본문으로 건너뛰기
cccskills
무료GitHub에 공개

context-budget

Audit Claude Code agents, skills, rules, and MCP tool configurations to estimate how much space they consume in the model's context. Identifies oversized and overlapping components, then ranks recommendations by potential token savings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Routes live context checks to the native Codex or Claude Code meter without scanning files, and performs a bounded static component audit only when explicitly requested. Use for context-budget requests about remaining capacity or installed context overhead.

설치 방법 보기

이럴 때 유용합니다

  • Investigating why context space fills quickly
  • Auditing a setup after adding skills or MCP servers
  • Finding oversized files and duplicated instructions
  • Estimating room before adding more components

포함된 파일(1)

  • SKILL.md6.1 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Context Budget

Use the provider's native meter for live session usage. Keep the separate, estimated installation audit behind an explicit opt-in.

Default: Live Context

Treat an invocation with no audit request as a live-context request. This includes requests for the current window, remaining capacity, headroom, token usage, or a context breakdown.

For this default path:

  • Do not call or use tools.
  • Do not scan or read files.
  • Do not run shell commands, provider CLIs, debug logging, or MCP discovery.
  • Reply with only the native command and one short explanation.

Route by the active harness:

HarnessNative commandGuidance
Codex CLI/statusShows current token usage and remaining context capacity. Use /statusline and enable Context stats plus Token counters for a persistent footer meter.
Codex app/statusShows current thread context usage and rate limits.
Claude Code/contextShows the live context breakdown. Use /context all for expanded items.
Other or unknownNone verifiedState that ECC cannot read live provider state and ask which harness is active.

These commands are documented in the Codex command reference and Claude Code command reference.

A skill cannot invoke a TUI slash command on the user's behalf. Never replace the native command with an estimated scan and never infer an audit request from phrases such as “context is heavy” or “how much is left.”

Explicit Static Audit

Run an installation/configuration audit only when the user explicitly says --audit, “audit installed components,” or equivalent unambiguous wording. --verbose changes report detail; it does not opt into an audit by itself.

If the audit scope is ambiguous, ask for the harness and installation root before reading anything. Keep every audit bounded and read-only:

  1. Inspect only the named plugin/install root and the named harness's known configuration files.
  2. Never recursively enumerate the workspace root, home directory, provider cache, transcripts, debug logs, or unrelated worktrees.
  3. Never launch a provider CLI, MCP server, or debug session to count tools.
  4. Cap inventory output at 50 entries per component class. Summarize the rest.
  5. Do not write temporary logs or copy configuration content into the report.

Audit only the component metadata relevant to eager discovery:

  • agent and skill descriptions;
  • command descriptions when the harness discovers them eagerly;
  • always-loaded instruction files in the explicitly named scope;
  • configured MCP tool schemas when an existing bounded manifest exposes them.

Full skill, agent, command, and rule bodies are generally on-demand content. Report their disk size separately from estimated eager overhead instead of adding every body to the session baseline.

Audit Output

Return a compact report containing:

  • harness and exact paths inspected;
  • estimated eager/discovery overhead by component class;
  • installed counts and the heaviest metadata entries;
  • assumptions, unavailable measurements, and up to three optimizations.

Every calculated token number is an estimate of static configuration overhead, not live session context. Do not claim that an estimate is provider-reported, current, exact, or evidence of realized savings. Direct the user back to the native live-context command for current usage.

Optional Selected-Record Diagnostic

Inspect a persisted record only when the user explicitly selects that file for this diagnostic. This is separate from the default live-context request and the installation audit.

For a local file diagnostic, explicitly select a stable, regular JSONL file or a snapshot you intend to inspect. Replace the example path below; this does not find or reconnect a session. The snippet prints aggregate byte counts only. It reads one line at a time, so memory use depends on the largest record; avoid very large records and actively growing files.

python3 - "/path/to/selected-session.jsonl" <<'EOF'
import json
import sys

total = attachment = other = unclassified = 0
try:
    with open(sys.argv[1], "rb") as source:
        for raw in source:
            size = len(raw)
            total += size
            try:
                record = json.loads(raw.decode("utf-8"))
            except (UnicodeDecodeError, json.JSONDecodeError):
                unclassified += size
                continue
            record_type = record.get("type") if isinstance(record, dict) else None
            if not isinstance(record_type, str):
                unclassified += size
            elif record_type == "attachment":
                attachment += size
            else:
                other += size
except OSError:
    print("Cannot read selected JSONL file.", file=sys.stderr)
    raise SystemExit(1)

pct = attachment * 100.0 / total if total else 0.0
print(f"persisted {total}B | attachment records {attachment}B ({pct:.1f}%) | "
      f"other records {other}B | unclassified {unclassified}B")
EOF

The three categories add up to the original file bytes, including line endings and blank lines. attachment is an exact record-type filter, not a guarantee about a harness's current internal schema. Other records have a different string type; malformed JSON, invalid UTF-8, nonobject values, missing or non-string types, and blank lines are unclassified. Neither category means "conversation," and the percentage is only a share of persisted bytes.

These counts do not establish active context, remaining room, token usage, billing, or what a reconnect loads. For current harness-reported context and usage, use the version-appropriate /context and /usage commands (/cost is an alias).

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC28.3만2026년 10월 16일 업데이트

agent-eval

무료요약 있음AI 평가 80점

실제 개발 과제로 여러 코딩 에이전트를 비교합니다. 성공률, 확인 가능한 API 비용, 소요 시간과 반복 실행의 안정성을 측정해 도입 결정과 업데이트 후 평가에 활용합니다.

  • 실제 개발 과제로 에이전트를 비교합니다
  • 새 도구나 모델을 도입하기 전에 평가합니다
  • 에이전트 업데이트 후 성능을 확인합니다
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC28.3만2026년 10월 5일 업데이트

agent-introspection-debugging

무료요약 있음AI 평가 100점

AI 에이전트의 반복 실패와 도구 사용을 기록하고 원인을 좁힙니다. 영향이 제한된 복구 작업과 결과 보고를 통해 근거 있는 재시도를 돕습니다.

  • 같은 도구 작업을 반복하는 원인 조사
  • 대화 증가에 따른 출력 품질 저하 조사
  • 파일 경로와 환경 불일치 확인
affaan-m/ECC28.3만2026년 10월 16일 업데이트

affaan-m의 스킬 모두 보기

이 스킬의 문제 신고