Cc Sdd New Agent
gotalab/cc-sdd
Add or extend coding-agent support in cc-sdd by executing the SOP in docs/cc-sdd/sop-new-agent.md end-to-end.
Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure.
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-sop --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/kanchi-dividend-sop .claude/skills/kanchi-dividend-sop && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "kanchi-dividend-sop" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sop into .claude/skills/kanchi-dividend-sop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kanchi-dividend-sop", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sopType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-sop --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/kanchi-dividend-sop .agents/skills/kanchi-dividend-sop && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "kanchi-dividend-sop" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sop into .agents/skills/kanchi-dividend-sop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kanchi-dividend-sop", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-sop --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/kanchi-dividend-sop .cursor/skills/kanchi-dividend-sop && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "kanchi-dividend-sop" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sop into .cursor/skills/kanchi-dividend-sop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kanchi-dividend-sop", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/tradermonty/claude-trading-skills.git --path skills/kanchi-dividend-sop--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-sop --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/kanchi-dividend-sop .gemini/skills/kanchi-dividend-sop && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "kanchi-dividend-sop" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sop into .gemini/skills/kanchi-dividend-sop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kanchi-dividend-sop", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-sopInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/kanchi-dividend-sop .github/skills/kanchi-dividend-sop && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "kanchi-dividend-sop" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sop into .github/skills/kanchi-dividend-sop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kanchi-dividend-sop", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install tradermonty/claude-trading-skills kanchi-dividend-sop --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/kanchi-dividend-sop .opencode/skills/kanchi-dividend-sop && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "kanchi-dividend-sop" agent skill from https://github.com/tradermonty/claude-trading-skills/tree/main/skills/kanchi-dividend-sop into .opencode/skills/kanchi-dividend-sop/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kanchi-dividend-sop", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
kanchi-dividend-sopConvert Kanchi-style dividend investing into a repeatable US-stock operating procedure.
Kanchi Dividend Sop is an agent skill from tradermonty/claude-trading-skills. Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Use when users ask for かんち式配当投資, dividend screening, dividend growth quality checks, PERxPBR adaptation for US sectors, pullback limit-order planning, or one-page stock memo creation. Covers screening, deep dive, entry planning, and post-purchase monitoring cadence.
Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 29 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/default-thresholds.md` and `references/sector-step2-modules.md`).
It sits in Business, Finance & HR, covering Operations and SOPs. The repository describes itself as: Claude Code skills for equity investors and traders — market analysis, technical charting, economic calendars, screeners, and trading strategy development. The licence is MIT.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit c8d58f0. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 10 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
python3From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
FMP_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Kanchi Dividend Sop loads about 3.1k tokens when it runs, and up to ~5.9k if it reads all its reference files. Until then it costs about 94 tokens; SKILL.md has 1,302 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from tradermonty/claude-trading-skills at commit c8d58f0, republished under its MIT licence (© tradermonty). 1,302 words, ~3,103 tokens.
.claude/skills/kanchi-dividend-sop/SKILL.md (or your agent's skills folder). This skill also uses 25 other files; get the full folder from GitHub.Implement Kanchi's 5-step method as a deterministic workflow for US dividend investing. Prioritize safety and repeatability over aggressive yield chasing.
Use this skill when the user needs:
The entry signal script requires FMP API access:
export FMP_API_KEY=your_api_key_herePrepare one of the following inputs before running the workflow:
skills/value-dividend-screener/scripts/screen_dividend_stocks.py.skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth_rsi.py.When using --input, provide JSON in one of these formats:
{
"profile": "balanced",
"candidates": [
{"ticker": "JNJ", "bucket": "core"},
{"ticker": "O", "bucket": "satellite"}
]
}Or simplified:
{
"tickers": ["JNJ", "PG", "KO"]
}The optional value-dividend-screener and
dividend-growth-pullback-screener handoffs use stocks[].symbol.
Both build_sop_plan.py --input and build_entry_signals.py --input
accept that shape directly, as well as the native candidates[].ticker and
tickers[] shapes above.
For deterministic artifact generation, provide tickers to:
python3 skills/kanchi-dividend-sop/scripts/build_sop_plan.py \
--tickers "JNJ,PG,KO" \
--output-dir reports/For Step 5 entry timing artifacts. --yield-floor is mandatory — it is
the Step-1 yield gate; without it every row fail-safes to STEP1-RECHECK
(a row can never reach a PASS tier without Step 1). Pass --profile /
--safety-bias for run_context, and --events-json for the Step 4b scan
(absent ⇒ every row is treated as SKIPPED and a TRIGGERED name is capped
to HOLD-REVIEW — never silently clean):
python3 skills/kanchi-dividend-sop/scripts/build_entry_signals.py \
--tickers "JNJ,PG,KO" \
--alpha-pp 0.5 \
--yield-floor 3.0 \
--profile balanced --safety-bias medium \
--events-json reports/kanchi_events_2026-05-17.json \
--output-dir reports/Collect and lock the parameters first:
Load references/default-thresholds.md and apply baseline
settings unless the user overrides.
Start with a quality-biased universe:
Use explicit source priority for ticker collection:
skills/value-dividend-screener/scripts/screen_dividend_stocks.py output (FMP/FINVIZ).skills/dividend-growth-pullback-screener/scripts/screen_dividend_growth_rsi.py output.Return a ticker list grouped by bucket before moving forward.
Primary rule:
latest_declared_regular dividend × cadence-implied frequency / price (WS-1 dividend_basis.py).
Never use profile.lastDividend / TTM — it lags the latest declared raise
(defect D5) and silently bundles specials (D4).Trap & freshness controls (machine-emitted by dividend_basis.py):
special_dividend_flag → exclude specials; report regular vs ttm yield.variable_policy_flag → FAIL (CALM-style; not an income base).cut_flag → FAIL; suspension_flag → FAIL.freeze_flag → HOLD-REVIEW (income cash-cow exception decided in
Step 8 synthesis only if safety is clean & unblocked).floor_borderline) and the latest declared dividend is not
confirmed from an authoritative source, emit STEP1-RECHECK — never a
hard FAIL (this is the CFR D5 fix).Safety is sector-specific — a uniform GAAP/FCF triad mis-judges banks
(FCF meaningless) and regulated utilities (FCF structurally negative).
Use references/sector-step2-modules.md; the deterministic dispatch is
scripts/payout_safety.py.
adjusted_eps_source = UNAVAILABLE ⇒ cap HOLD-REVIEW (fail-safe;
never a silent PASS).HOLD-REVIEW (FITB/Comerica golden case).When trend is mixed but not broken, classify as HOLD-REVIEW instead of
hard reject.
Use references/valuation-and-one-off-checks.md and apply
sector-specific valuation logic:
PER x PBR can remain primary.P/FFO or P/AFFO instead of plain P/E.P/E, P/FCF, and historical range.Always report which valuation method was used for each ticker.
Reject or downgrade names where recent profits rely on one-time effects:
Record one-line evidence for each FAIL to keep auditability.
Step 4 is backward-looking; Step 4b catches pending/recent structural
events (the MKC-Unilever miss, D3). For each surviving candidate, run a
WebSearch + issuer-IR/SEC check using the source hierarchy: issuer IR
→ SEC filing (8-K/10-Q/10-K/proxy/S-4) → exchange/company deck →
reputable wire → finance portals (secondary only). Record findings into a
curated events JSON and pass it via build_entry_signals.py --events-json.
HOLD-REVIEW
(tx > 10% mcap, share issuance > 10–20%, leverage +0.5x EBITDA,
control/listing/HQ change, merger-of-equals / RMT / spin-off / large
asset sale, dividend/rating/leverage-policy change, sector-specific
materiality, or rolling-24m cumulative M&A > 15% mcap). Minor bolt-ons
are a CAUTION note only.FAILED-DEGRADED / SKIPPED / NO_EVENT_FOUND
on a Step-5 TRIGGERED name ⇒ HOLD-REVIEW + T1 BLOCKED. WebSearch
unavailable (web app / offline) is treated the same — never a silent
skip. CLEAN_CONFIRMED (primary source checked) is stronger than
NO_EVENT_FOUND (search only).Set entry triggers mechanically:
+0.5pp).P/E, P/FFO, or P/FCF).Execution pattern:
40% -> 30% -> 30%.pre_order_blockers[] (from WS-1/2/3 — variable/cut/suspension,
adjusted-EPS-unavailable, GAAP/Adj divergence, bank credit, utility
FFO/debt, event-scan failed/skipped, stale dividend, …) OR
t1_blocked is true, the first tranche is blocked or downsized to a
≤20% tracking tranche — not 40%.SECTOR_CLUSTER_WARN_COUNT same-sector
names pass (e.g. many small banks share one macro beta), emit a
portfolio-level CLUSTER-RISK warning.Always produce:
CLEAN-PASS,
PASS-CAUTION, CONDITIONAL-PASS, HOLD-REVIEW, STEP1-RECHECK,
FAIL (synthesized by verdict.py from Step 1 + Step 2 + Step 4b +
blockers). Include evidence per row.references/stock-note-template.md) with the
per-ticker provenance block (price/dividend/payout/event sources,
unresolved_blockers, evidence_refs[]).Return and/or generate:
references/stock-note-template.md.skills/kanchi-dividend-sop/scripts/build_sop_plan.py in reports/.skills/kanchi-dividend-sop/scripts/build_entry_signals.py in reports/.Use this minimum rhythm:
Run this skill first, then hand off outputs:
kanchi-dividend-review-monitor for daily/weekly/quarterly anomaly detection.kanchi-dividend-us-tax-accounting for account-location and tax classification planning.STEP1-RECHECK, not FAIL.HOLD-REVIEW + T1
blocked. Never silently skip Step 4b.adjusted_eps/data missing ⇒ fail-safe
HOLD-REVIEW, never silent PASS.scripts/thresholds.py: single source of truth for all SOP
thresholds + SCHEMA_VERSION (downstream schema-evolution guard).scripts/dividend_basis.py: WS-1 regular/special/variable/freeze/cut +
Data Freshness Gate engine (pure, offline).scripts/payout_safety.py: WS-2 sector-aware GAAP/Adjusted/FCF payout
triad + completed-merger linkage.scripts/event_scanner.py: WS-3 isolated forward/recent corporate-action
scanner + materiality gate + pessimistic cap.scripts/verdict.py: WS-5 actionable-tier synthesis + run_context +
evidence_ref helpers.scripts/build_entry_signals.py: orchestrator (Step 5 targets + WS-1/2/3/5
integration). Flags: --yield-floor, --events-json, --profile,
--safety-bias, --universe-source.scripts/build_sop_plan.py: deterministic SOP plan scaffold generator.scripts/tests/test_golden_p0.py: P0 merge gate — end-to-end frozen
verdicts for CALM/ORI/CMCSA/MKC/CFR/cut (run via scripts/run_all_tests.sh).references/default-thresholds.md: human-readable threshold mirror.references/sector-step2-modules.md: Step 2 safety indicators by sector.references/valuation-and-one-off-checks.md: Step 3 valuation + Step 4 one-off.references/stock-note-template.md: one-page memo + provenance block.© tradermonty, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 25 other files (scripts, references) in skills/kanchi-dividend-sop of tradermonty/claude-trading-skills.
Open the folder on GitHubat commit c8d58f0
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in tradermonty/claude-trading-skills, which our catalogue first saw on October 7, 2026.
Kanchi Dividend Sop next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Kanchi Dividend Sop this skilltradermonty/claude-trading-skills | 3k | 1 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Cc Sdd New Agentgotalab/cc-sdd | 3.7k | — | ~1.1k | Automated safety check: Pass | MIT | |
| DBS Business Toolkit Entrydontbesilent2025/dbskill | 11k | — | ~2k | Automated safety check: Pass | Custom licence | |
| Agent Sop Authorstrands-agents/agent-sop | 1.2k | — | ~3.5k | Automated safety check: Pass | Apache-2.0 | |
| Diffusion Narrative Denouncingcanwhite/Krebs | 1k | — | ~831 | Automated safety check: Pass | MIT | |
| Company Researchsimonlin1212/Vibe-Research | 2.7k | — | ~3.2k | Automated safety check: Pass | MIT |
gotalab/cc-sdd
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dontbesilent2025/dbskill
Chinese-language entry skill for the dontbesilent business toolkit: onboards new users, orchestrates tasks across sub-skills, runs numbered prompts and lists hidden ones.
strands-agents/agent-sop
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canwhite/Krebs
基于"扩散模型叙事去噪流"的小说写作 SOP。将 AI 视为去杂质机器,通过锁定全局信号、预测叙事噪声、精准去噪、随机修正四个步骤,解决 AI 翻译腔、逻辑断层和故事平淡的问题。
simonlin1212/Vibe-Research
A 股 / 港股 / 美股个股研究六阶段 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个股时使用(港股 / 美股的口径差异见 §7);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…
0xenzyme/polanyi-skill
Michael Polanyi 的思维框架。用 Polanyi 视角分析隐性知识、技能习得、经验传承、师徒制、 知识管理、学习方法、AI/工具替代边界、科学共同体与后批判哲学问题。
tradermonty/claude-trading-skills
This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs.
tradermonty/claude-trading-skills
Detect and analyze trending market themes across sectors. An agent skill from tradermonty/claude-trading-skills.
tradermonty/claude-trading-skills
Track investment theses across their lifecycle — from screening idea to closed position with postmortem.
tradermonty/claude-trading-skills
Critically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism.
tradermonty/claude-trading-skills
This skill should be used when analyzing sector rotation patterns and market cycle positioning.
tradermonty/claude-trading-skills
Druckenmiller Strategy Synthesizer - Integrates 8 upstream skill outputs (Market Breadth, Uptrend Analysis, Market Top, Macro Regime, FTD Detector, VCP Screener, Theme Detector, CANSLIM Screener)…
Categories
Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure. Kanchi Dividend Sop is an agent skill from tradermonty/claude-trading-skills. Convert Kanchi-style dividend investing into a repeatable US-stock operating procedure.
Kanchi Dividend Sop fits situations like: users ask for かんち式配当投資; dividend screening; dividend growth quality checks; PERxPBR adaptation for US sectors.
Run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a claude-code`. Or copy the skill folder (skills/kanchi-dividend-sop in tradermonty/claude-trading-skills) into .claude/skills/kanchi-dividend-sop in your project. Claude Code loads it when a task matches its description.
Run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a codex`. Or copy the skill folder (skills/kanchi-dividend-sop in tradermonty/claude-trading-skills) into .agents/skills/kanchi-dividend-sop in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tradermonty/claude-trading-skills --skill kanchi-dividend-sop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kanchi-dividend-sop, .gemini/skills/kanchi-dividend-sop, .github/skills/kanchi-dividend-sop and .opencode/skills/kanchi-dividend-sop in your project.
Going by SKILL.md and its folder, Kanchi Dividend Sop needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named FMP_API_KEY. Our summary lists: Python 3; A credential in FMP_API_KEY.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Kanchi Dividend Sop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.8k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Kanchi Dividend Sop: Cc Sdd New Agent (gotalab/cc-sdd, 3.7k stars), DBS Business Toolkit Entry (dontbesilent2025/dbskill, 11k stars), Agent Sop Author (strands-agents/agent-sop, 1.2k stars) and Diffusion Narrative Denouncing (canwhite/Krebs, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
tradermonty (a GitHub user) maintains it in tradermonty/claude-trading-skills, which has 2,982 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on October 11, 2026.
Source: tradermonty/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.