依据WCAG 2.2设计和检查Web、iOS及Android界面,核查读屏标签、键盘操作、配色对比度和操作目标尺寸,使界面支持辅助技术。
- 为纯图标按钮添加说明
- 检查键盘操作与模态框焦点
- 检查对比度与操作目标尺寸
检查Claude Code加载的智能体、技能、规则和MCP配置所占的信息量,估算词元数,找出过长或重复的定义,并按预计可削减的量为改进建议排序。
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.
检查Claude Code加载的各类配置包含多少信息,整理占用上下文的主要因素;上下文是模型可参考的信息范围。针对智能体、技能、规则、MCP工具定义和CLAUDE.md估算词元数,查找过长文件及重复内容。按必要程度对组件分类,再根据预计可削减的量为改进建议排序。
适合上下文很快占满,或添加大量技能、MCP服务器后检查配置。继续添加组件前,也可评估现有负担和新增部分的预计负担。详细输出可查看各文件的占用明细及重复位置。
/context-budget检查配置的信息量。”/context-budget --verbose显示大文件的占用明细。”词元数和工具定义的开销均为估算值。可选的JSONL诊断使用Python 3,需要明确选择一个状态稳定的普通文件。已保存文件的字节数不代表当前上下文用量或费用。查看当前值时,请使用适合所用版本的/context或/usage。
本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。
AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。
总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。
安装前可以查看交给 AI 代理的指令内容。
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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依据WCAG 2.2设计和检查Web、iOS及Android界面,核查读屏标签、键盘操作、配色对比度和操作目标尺寸,使界面支持辅助技术。
从指令、记忆、工具执行和界面显示等十二个层面排查人工智能代理故障,依据代码和日志梳理原因,按严重程度列出问题并提出修复方案。
用实际开发任务比较多个编程智能体,记录成功率、可获取的API费用、耗时和重复运行的稳定性,为工具选型及更新后的性能评估提供数据。
设计和调整AI智能体使用的工具、输入与返回格式,明确出错后的恢复、重试和停止条件,管理上下文信息量,并以完成率、重试次数和成功任务成本评估改进。
在AI代理反复失败或重复操作时,记录错误与环境前提、排查原因,采取影响有限的恢复操作,并汇总结果和证据,为有依据的重试与交接提供支持。
根据错误信息和执行环境,分析AI代理反复失败或重复操作的原因。通过小范围、可撤销的恢复操作检验假设,并将结果与依据整理成可供交接的报告。