Clawdoc
/install clawdoc
clawdoc
Examine agent sessions. Diagnose failures. Prescribe fixes.
Invocation modes
/clawdoc (slash command — default: headline mode)
Produces a compact, tweetable health check:
🩻 clawdoc — 3 findings across 12 sessions (last 7 days)
💸 $47.20 spent — $31.60 was waste (67% recoverable)
🔴 Retry loop on exec burned $18.40 in one session
🟡 Opus running 34 heartbeats ($8.20 → $0.12 on Haiku)
🟡 SOUL.md is 9,200 tokens — 14% of your context window
Run: bash {baseDir}/scripts/headline.sh ~/.openclaw/agents/main/sessions
/clawdoc full or "give me a full diagnosis"
Runs all 14 pattern detectors and produces the complete diagnosis report with evidence and prescriptions.
/clawdoc brief or "clawdoc one-liner for daily brief"
Single-line summary for morning cron integration:
Yesterday: 8 sessions, $3.40, 1 warning (cron context growth on daily-report)
Run: bash {baseDir}/scripts/headline.sh --brief ~/.openclaw/agents/main/sessions
Natural language triggers
Also activates when user says: "what went wrong", "why did that fail", "debug", "diagnose", "why was that so expensive", "where are my tokens going", "cost breakdown", "health check", "check my agent", "what's wrong", "examine"
Quick examination — most recent session
Find the most recent session file and run:
bash {baseDir}/scripts/examine.sh \x3Csession.jsonl>
This outputs a JSON summary with turns, cost, token counts, tool call frequency, and error count.
Single-session diagnosis
Run all 14 pattern detectors against a specific session file:
bash {baseDir}/scripts/diagnose.sh \x3Csession.jsonl> | jq .
Diagnosis with prescriptions
Pipe diagnose output into prescribe for a formatted report with fix recommendations:
bash {baseDir}/scripts/diagnose.sh \x3Csession.jsonl> | bash {baseDir}/scripts/prescribe.sh
Cost breakdown
Show per-turn cost waterfall for a session:
bash {baseDir}/scripts/cost-waterfall.sh \x3Csession.jsonl> | jq '.[0:5]'
Cross-session pattern recurrence
Analyze pattern recurrence across multiple sessions in a directory:
bash {baseDir}/scripts/history.sh \x3Csessions-dir> | jq .
Full diagnosis
When the user wants a comprehensive diagnosis, run the scripts above and synthesize findings into this report format:
Diagnosis report format
## 🩻 Diagnosis — [date]
### Patient summary
- Sessions examined: N
- Period: [date range]
- Total spend: $X.XX
- Total tokens: XXk in / XXk out
### Findings
#### 🔴 Critical
[Infinite retry loops, context exhaustion, tool-as-text failures]
Each finding includes: what happened, evidence, estimated cost impact, and specific prescription.
#### 🟡 Warning
[Cost spikes, model routing waste, cron accumulation, compaction damage, workspace overhead]
#### 🟢 Healthy
[What's working well — efficient sessions, good model routing]
### Prescriptions (ranked by cost impact)
1. [Highest-impact fix with specific config change or command]
2. [Second highest]
3. [Third]
### Cost breakdown
[Per-day costs for the examination period]
[Top 3 most expensive sessions with root cause]
Pattern reference
| # | Pattern | Severity | Key indicator |
|---|---|---|---|
| 1 | Infinite retry loop | 🔴 Critical | Same tool called 5+ times consecutively |
| 2 | Non-retryable error retried | 🔴 High | Validation error → identical retry |
| 3 | Tool calls as text | 🔴 High | Tool names in assistant text, no toolCall blocks |
| 4 | Context window exhaustion | 🟡-🔴 | inputTokens > 70% of contextTokens |
| 5 | Sub-agent replay | 🟡 Medium | Duplicate completion messages in parent |
| 6 | Cost spike | 🟡-🔴 | Session cost > 2x rolling average |
| 7 | Skill selection miss | 🟢 Low | "command not found" after skill activation |
| 8 | Model routing waste | 🟡 Medium | Premium model on heartbeat/cron |
| 9 | Cron context accumulation | 🟡 Medium | Growing inputTokens across cron runs |
| 10 | Compaction damage | 🟡 Medium | Post-compaction tool call repetition |
| 11 | Workspace token overhead | 🟡 Medium | Baseline > 15% of context window |
| 12 | Task drift | 🟡 Medium | Post-compaction directory divergence or 10+ reads without edits |
| 13 | Unbounded walk | 🟠 High | Repeated unscoped find/grep -r flooding output |
| 14 | Tool misuse | 🟡 Medium | Same file read 3+ times without edit, or identical search repeated |
Self-improving-agent integration
To enable writing findings to .learnings/LEARNINGS.md, set CLAWDOC_LEARNINGS=1 before running prescribe:
CLAWDOC_LEARNINGS=1 bash {baseDir}/scripts/diagnose.sh \x3Csession.jsonl> | bash {baseDir}/scripts/prescribe.sh
Tips
- Session JSONL files are the ground truth for all diagnostics
- Use
jq -s(slurp) for aggregations across all lines in a session file - Filter
message.content[]bytype=="text"for readable content,type=="toolCall"for tool invocations - When prescribing config changes, always show the exact JSON path and value
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install clawdoc - 安装完成后,直接呼叫该 Skill 的名称或使用
/clawdoc触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Clawdoc 是什么?
Diagnose OpenClaw agent failures, cost spikes, and performance issues with 14 pattern detectors. Use when: task failed unexpectedly, costs seem high, agent b... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 193 次。
如何安装 Clawdoc?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install clawdoc」即可一键安装,无需额外配置。
Clawdoc 是免费的吗?
是的,Clawdoc 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Clawdoc 支持哪些平台?
Clawdoc 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Clawdoc?
由 Ashish Jain(@ashishjaingithub)开发并维护,当前版本 v0.12.0。