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 davidromeo/tradeblocks-skills --skill dc-analysisgit clone --depth 1 https://github.com/davidromeo/tradeblocks-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/davidromeo/tradeblocks-skills/dc-analysis)<a href="https://agentmods.dev/skills/davidromeo/tradeblocks-skills/dc-analysis"><img src="https://agentmods.dev/badge/skills/davidromeo/tradeblocks-skills/dc-analysis/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/davidromeo/tradeblocks-skills/dc-analysis"><img src="https://agentmods.dev/badge/skills/davidromeo/tradeblocks-skills/dc-analysis.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 | $0.00080 | $0.03668 |
| Opus 5 | $0.00040 | $0.01834 |
| Sonnet 5 | $0.00016 | $0.00734 |
| Haiku 4.5 | $0.00008 | $0.00367 |
Grade A, and why
dc-analysis 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 10d 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 — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Double Calendar Analysis
Comprehensive health check for double calendar strategies. Each DC responds differently to filters and exits depending on its DTE spread, delta selection, and underlying. This skill surfaces those differences.
Prerequisites
- TradeBlocks MCP server running
- Block with DC trade data loaded
- Strategy profile recommended (will prompt to create if missing)
- Market data (SPX daily + VIX context) for regime analysis
Process
Step 1: Select Block and Load Profile
- Ask which DC to analyze. Use
list_blocksif needed. - Check for a profile. Call
get_strategy_profilewith the block and strategy name.- If profile exists: load it and summarize the structure (DTE spread, deltas, entry/exit rules, underlying).
- If no profile: ask the user for the OO settings (screenshots work) and create one via
profile_strategy. Key fields needed:- Underlying, DTE spread (short/long), put/call deltas
- Entry filters (day, time, S/L ratio min, VIX, RSI)
- Exit rules (profit target, time exit, S/L ratio exit, delta exits)
- Position sizing (allocation %)
Display the profile summary before continuing:
Structure: [underlying] [short DTE]/[long DTE] DC, [put delta]/[call delta] delta
Entry: [day], [time], [filters]
Exits: [list exit rules]
Sizing: [allocation]%
Step 2: Baseline Performance
Run get_statistics for the block.
Present the core metrics:
| Metric | Value | Context |
|---|---|---|
| Win Rate | >60% typical for DCs | |
| Profit Factor | >2.0 strong | |
| Sharpe | >3.0 strong for DCs | |
| Max Drawdown | <15% good | |
| Avg Win / Avg Loss | Payoff ratio | |
| Trade Count | <100 = thin data warning |
Step 3: Exit Attribution
Run get_performance_charts with charts: ["exit_reason_breakdown"].
This is critical for DCs. Build a table:
| Exit Type | Count | Avg P&L | Total P&L | Verdict |
|---|---|---|---|---|
| Time exit | Money maker or money loser? | |||
| S/L ratio | Primary stop or profit engine? | |||
| Delta exit (above) | How much damage? | |||
| Delta exit (below) | How much damage? | |||
| Profit target | Capturing enough? | |||
| Expired | Good or bad for this DC? |
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.
- 10d ago First seen · 310 lines · 80 tokens per session scan A a79ddb71219e
dc-analysis is a skill published in the GitHub repository davidromeo/tradeblocks-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 80 tokens to every session and 3,668 once invoked, about $0.0004 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 2026-08-31.
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