tcx-memory

A skill for reviewing past investment decisions and forecasts, including what evidence was available at a past date and what happened afterward. A replay freezes the information available at that time.

In plain words
What is it for?
It is for retrieving prior decisions, replaying historical reasoning, reviewing outcomes, and validating proposed lessons against past and later evidence.
Why use it?
It helps distinguish the quality of the original decision process from the eventual financial result and test whether lessons hold up over time.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/monarchjuno/tradingcodex/tcx-memory
Any agent
npx skills add monarchjuno/tradingcodex --skill tcx-memory
Clone the repo
git clone --depth 1 https://github.com/monarchjuno/tradingcodex

Made for: Claude Code, Codex.

Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,462 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 2d ago against content hash cfaf08319b92, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

workspace_templates/modules/repo-skills/files/.agents/skills/tcx-memory/SKILL.md · 124 lines

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

  1. Identify the subject, decision or forecast id when known, time cutoff, evidence origin, and selected strategy snapshot. Use no_strategy when 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, and get_forecast_calibration_report.
  2. 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 memory block: cutoff, initial view, canonical Judgment/Postmortem/Lesson refs, and a delta direction of unchanged, strengthened, weakened, or reversed with its reason. Omit the block entirely when Memory was not used; the receipt seals exact hashes.
  3. For historical replay, reject sources whose known_at exceeds 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 through create_research_spec and create_replay_manifest when 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.
  4. Freeze forecasts and invalidation conditions before outcomes are visible. Use the role-bound issue_forecast, revise_forecast, resolve_forecast, and score_forecast MCP tools for the append-only lifecycle. Dispatch the registered independent reviewer for resolution when required by the tool contract.
  5. 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 remains evidence_only.
  6. 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.
  7. 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.
  8. Label lesson state as candidate, corroborated, validated, or retired. Record evidence origin separately as historical_replay, historical_holdout, or live_forward.
  9. 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_lesson MCP 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.

Read the full file on GitHub · 124 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 124 lines · 63 tokens per session scan A cfaf08319b92

Subscribe to this mod's changes

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.

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