Borrowing it
Nothing to install: this file belongs to daloopa/investing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/daloopa/investing/main/.claude/skills/meta-skill/SKILL.mdgit clone --depth 1 https://github.com/daloopa/investingWrote 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/daloopa/investing/meta-skill)<a href="https://agentmods.dev/skills/daloopa/investing/meta-skill"><img src="https://agentmods.dev/badge/skills/daloopa/investing/meta-skill/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/daloopa/investing/meta-skill"><img src="https://agentmods.dev/badge/skills/daloopa/investing/meta-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.05125 |
| Opus 5.5 | $0.00000 | $0.02050 |
| Sonnet 5.5 | $0.00000 | $0.01025 |
| Haiku 4.5 | $0.00000 | $0.00513 |
Grade A, and why
meta-skill 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 7d 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 — 319 lines — stays where its author put it; the contents beside it link to each section on GitHub.
| name | description | argument-hint |
|---|---|---|
| convert-skills-to-prompts | Convert all Claude Code skills into MCP prompt functions | (none) |
Convert every skill in .claude/skills/ into a Python MCP prompt function, producing a single output file prompts.py with one @daloopa_mcp.prompt function per skill.
This is a conversion task, not a rewrite. The analytical logic, calculations, data requirements, and output structure of each skill must be preserved exactly. What changes is the execution context: from Claude Code (file system, local scripts, MCP tools) to MCP prompt (MCP tools only, no file system, no local scripts).
Step 0: Read All Source Files
Read these files first — you need them all loaded before you start converting:
-
Shared references:
.claude/skills/data-access.md— data access patterns, tool signatures, citation rules.claude/skills/design-system.md— formatting, analytical voice, HTML template, color palette
-
Every skill file:
.claude/skills/*/SKILL.md— read all of them
-
Note the full skill list from the directory listing. Some skills may reference other skills (e.g.,
/initiatecalls/research-note+/build-model,/updatereferences prior output). Capture these dependencies.
Step 1: Understand the Two Shared References
Before converting any skill, analyze data-access.md and design-system.md to build a dependency map. Each skill uses different parts of these files.
data-access.md — Section Inventory
Identify these sections and what each provides:
| Section | Content | MCP Prompt Relevance |
|---|---|---|
| Section 1: Daloopa MCP Tools | Tool signatures: discover_companies, discover_company_series, get_company_fundamentals, search_documents |
ALWAYS INLINE — every skill needs these. The period parameter is calendar_periods, not periods (outdated): emit discover_company_series(company_id, keywords, calendar_periods), get_company_fundamentals(company_id, calendar_periods, series_ids), search_documents(keywords, company_ids, calendar_periods) even if data-access.md still says periods |
| Section 1.8: Expert Call Transcripts | get_expert_call_transcripts(company_id) — firm-gated BlueHeron expert calls: never-block rule, usage, mandatory call-URL citation |
CONDITIONAL — the tool row always stays in the Section 1 tool table; inline the Section 1.8 body verbatim as a shared _EXPERT_CALLS block only in prompts whose skill calls get_expert_call_transcripts |
| Section 1: API Script Fallback | python recipes/company_fundamentals.py etc. |
ALWAYS DROP — no file system in MCP prompt context |
| Section 2: External Market Data | Stock quotes, trading multiples, historical prices, peer multiples, risk-free rate | CONDITIONAL — only inline for skills that use market data |
| Section 2: Market Data Resolution Order | MCP → infra scripts → web search → defaults | ALWAYS INLINE — preserve the full 4-step resolution order (MCP tools → infra scripts → web search → defaults) |
| Section 3: Consensus Estimates | Consensus revenue/EPS, forward estimates, revisions, price targets | CONDITIONAL — only inline if the skill doesn't already handle consensus in its own steps |
| Section 4: Citation Requirements | Daloopa citation format, fundamental_id linking, document citations | ALWAYS INLINE — mandatory for all skills |
| Section 5: Infrastructure Tools | market_data.py, chart_generator.py, projection_engine.py, excel_builder.py, docx_renderer.py, etc. | ALWAYS DROP — not available in MCP prompt context |
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.
- 7d ago Changed · +7 lines 39afdeca5a79
- 2mo ago First seen · 312 lines · 0 tokens per session scan A abd4a02c2626
meta-skill is a skill published in the GitHub repository daloopa/investing (488 stars, last pushed 8d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 5,125 tokens. 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.
meta-skill compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| meta-skilldaloopa/investing | Skill | 488 | 8d ago | Apache-2.0 | A | 0 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |