Feature Planner
serendipity1004/cc-feature-implementer
Creates phase-based feature plans with quality gates and incremental delivery structure.
Gate quantitative Mira conclusions by requiring reproducible data, formulas, calculation ledgers, or explicit downgrades when numbers drive judgment.
$ npx skills add byteseek/Mira --skill data-analysis-quality-gate -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install byteseek/Mira data-analysis-quality-gate --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/byteseek/Mira.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-analysis-quality-gate .claude/skills/data-analysis-quality-gate && 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 "data-analysis-quality-gate" agent skill from https://github.com/byteseek/Mira/tree/main/skills/data-analysis-quality-gate into .claude/skills/data-analysis-quality-gate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-quality-gate", 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/byteseek/Mira/tree/main/skills/data-analysis-quality-gateType 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 byteseek/Mira --skill data-analysis-quality-gate -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install byteseek/Mira data-analysis-quality-gate --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/byteseek/Mira.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-analysis-quality-gate .agents/skills/data-analysis-quality-gate && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-analysis-quality-gate" agent skill from https://github.com/byteseek/Mira/tree/main/skills/data-analysis-quality-gate into .agents/skills/data-analysis-quality-gate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-quality-gate", 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 byteseek/Mira --skill data-analysis-quality-gate -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install byteseek/Mira data-analysis-quality-gate --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/byteseek/Mira.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-analysis-quality-gate .cursor/skills/data-analysis-quality-gate && 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 "data-analysis-quality-gate" agent skill from https://github.com/byteseek/Mira/tree/main/skills/data-analysis-quality-gate into .cursor/skills/data-analysis-quality-gate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-quality-gate", 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/byteseek/Mira.git --path skills/data-analysis-quality-gate--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 byteseek/Mira --skill data-analysis-quality-gate -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install byteseek/Mira data-analysis-quality-gate --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/byteseek/Mira.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-analysis-quality-gate .gemini/skills/data-analysis-quality-gate && 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 "data-analysis-quality-gate" agent skill from https://github.com/byteseek/Mira/tree/main/skills/data-analysis-quality-gate into .gemini/skills/data-analysis-quality-gate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-quality-gate", 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 byteseek/Mira data-analysis-quality-gateInstalls 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 byteseek/Mira --skill data-analysis-quality-gate -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/byteseek/Mira.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-analysis-quality-gate .github/skills/data-analysis-quality-gate && 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 "data-analysis-quality-gate" agent skill from https://github.com/byteseek/Mira/tree/main/skills/data-analysis-quality-gate into .github/skills/data-analysis-quality-gate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-quality-gate", 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 byteseek/Mira --skill data-analysis-quality-gate -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install byteseek/Mira data-analysis-quality-gate --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/byteseek/Mira.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-analysis-quality-gate .opencode/skills/data-analysis-quality-gate && 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 "data-analysis-quality-gate" agent skill from https://github.com/byteseek/Mira/tree/main/skills/data-analysis-quality-gate into .opencode/skills/data-analysis-quality-gate/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-analysis-quality-gate", 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.
data-analysis-quality-gateGate quantitative Mira conclusions by requiring reproducible data, formulas, calculation ledgers, or explicit downgrades when numbers drive judgment.
Data Analysis Quality Gate is an agent skill from byteseek/Mira. Gate quantitative Mira conclusions by requiring reproducible data, formulas, calculation ledgers, or explicit downgrades when numbers drive judgment.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Testing & QA, covering Quality gates and Data analysis. The repository describes itself as: Agent-native investment research workspace for evidence-tracked, refreshable investment theses across equities, earnings, macro, and portfolio review. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit adddce7. 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.
No scripts in the folder and no shell commands in SKILL.md.
From 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 no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Data Analysis Quality Gate loads about 1.1k tokens when it runs. Until then it costs about 44 tokens; SKILL.md has 337 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); files beside SKILL.md are not scanned.
The full file from byteseek/Mira at commit adddce7, republished under its Apache-2.0 licence (© byteseek). 337 words, ~1,113 tokens.
.claude/skills/data-analysis-quality-gate/SKILL.md (or your agent's skills folder).这个 skill 用于在 Mira 研究中判断数量型结论是否需要可复算数据、工具计算或显式降级。
它不是一个独立数据分析插件,也不绑定 Data Analytics、Python、Spreadsheet 或外部 API。它的职责是把 LLM 从“直接给数字结论”约束为:
当研究结论涉及以下任一内容时,必须进入本 gate,或明确写明 waived reason:
thesis_impact、research_action、actionability_bridge 或 durable conclusion 的数量判断research_objectresearch_questionmarket_scopetime_boundarycandidate_numeric_claimsavailable_sourcesuser_speed_preference
可选。若用户明确要求快看,可降低计算深度,但不能升级结论强度。tool_constraints
可选。说明是否允许本地脚本、CSV、Spreadsheet、联网、外部 API 或插件。每次运行本 gate,至少输出:
quant_dependency
none / low / medium / highcalculation_required
yes / nodata_requirement_brief_required
yes / nocalculation_ledger_required
yes / notool_consent_required
yes / noallowed_without_tool
yes / nodowngrade_if_not_calculated
none / calculation_gap / source_gap / watch_only / needs_refreshrecommended_tool_path
none / manual_formula_note / local_csv_script / spreadsheet / python / public_api / external_plugincalculation_depth
none / formula_note / ledger_required / full_model_requiredrefresh_conditionnone用于没有派生数量结论,或数量只作为非核心背景且已有可靠来源直接披露的场景。
输出要求:
quant_dependency: none 或 lowformula_note用于简单、低行数、可口头复核的计算,例如一个同比、一个 run-rate sanity check、简单估值倍数或明确公式的市场隐含值。
输出要求:
claim_type=derived_calculationcalculation-ledger.csvledger_required用于会影响 thesis impact、research action、actionability bridge、peer ranking、scenario table 或多来源冲突处理的计算。
输出要求:
templates/calculation-ledger.csvdata-requirement-brief.md 或 source_gapfull_model_required用于多变量估值、三表联动、复杂 peer set、时间序列、宏观/商品历史比较、TAM / SAM / SOM 或需要用户复用的 spreadsheet / script。
输出要求:
watch_only、needs_refresh、source_gap 或 calculation_gap如果 data_requirement_brief_required = yes,或 calculation_depth 为 ledger_required / full_model_required 且关键输入缺失,使用:
templates/data-requirement-brief.mdbrief 必须回答:
如果 calculation_ledger_required = yes,或 calculation_depth 为 ledger_required / full_model_required,使用:
templates/calculation-ledger.csvledger 必须记录:
默认不因为本 gate 自动引入插件或联网。
可以直接使用工具的场景:
必须先征求用户意见的场景:
建议话术:
这个判断依赖可复算计算。仅靠文本阅读容易出错。建议进入 calculation gate,用本地 CSV/Python/Spreadsheet 生成 calculation ledger;是否继续?
如果数量型结论没有完成必要计算:
research_action 或 actionability_bridge 的唯一依据。calculation_gap 或 source_gap。calculation_waived_by_speed,但置信度不得高于 low 或 medium。watch_only、needs_refresh 或 no_action。evidence-log.csv 记录 claim 来源和性质。
calculation-ledger.csv 记录公式、口径和复算路径。
派生计算结论必须同时满足:
claim_type=derived_calculation 或 explicit source noteupstream_sources 指向 L1-L5 来源© byteseek, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/data-analysis-quality-gate of byteseek/Mira.
Open the folder on GitHubat commit adddce7
Data Analysis Quality Gate 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 |
|---|---|---|---|---|---|---|
| Data Analysis Quality Gate this skillbyteseek/Mira | 275 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Feature Plannerserendipity1004/cc-feature-implementer | 176 | — | ~2.4k | Automated safety check: Pass | None | |
| Ccg Workflowfengshao1227/ccg-workflow | 5.9k | — | ~2.3k | Automated safety check: Pass | MIT | |
| Conducty Checkpointrobertbarclayy/conducty | 176 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Mission Plannerjdforsythe/forge | 151 | — | ~3.5k | Automated safety check: Pass | MIT | |
| Quality Gate0xNyk/lacp | 305 | — | ~382 | Automated safety check: Pass | MIT |
serendipity1004/cc-feature-implementer
Creates phase-based feature plans with quality gates and incremental delivery structure.
fengshao1227/ccg-workflow
How to run a non-trivial change end to end with the CCG role tools (ccganalyze / ccgdesign / ccgbuild / ccgdebug / ccgoptimize / ccgreview / ccgtest) and the verify- quality gates.
robertbarclayy/conducty
Quality gate between parallelization groups. An agent skill from robertbarclayy/conducty.
jdforsythe/forge
Decomposes goals into team blueprints using evidence-based scaling laws, topology selection, and role design.
0xNyk/lacp
Production quality gate for agent sessions. An agent skill from 0xNyk/lacp.
nwiizo/ccswarm
Release deployment process for ccswarm. An agent skill from nwiizo/ccswarm.
byteseek/Mira
Discover listed, pending, filed, or newly announced ETFs and create a structured candidate watchlist for ETF listing analysis.
byteseek/Mira
Run Mira's core single-equity research workflow across fundamentals, financial quality, macro context, technical pricing, events, and thesis framing.
byteseek/Mira
Analyze new, pending, or expanding ETF products as product signals, exposure maps, demand indicators, and potential asset-pricing read-throughs.
byteseek/Mira
Analyze commodity cycles, futures curves, inventories, cost curves, policy/geopolitics, positioning, and transmission into related assets.
byteseek/Mira
Analyze earnings releases, filings, transcripts, guidance, peer comparisons, market reaction, and thesis impact for a company reporting event.
byteseek/Mira
Map an unclear industry concept into boundaries, value chain, supply-demand mechanics, profit pools, evidence gaps, and investable candidates.
Categories
Gate quantitative Mira conclusions by requiring reproducible data, formulas, calculation ledgers, or explicit downgrades when numbers drive judgment. Data Analysis Quality Gate is an agent skill from byteseek/Mira. Gate quantitative Mira conclusions by requiring reproducible data, formulas, calculation ledgers, or explicit downgrades when numbers drive judgment.
Data Analysis Quality Gate fits situations like: tasks that involve Quality gates; tasks that involve Data analysis.
Run `npx skills add byteseek/Mira --skill data-analysis-quality-gate -a claude-code`. Or copy the skill folder (skills/data-analysis-quality-gate in byteseek/Mira) into .claude/skills/data-analysis-quality-gate in your project. Claude Code loads it when a task matches its description.
Run `npx skills add byteseek/Mira --skill data-analysis-quality-gate -a codex`. Or copy the skill folder (skills/data-analysis-quality-gate in byteseek/Mira) into .agents/skills/data-analysis-quality-gate 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 byteseek/Mira --skill data-analysis-quality-gate -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-analysis-quality-gate, .gemini/skills/data-analysis-quality-gate, .github/skills/data-analysis-quality-gate and .opencode/skills/data-analysis-quality-gate in your project.
SKILL.md names no scripts, command-line tools or credentials: Data Analysis Quality Gate is instructions for the agent only.
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. Review the folder before installing.
Data Analysis Quality Gate is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Data Analysis Quality Gate: Feature Planner (serendipity1004/cc-feature-implementer, 176 stars), Ccg Workflow (fengshao1227/ccg-workflow, 5.9k stars), Conducty Checkpoint (robertbarclayy/conducty, 176 stars) and Mission Planner (jdforsythe/forge, 151 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
byteseek (a GitHub organization) maintains it in byteseek/Mira, which has 275 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on September 8, 2026.
Source: byteseek/Mira on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.