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 skills add mphinance/alpha-skills --skill trader-memory-coregit clone --depth 1 https://github.com/mphinance/alpha-skillsWrote 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/mphinance/alpha-skills/trader-memory-core)<a href="https://agentmods.dev/skills/mphinance/alpha-skills/trader-memory-core"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/trader-memory-core/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mphinance/alpha-skills/trader-memory-core"><img src="https://agentmods.dev/badge/skills/mphinance/alpha-skills/trader-memory-core.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00096 | $0.01353 |
| Opus 5.5 | $0.00038 | $0.00541 |
| Sonnet 5.5 | $0.00019 | $0.00271 |
| Haiku 4.5 | $0.00010 | $0.00135 |
Grade A, and why
trader-memory-core 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 1mo 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trader Memory Core
Overview
Persistent state layer that bundles screening → analysis → position sizing → portfolio management outputs into a single "thesis object" per investment idea. Tracks what you thought, what happened, and what you learned — across conversations.
Phase 1 supports single-ticker theses: dividend_income, growth_momentum, mean_reversion, earnings_drift, pivot_breakout.
When to Use
- After a screener (kanchi, earnings-trade-analyzer, vcp, pead, canslim, edge-candidate-agent) produces candidates
- When transitioning a thesis from IDEA → ENTRY_READY → ACTIVE → CLOSED
- When attaching position-sizer output to a thesis
- When checking which theses are due for review
- When closing a position and generating a postmortem with lessons learned
Prerequisites
- Python 3.10+
pyyaml(already in project dependencies)- FMP API key (optional, only for MAE/MFE calculation in postmortem)
Workflow
1. Register — Ingest screener output as thesis
Read the screener's JSON output and convert to thesis using the appropriate adapter.
python3 skills/trader-memory-core/scripts/thesis_ingest.py \
--source kanchi-dividend-sop \
--input reports/kanchi_entry_signals_2026-03-14.json \
--state-dir state/theses/
Supported sources: kanchi-dividend-sop, earnings-trade-analyzer, vcp-screener, pead-screener, canslim-screener, edge-candidate-agent.
Each thesis starts in IDEA status.
2. Query — Search and list theses
python3 skills/trader-memory-core/scripts/thesis_store.py \
--state-dir state/theses/ list --ticker AAPL --status ACTIVE
Filter by --ticker, --status, or --type.
3. Update — Transition, attach position, link reports
State transition (IDEA → ENTRY_READY only):
Use thesis_store.transition(state_dir, thesis_id, "ENTRY_READY", reason) from Python.
Open position (ENTRY_READY → ACTIVE):
Use thesis_store.open_position(state_dir, thesis_id, actual_price, actual_date) — the only path to ACTIVE. Accepts optional shares and event_date (for backfilling past trades).
What ships with it
14 files 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.
- assets/postmortem_template.md 971 B
- README.md 5.5 KB
- references/field_mapping.md 2.8 KB
- references/thesis_lifecycle.md 3.0 KB
- schemas/thesis.schema.json 6.7 KB
- scripts/fmp_price_adapter.py 3.3 KB runs code
- scripts/tests/conftest.py 141 B runs code
- scripts/tests/test_fmp_price_adapter.py 2.7 KB runs code
- scripts/tests/test_thesis_ingest.py 9.9 KB runs code
- scripts/tests/test_thesis_review.py 7.8 KB runs code
- scripts/tests/test_thesis_store.py 38 KB runs code
- scripts/thesis_ingest.py 14 KB runs code
- scripts/thesis_review.py 9.1 KB runs code
- scripts/thesis_store.py 40 KB runs code
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.
- 1mo ago First seen · 142 lines · 96 tokens per session scan A e992c6880b27
trader-memory-core is a skill published in the GitHub repository mphinance/alpha-skills (28 stars, last pushed 10d ago), licensed MIT. It adds 96 tokens to every session and 1,353 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-14.
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