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/monarchjuno/tradingcodex/tcx-memorynpx skills add monarchjuno/tradingcodex --skill tcx-memorygit clone --depth 1 https://github.com/monarchjuno/tradingcodexWhat 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 | $0.00063 | $0.01462 |
| Opus 5 | $0.00032 | $0.00731 |
| Sonnet 5 | $0.00013 | $0.00292 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
tcx-memory 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 2d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decision Memory
Use the existing file-native decision packages, research artifacts, source snapshots, replay manifests, forecast ledger, postmortems, and improve records. Treat generated summaries, links, and Wiki-style pages as read views, never as the canonical record.
Choose The Mode
- Retrieve: find relevant prior decisions, forecasts, evidence, outcomes, and lessons. Preserve contrary and retired records; do not return only successful cases.
- Replay: freeze an as-of time and use only source snapshots knowable by that cutoff. Record the ResearchSpec and replay manifest before revealing the outcome.
- Review: compare the frozen decision process with the resolved outcome. Assess process quality before revealing P&L or outcome quality, then keep the two judgments separate.
- Validate: compare a lesson candidate across independent episodes, historical holdout periods, regimes, and live forward evidence.
Procedure
- Identify the subject, decision or forecast id when known, time cutoff,
evidence origin, and selected strategy snapshot. Use
no_strategywhen no strategy applied. Retrieve through the structured read-only MCP tools available to the current role:list_workflow_artifacts,list_research_artifacts,search_research_artifacts,get_research_artifact,list_research_specs,get_research_spec,list_forecasts,get_forecast, andget_forecast_calibration_report. - For a current decision, record the independent initial view before retrieving
similar cases. After retrieval, show what changed and why. When Memory changes
a synthesis, store only the compact
memoryblock: cutoff, initial view, canonical Judgment/Postmortem/Lesson refs, and a delta direction ofunchanged,strengthened,weakened, orreversedwith its reason. Omit the block entirely when Memory was not used; the receipt seals exact hashes. - For historical replay, reject sources whose
known_atexceeds the cutoff. Preserve data vintage, universe membership, delistings, corporate actions, costs, model/prompt/tool hashes, and every attempted hypothesis or parameter trial when applicable. Freeze the plan and manifest throughcreate_research_specandcreate_replay_manifestwhen the current role is authorized; otherwise dispatch the smallest registered role that owns the required MCP tool. Never substitute a shell command or caller-supplied principal. - Freeze forecasts and invalidation conditions before outcomes are visible.
Use the role-bound
issue_forecast,revise_forecast,resolve_forecast, andscore_forecastMCP tools for the append-only lifecycle. Dispatch the registered independent reviewer for resolution when required by the tool contract. - Once an accepted synthesis has future evaluation value, Head Manager records
its immutable JudgmentSnapshot with
record_judgment_snapshot. It freezes the canonical synthesis receipt, run context, cutoff, and forecast refs or forecast block reason. It remainsevidence_only. - Before any outcome is recorded or revealed, reconstruct intent, evidence, alternatives, assumptions, guardrails, and the decision-time process from durable artifacts. Prepare a process-review payload without outcome knowledge, then use the explicit user-terminal handoff below to lock it. Do not invent missing events.
- Only after the process review is locked, record and independently resolve the
outcome. Prepare the second-pass postmortem payload, binding
process_review_id, the sealed DecisionSnapshot, and undisputed forecast outcome events, then hand it to the user for the terminal action below. Store any generalization as a lesson candidate, not as a durable rule. Separate knowledge-base integrity errors, decision process errors, and forecast resolution or calibration errors. - Label lesson state as
candidate,corroborated,validated, orretired. Record evidence origin separately ashistorical_replay,historical_holdout, orlive_forward. - Promote a lesson only after independent contrary-case review and out-of-sample
evidence appropriate to its scope and regime. Dispatch the registered
independent review role; its authenticated review principal must call the
promote_lessonMCP tool. There is no direct CLI promotion path, and a caller-supplied role is not reviewer authentication. Historical replay evidence alone cannot become holdout or live-forward validation.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 124 lines · 63 tokens per session scan A cfaf08319b92
tcx-memory is a skill published in the GitHub repository monarchjuno/tradingcodex (364 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 1,462 once invoked, about $0.0003 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
agent-agentic-payments
Agent skill for agentic-payments - invoke with $agent-agentic-payments.
agent-payments
Agent skill for payments - invoke with $agent-payments.
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。.