175 unittest.main()
tests/test_track_token_usage.py new
+91
@@ -0,0 +1,91 @@
1 +import json
2 +import tempfile
3 +import unittest
4 +from pathlib import Path
5 +
6 +import scripts.track_token_usage as track_token_usage
7 +
8 +
9 +class TrackTokenUsageTests(unittest.TestCase):
10 + def test_main_estimates_tokens_and_appends_record(self) -> None:
11 + tests_root = Path(__file__).resolve().parent
12 + with tempfile.TemporaryDirectory(dir=tests_root) as tmpdir:
13 + base = Path(tmpdir)
14 + prompt_path = base / "prompt.txt"
15 + output_path = base / "output.md"
16 + usage_file = base / "data" / "metrics" / "token-usage.jsonl"
17 + prompt_path.write_text("x" * 40, encoding="utf-8")
18 + output_path.write_text("y" * 20, encoding="utf-8")
19 +
20 + exit_code = track_token_usage.main(
21 + [
22 + "--stage",
23 + "analysis",
24 + "--source",
25 + "copilot-cli",
26 + "--model",
27 + "claude-sonnet-4",
28 + "--current-datetime",
29 + "2026-05-19T08:00:00Z",
30 + "--week",
31 + "2026-W21",
32 + "--prompt-file",
33 + str(prompt_path),
34 + "--output-file",
35 + str(output_path),
36 + "--usage-file",
37 + str(usage_file),
38 + ]
39 + )
40 +
41 + self.assertEqual(exit_code, 0)
42 + records = [json.loads(line) for line in usage_file.read_text(encoding="utf-8").splitlines() if line.strip()]
43 + self.assertEqual(len(records), 1)
44 + record = records[0]
45 + self.assertEqual(record["stage"], "analysis")
46 + self.assertEqual(record["source"], "copilot-cli")
47 + self.assertEqual(record["model"], "claude-sonnet-4")
48 + self.assertEqual(record["week"], "2026-W21")
49 + self.assertEqual(record["input_tokens"], 10)
50 + self.assertEqual(record["output_tokens"], 5)
51 + self.assertEqual(record["total_tokens"], 15)
52 + self.assertEqual(record["cost_usd"], 0.000105)
53 + self.assertTrue(record["estimated"])
54 +
55 + def test_main_uses_explicit_tokens_when_provided(self) -> None:
56 + tests_root = Path(__file__).resolve().parent
57 + with tempfile.TemporaryDirectory(dir=tests_root) as tmpdir:
58 + base = Path(tmpdir)
59 + usage_file = base / "token-usage.jsonl"
60 +
61 + exit_code = track_token_usage.main(
62 + [
63 + "--stage",
64 + "reskill",
65 + "--source",
66 + "github-models",
67 + "--model",
68 + "openai/gpt-4.1",
69 + "--current-datetime",
70 + "2026-05-19T08:00:00Z",
71 + "--input-tokens",
72 + "1000",
73 + "--output-tokens",
74 + "250",
75 + "--usage-file",
76 + str(usage_file),
77 + ]
78 + )
79 +
80 + self.assertEqual(exit_code, 0)
81 + record = json.loads(usage_file.read_text(encoding="utf-8").strip())
82 + self.assertEqual(record["input_tokens"], 1000)
83 + self.assertEqual(record["output_tokens"], 250)
84 + self.assertEqual(record["total_tokens"], 1250)
85 + self.assertEqual(record["week"], "2026-W21")
86 + self.assertEqual(record["cost_usd"], 0.004)
87 + self.assertFalse(record["estimated"])
88 +
89 +
90 +if __name__ == "__main__":
91 + unittest.main()