Best fit
- The user asks "how much have I spent?", "what did this session cost?", or
- The user mentions budgets, spending limits, overruns, or cost controls.
- The user wants a cost breakdown by model, session, or date, or a CSV export.
affaan-m/ECC
Track and report Claude Code token usage, spending, and budgets from the local ECC cost-tracker metrics log. Use when the user asks about costs, spending, usage, tokens, budgets, or cost breakdowns by model, session, or date.
npx skills add https://github.com/affaan-m/ECC --skill "skills/cost-tracking"Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
According to the pinned SKILL.md from affaan-m/ECC: Use this skill to analyze Claude Code cost and usage history from the metrics log that ECC's stop:cost-tracker hook writes.
npx skills add https://github.com/affaan-m/ECC --skill "skills/cost-tracking"Best fit
Bring this context
Expected outputs
Key source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
The tracker appends one JSON object per session-stop to /.claude/metrics/costs.jsonl. Each row is a cumulative snapshot for that session, so to total spend you take the latest row per sessionid and sum across sessions — summing every row multiply-counts.
The user asks "how much have I spent?", "what did this session cost?", or
First verify the log exists (use node, not sqlite3 — the tracker writes JSONL, and node is cross-platform):
For a session drilldown or CSV export, iterate the same latest set (or the raw rows for CSV) and print the fields you need.
When presenting cost data, include today's spend vs yesterday, total across all sessions, a by-model breakdown, and session count. Format sub-dollar amounts with four decimals, larger amounts with two.
SkillSignal prompt templates
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use cost-tracking to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned cost-tracking source to [task]. Pay particular attention to these source sections: “Where the data lives”, “When to Use”, “How It Works”, “Example — summary, by model, last 7 days”, “Reporting Guidance”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current cost-tracking result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
The task matches the purpose documented in the SKILL.md.
The source section “Where the data lives” has been checked.
The source section “When to Use” has been checked.
The source section “How It Works” has been checked.
The source section “Example — summary, by model, last 7 days” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
ローカルのコスト追跡データベースからClaude Codeのトークン使用量、支出、予算を追跡・レポートします。コスト、支出、使用量、トークン、予算、またはプロジェクト、ツール、セッション、日付によるコスト内訳について質問する場合に使用します。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailAudit skill SKILL.md files for compliance with the agentskills.io specification and house conventions. Checks frontmatter fields (name, description, compatibility, metadata, argument-hint), metadata sub-fields (author, scope, layer, confirms), and layer/suffix consistency. Use when adding new skills, reviewing skill quality, or ensuring all skills follow the spec. Triggers: "audit skills", "check skill spec", "skill compliance", "are my skills up to spec", "/claude-skill-spec-audit".
A separate implementation from jackchuka/skills; compare its source, maintenance signals, and permission requirements.
Open source detailPatterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
Use this skill to analyze Claude Code cost and usage history from the metrics log that ECC's stop:cost-tracker hook writes.
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/cost-tracking". Inspect the command and pinned source before running it.
claude code
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Compare before choosing
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
ローカルのコスト追跡データベースからClaude Codeのトークン使用量、支出、予算を追跡・レポートします。コスト、支出、使用量、トークン、予算、またはプロジェクト、ツール、セッション、日付によるコスト内訳について質問する場合に使用します。
Audit skill SKILL.md files for compliance with the agentskills.io specification and house conventions. Checks frontmatter fields (name, description, compatibility, metadata, argument-hint), metadata sub-fields (author, scope, layer, confirms), and layer/suffix consistency. Use when adding new skills, reviewing skill quality, or ensuring all skills follow the spec. Triggers: "audit skills", "check skill spec", "skill compliance", "are my skills up to spec", "/claude-skill-spec-audit".
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
Codified expertise for quality control, non-conformance investigation, root cause analysis, corrective action, and supplier quality management in regulated manufacturing. Informed by quality engineers with 15+ years experience across FDA, IATF 16949, and AS9100 environments. Includes NCR lifecycle management, CAPA systems, SPC interpretation, and audit methodology. Use when investigating non-conformances, performing root cause analysis, managing CAPAs, interpreting SPC data, or handling supplier
Interactive installer for Everything Claude Code — guides users through selecting and installing skills and rules to user-level or project-level directories, verifies paths, and optionally optimizes installed files.
Use this skill to analyze Claude Code cost and usage history from the metrics log
that ECC's stop:cost-tracker hook writes.
The tracker appends one JSON object per session-stop to
~/.claude/metrics/costs.jsonl. Each row is a cumulative snapshot for that
session, so to total spend you take the latest row per session_id and
sum across sessions — summing every row multiply-counts.
Row schema:
| Field | Meaning |
|---|---|
timestamp | ISO timestamp of the snapshot |
session_id | Claude Code session identifier |
transcript_path | Path to the session transcript |
model | Model used |
input_tokens / output_tokens | Token counts |
cache_write_tokens / cache_read_tokens | Prompt-cache token counts |
estimated_cost_usd | Precomputed cumulative cost in USD for the session |
Prefer estimated_cost_usd over hand-calculating pricing — model and cache
prices change, and the tracker is the source of truth.
First verify the log exists (use node, not sqlite3 — the tracker writes
JSONL, and node is cross-platform):
node -e 'const fs=require("fs"),os=require("os"),p=require("path");const f=p.join(os.homedir(),".claude","metrics","costs.jsonl");console.log(fs.existsSync(f)?"cost log found":"cost log not found: "+f)'
If the log is missing, do not fabricate usage data. Tell the user that cost
tracking populates after the first session ends with the stop:cost-tracker
hook enabled.
node -e '
const fs=require("fs"),os=require("os"),path=require("path");
const f=path.join(os.homedir(),".claude","metrics","costs.jsonl");
if(!fs.existsSync(f)){console.log("cost log not found: "+f);process.exit(0);}
const rows=fs.readFileSync(f,"utf8").split(/\r?\n/).filter(Boolean).map(l=>{try{return JSON.parse(l)}catch{return null}}).filter(Boolean);
const bySession=new Map();
for(const r of rows){const k=r.session_id||r.transcript_path||r.timestamp;const p=bySession.get(k);if(!p||String(r.timestamp)>String(p.timestamp))bySession.set(k,r);}
const latest=[...bySession.values()];
const cost=r=>Number(r.estimated_cost_usd)||0, day=r=>String(r.timestamp||"").slice(0,10), sum=a=>a.reduce((s,r)=>s+cost(r),0), f4=n=>"$"+n.toFixed(4);
const today=new Date().toISOString().slice(0,10), yest=new Date(Date.now()-864e5).toISOString().slice(0,10);
console.log("today: "+f4(sum(latest.filter(r=>day(r)===today)))+" | yesterday: "+f4(sum(latest.filter(r=>day(r)===yest)))+" | total: "+f4(sum(latest))+" ("+latest.length+" sessions)");
const m=new Map();for(const r of latest){const k=r.model||"(unknown)";m.set(k,(m.get(k)||0)+cost(r));}
console.log("by model:");[...m.entries()].sort((a,b)=>b[1]-a[1]).forEach(([k,v])=>console.log(" "+f4(v)+" "+k));
'
For a session drilldown or CSV export, iterate the same latest set (or the raw
rows for CSV) and print the fields you need.
When presenting cost data, include today's spend vs yesterday, total across all sessions, a by-model breakdown, and session count. Format sub-dollar amounts with four decimals, larger amounts with two.
session_id first.estimated_cost_usd is present./cost-report - Command-form report over the same metrics log.cost-aware-llm-pipeline - Model-routing and budget-design patterns.token-budget-advisor - Context and token-budget planning.strategic-compact - Context compaction to reduce repeated token spend.