做什么
路由在不扫描文件的情况下对本地的Codex或Claude Code meter进行上下文检查,只有在明确要求时才进行有界限的静态组件审计. 将剩余容量或安装的上下文间接费用用于上下文预算请求.
技能库 智客分类:Agent 工作流 context-budget
路由在不扫描文件的情况下对本地的Codex或Claude Code meter进行上下文检查,只有在明确要求时才进行有界限的静态组件审计. 将剩余容量或安装的上下文间接费用用于上下文预算请求.
官方网址:skills.sh
先看中文介绍;官方 description 原文单独保留,不改写 SKILL.md。
路由在不扫描文件的情况下对本地的Codex或Claude Code meter进行上下文检查,只有在明确要求时才进行有界限的静态组件审计. 将剩余容量或安装的上下文间接费用用于上下文预算请求.
官方 description 未单独写出 Use when。按规范,代理会在用户任务与这段 description 的关键词匹配时激活本技能。
按 Agent Skills 渐进披露:启动时只加载 name 与 description(约 100 token);任务匹配后才读入整份 SKILL.md 正文;scripts/、references/、assets/ 仅在需要时再读。 本文件正文结构:Context Budget、Default: Live Context、Explicit Static Audit、Audit Output、Optional Selected-Record Diagnostic。
文件分析:这是一份仅含 SKILL.md 的指令型技能,代理激活后整份正文进入上下文。
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.
Context BudgetDefault: Live ContextExplicit Static AuditAudit OutputOptional Selected-Record Diagnostic
来源分类:skills.sh agent-skill
namecontext-budgetdescription具体调用语法与可用工具以目标 Agent 客户端为准。 查看调用机制说明 ↗
先选择目标 Agent 和安装范围,保留技能包的附属文件,安装后检查客户端能否发现该技能。
复制安装指令给支持 Agent Skills 的代理,确认其中的目标目录与客户端匹配。
把 Agent Skill「context-budget」安装到我的项目:SKILL.md 原文与官方 description 见 https://zicq.com/zh/skills/skl-a95bba7ce2f50778-Context-Budget.html 请存为 .cursor/skills/context-budget/SKILL.md 或 .claude/skills/context-budget/SKILL.md,frontmatter 的 name 与 description 保持原样,不要改写。
需要 Node.js 与 npx。先查看仓库技能列表,确认实际名称。
npx skills add 'https://github.com/affaan-m/ecc' --list
npx skills add 'https://github.com/affaan-m/ecc' --skill 'context-budget'
CLI 会交互选择目标 Agent,默认安装到项目;用户级安装使用 -g。先通过查看命令核对仓库内容,再用 npx skills list 检查已安装技能。
Use the provider's native meter for live session usage. Keep the separate, estimated installation audit behind an explicit opt-in.
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:
Route by the active harness:
| Harness | Native command | Guidance |
| --- | --- | --- |
| Codex CLI | /status | Shows current token usage and remaining context capacity. Use /statusline and enable Context stats plus Token counters for a persistent footer meter. |
| Codex app | /status | Shows current thread context usage and rate limits. |
| Claude Code | /context | Shows the live context breakdown. Use /context all for expanded items. |
| Other or unknown | None verified | State 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.”
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:
Audit only the component metadata relevant to eager discovery:
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.
Return a compact report containing:
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.
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).
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