Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/kcnyu/clawock/openclawnpx skills add KCNyu/clawock --skill openclawgit clone --depth 1 https://github.com/KCNyu/clawockWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kcnyu/clawock/openclaw)<a href="https://agentmods.dev/skills/kcnyu/clawock/openclaw"><img src="https://agentmods.dev/badge/skills/kcnyu/clawock/openclaw.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00036 | $0.01351 |
| Opus 5 | $0.00018 | $0.00675 |
| Sonnet 5 | $0.00007 | $0.00270 |
| Haiku 4.5 | $0.00004 | $0.00135 |
Grade A, and why
investment-decision scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
clawock investment-decision
You are the model side of a clawock decision run. Your runtime (OpenClaw) owns conversation, memory and tools; clawock owns the decision contract. Your entire job is one file in, one file out.
When to run
A run prepare has written a request file (the request_file path it
printed, e.g. .clawock/work/<run_id>/request.json). Open it first; it tells
you. On a fresh machine the workspace must be created once first:
clawock init book --workflow investment-decision (see ../../README.md
— "From zero").
task— the decision contract (evidence, opposing case, bounded action, reconciled amounts)context.documents— the certified context, with per-file sha256 fingerprintsworkflow.parameters— gates likemin_supporting_evidence,min_opposing_evidence,max_confidence_without_primary_source
What to produce
Write decision.json at the workspace root — artifact paths are resolved
against the workspace root, not the request directory. The schema is the
package's investment-decision workflow; when in doubt, keep the fields
minimal and explicit:
{
"schema_version": 1,
"workflow": {"id": "investment-decision", "version": "1.1.0"},
"decision_id": "example-2026-08-08",
"as_of": "2026-08-08T08:00:00+00:00",
"subject": {"ticker": "EXAMPLE", "market": "US", "currency": "USD"},
"evidence": [
{"id": "filing-growth", "stance": "supporting",
"summary": "The latest filed revenue figure grew year over year.",
"source": "issuer filing", "source_class": "primary",
"observed_at": "2026-08-08T07:30:00+00:00"},
{"id": "valuation-risk", "stance": "opposing",
"summary": "The current market multiple is above its stated range.",
"source": "market data snapshot", "source_class": "market",
"observed_at": "2026-08-08T07:45:00+00:00"}
],
"debate": {
"bull_case": {"summary": "Filed growth supports continued monitoring.",
"evidence_ids": ["filing-growth"]},
"bear_case": {"summary": "Valuation leaves insufficient margin of safety.",
"evidence_ids": ["valuation-risk"]}
},
"thesis": {
"statement": "Momentum is constructive, but price does not compensate for valuation risk.",
"confidence": 0.7,
"invalidation_conditions": ["Filed growth reverses"]
},
"decision": {
"action": "watch",
"rationale": "The opposing valuation evidence blocks an entry.",
"evidence_ids": ["filing-growth", "valuation-risk"],
"order": null
}
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 4d ago First seen · 120 lines · 36 tokens per session scan A fdae32279755
investment-decision is a skill published in the GitHub repository KCNyu/clawock (14 stars, last pushed today), licensed MIT. It adds 36 tokens to every session and 1,351 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
company-research
A 股个股研究六阶段 SOP(profile → financials → estimates → valuation → risk → report),Phase 0 范围 = 财务估值闭环。当任务是研究 / 分析 / 评估一只或多只已指定代码的 A 股个股时使用;规定每阶段取哪些数据、调哪些 calc 函数、必须落盘什么产物、过什么 Gate。不用于:从市场中筛选标的、泛行业讨论、概念解释、给投资动作建议。.
data-access
A 股零鉴权取数手册。当需要真实的行情 / 市值 / 估值快照、季度报告期累计财务数据、机构一致预期 EPS、PE 历史序列、公告标题、日 K 线、交易日历时使用;只允许运行本 skill 登记的脚本取数(腾讯 / 新浪 / 同花顺 / baostock / 深交所 / 东财),禁止凭模型记忆给数,禁止自造爬虫。概念解释、观点讨论等不需要取数的话题不要加载。.
industry-chain
产业链下钻与不可替代性判定方法:以龙头为"需求入口"沿供应链逐层下钻(整机 / 龙头 → 部件 → 核心器件 → 材料 → 衬底与设备),用物理 / 材料约束(扩产周期、良率、认证周期、有无替代)当筛子找供给刚性的卡口;给每个标的贴不可替代性标签(techmoat / capacitymoat / both / 待补)并列证据;含"卡口越硬越贵"与预期差四问的校准。当任务涉及产业链位置、上下游、护城河、不可替代性、供给瓶颈、竞争格局时加载;单纯取数、估值计算、财报拆分等不涉及产业链结构的任务不要加载。只产出框架与证据表,不给投资动作建议。.
catalyst-risk
催化剂与风险的反证式写法:每个强结论必须先找反证;催化剂按"兑现型 / 预期型 / 周期型"分类并要求可验证的数据时点;风险按技术路线断层、客户集中、产能过剩与价格战、周期顶、预期透支(假便宜 PEG)、一致预期下修、治理与流动性、数据源冲突分类;裁决点的标准写法(什么数据出来会改变判断 + 下一个公开数据时点);知识档案旧结论的反证处理。当任务涉及风险、反证、催化剂、裁决点、预期兑现、什么会推翻结论时加载;单纯取数、估值计算、财报拆分等不需要反证框架的任务不要加载。只产出框架、概率与裁决点,不给投资动作建议。.
earnings-analysis
财报拆解手册:报告期累计值 → 单季(quarterize)→ 最新单季 / TTM / TTM 同比 / 环比的口径地图,扣非与归母的取舍(一次性损益),季节性与报告期对齐(分子分母同期),三表交叉核对(利润表 / 资产负债表 / 现金流量表),比率(毛利率 / 费用率 / 负债率)一律经 calc ratio,"转向看最新期、规模看 TTM"的判读模板与质量检查清单。当任务涉及财报、季报、业绩、利润拆分、同比环比、毛利率、现金流、扣非时加载;只查行情 / 公告 / 产业链结构、或只讨论概念不涉及财务数字的任务不要加载。取数层不做任何算术,所有派生数字出自 calc;不给投资动作建议。.
valuation
成长股估值口径手册(A 股为主,US/HK 通用):扣非×4 年化 PE、前瞻 PE、TTM PE 历史分位、PEG(扣非×4 PE ÷ 前瞻 CAGR)、前瞻 CAGR 与 TTM 同比交叉验证、一致预期分歧、四锚 PE 消化年数与"30 倍锚三铁律"、判读规则与常见错误。当任务涉及估值、PE、PEG、贵不贵、能不能消化、历史分位、一致预期时加载;只讨论概念、与估值无关的取数 / 行情 / 公告问题不要加载。所有数字一律经 calc/ 计算,本 skill 只管口径与判读,不给价格锚、不给投资动作建议。.