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BP Monthly Report Writer

作者 houtonghoutong · GitHub ↗ · v0.3.1 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install bp-monthly-report
功能描述
Draft monthly BP reports by normalizing templates, mapping BP anchors, collecting evidence, and writing sections in order with data-backed progress evaluation.
使用说明 (SKILL.md)

BP Monthly Report Skill

Use this skill when the user wants a monthly report draft that must match a fixed structure and be grounded in real BP data, node progress, and business updates.

Capability Summary

This skill can directly handle these tasks:

  • identify the target BP node from a node name, groupId, or BP组织节点ID
  • fetch the node's BP goals, key results, measure standards, and linked reports
  • generate lightweight evidence manifests and direct report references
  • generate staged artifacts, fine-grained KR/action cards, and a monthly or quarterly report draft
  • roll a later month forward from the previous month's baseline
  • use the bundled default month template and bundled fill-in specification when the environment has standardized on them

It is not only a writing guide. It is a BP-fetching and report-drafting workflow.

First-Turn Behavior

On the first reply, state the skill's capability plainly and ask only for the minimum missing inputs.

Preferred opening pattern:

我可以直接按 BP 系统取数并生成这份月报。
最少只需要这 3 个信息:
1. BP周期或 `periodId`
2. 节点名称、`groupId` 或 BP组织节点ID
3. 月报月份

如果模板和填报规范已经固定,我会直接按既定模板执行,不再重复询问。
如果你要,我先从节点解析和 BP 取数开始。

Do not describe the skill as if it were only a manual or passive guideline. Do not ask for 月报模板 or 进展数据 again if the workflow is already designed to fetch evidence from the BP system itself. Ask about template or spec only if:

  • the user explicitly says a different template must be used
  • the template is genuinely unknown for the current environment
  • the report structure cannot be inferred from prior context

Goal

Produce a controllable v1.0 monthly report draft that:

  • matches the target template structure exactly
  • uses the user's fill-in convention and wording constraints
  • is backed by traceable BP and progress evidence
  • surfaces concrete work progress for each major item, not only evidence existence
  • is generated section by section, not in one pass
  • evaluates progress primarily through 关键成果 + 衡量标准, not through 关键举措 volume
  • exposes how many original work reports were adopted for the draft so source coverage can be judged quickly
  • uses 汇报日期 as the only default month-attribution rule for monthly evidence selection

What to read

  1. Read the target month report template.
  2. Read the user's fill-in specification or a completed sample based on that template.
  3. Read only the BP and progress materials needed for the current node and person.
  4. If the data layout is unclear, read references/source-schema.md.
  5. For the staged workflow, read references/workflow.md.
  6. For section order and section writing rules, read references/section-order.md.
  7. If the user supplied a filled sample or fill-in convention, read references/fill-patterns.md.
  8. When BP system fetching is involved, read references/bp-system.md.
  9. When status judgment is required, read references/traffic-lights.md.
  10. When producing deliverables, read references/artifact-layout.md.
  11. When writing a later month or quarter, read references/rolling-baseline.md.
  12. When a Chinese business-facing explanation is needed, read references/business-description.zh-CN.md.
  13. When a Chinese design or architecture explanation is needed, read references/design-solution.zh-CN.md.

If the user does not provide another template package and the current environment uses the standard monthly pack, use these bundled defaults first:

Non-negotiable rules

  • Do not draft the full report in a single generation pass.
  • Do not write section 1 before sections 2-8 are materially stable.
  • Keep the final chapter order and heading structure identical to the target template.
  • Every conclusion must be attributable to a BP anchor, a progress update, or an explicit user input.
  • When evidence is missing, mark the field as missing and ask for补充 only if the gap blocks a reliable draft.
  • Prefer deterministic intermediate artifacts over free-form reasoning.
  • Ask for and confirm the BP周期 and the 目标节点 before fetching BP data. If the user already provides periodId together with groupId or a BP组织节点ID, use them directly and skip redundant lookup.
  • Treat 目标 as the desired end state, 关键成果 as the evaluation basis, and 关键举措 as supporting actions.
  • Always fetch 关键举措 metadata together with goals and key results.
  • In chapter 2, the traffic-light judgment target is each 关键成果. Do not judge only at the goal heading level. If one subsection contains multiple key results, render separate result blocks and judge each one independently.
  • In chapter 4, the traffic-light judgment target is each 关键举措. Do not judge only at the chapter summary level. Each action block must carry its own 🟢 / 🟡 / 🔴 / ⚫ judgment.
  • In report writing, prioritize evidence linked to goals and key results. Use action reports as secondary support for evaluation, but treat them as a major evidence pool for concrete progress extraction.
  • When evaluating related reports, prioritize reports written by the current node's responsible owners or assignees. Reports written by others are only auxiliary evidence unless the user explicitly says otherwise.
  • Every major BP item must carry a traffic-light icon judgment: 🟢 / 🟡 / 🔴 / ⚫.
  • Treat the traffic light as the single deviation signal. Do not add a second independent 偏离判断 color field.
  • If no valid progress report or no credible evidence can support progress judgment for the current month, default to rather than 🟡 or 🔴.
  • AI may judge an item as , but AI must not decide the black-light subtype on its own.
  • After a judgment, the report must explicitly tell the user that black-light subtype requires manual review, and ask the user to choose one of: 未开展/未执行, 已开展但未关联, or 体外开展但体系内无留痕.
  • If the user confirms 未开展/未执行, the report must explicitly ask and record what will be done in the next month / next cycle.
  • If the user confirms 已开展但未关联, the report must explicitly require BP re-association of the existing work report/material and keep that item as a reminder until the next cycle confirms completion.
  • If the user confirms 体外开展但体系内无留痕, the report must explicitly require补充留痕 and improvement of the working method so the same level can be judged in future cycles.
  • Every traffic-light judgment in the final report must be followed by an explicit 判断理由.
  • In user-facing report markdown, render the whole traffic-light review block in a color that matches the judgment itself: 🟢 uses green text, 🟡 uses yellow text, 🔴 uses red text, and uses black or deep gray text.
  • Every traffic-light judgment in the final report must also include a short human-review block asking whether the user agrees with the judgment.
  • The human-review block must support: 同意 / 不同意.
  • If the reviewer disagrees, the block must ask for a reason category and free-text reason.
  • Reason categories should at least support: BP不清晰 / 举证材料不足 / AI判断错误 / 其他.
  • If the judgment is 🟡 or 🔴 or , the final report must require a corrective-action block: 整改方案 / 承诺完成时间 / 下周期具体举措.
  • Every adopted evidence item in the final report must point to the original BP report itself, preferably via [标题](reportId=\x3Cid>&linkType=report). Do not dump full report bodies into local JSON files unless the user explicitly asks for archival snapshots.
  • Until upstream APIs expose stable attachment metadata and reportId, treat attachment reading and direct online links as pending integrations instead of fabricating local substitutes.
  • For each major report item, extract concrete progress lines from the source report: what was completed, what moved forward, what decision was made, what remains blocked, and what happens next.
  • When raw report payloads expose attachment metadata or attachment links, record that metadata together with the evidence entry and read the attachments before drafting if they materially affect progress judgment.
  • Do not reduce a chapter to “there is evidence”. Convert evidence into explicit progress statements.
  • Section 1 must state how many original work reports were adopted for this draft. Prefer a compact breakdown such as total adopted reports, owner-authored primary reports, other manual reports, and AI reports if any were used.

Required inputs

At minimum, gather:

  • reporting month
  • BP period or periodId
  • reporting node or node identifier such as groupId, node_id, or an unambiguous node name

Default minimum call contract:

  • period_id or bp_period
  • groupId or node_id or node_name
  • report_month

Ask for template path, spec path, or extra progress materials only when they are actually missing for the current environment.

Optional but useful:

  • person name, role, department, if the template metadata or node resolution needs it
  • employeeId, if the node is a personal BP node and is easier to resolve this way
  • explicit BP IDs for the node
  • management summary or leader concerns for the month
  • optional wording preferences for rendering traffic-light explanations

Usually do not require these optional fields before starting BP fetch and evidence collection.

Working sequence

Follow this sequence without collapsing steps:

  1. Confirm the BP period and target node.
  2. Normalize the template and extract the exact heading tree.
  3. Resolve the node inside BP and build a BP anchor map.
  4. Collect month-specific evidence and map each fact to a BP anchor.
    • Prefer using scripts/collect_bp_month_evidence.py instead of ad hoc query code.
  5. Build section cards before writing any chapter prose.
    • Prefer using scripts/dump_bp_anchor_map.py to stabilize the BP skeleton.
  6. Materialize dual-report artifacts.
    • Prefer using scripts/build_dual_report_artifacts.py to create 04_cards / 05_ai_baseline_report / 05_review_queue / 07_user_review_report.
  7. Draft sections in this order: 2 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> 1.
  8. Run a consistency pass to check wording, metrics, status, and cross-section alignment.

Expected intermediate artifacts

Create these artifacts in memory or as working notes:

  • intake_summary
  • template_outline
  • bp_anchor_map
  • evidence_ledger
  • 04_cards
    • kr_cards
    • action_cards
  • 05_review_queue
  • 05_ai_baseline_report
  • 07_user_review_report
  • source_inventory_summary

Use the field definitions from references/source-schema.md.

Persist the run artifacts using the layout from references/artifact-layout.md.

Output standard

The final output should include:

  • a short note on what sources were used
  • the monthly report draft with the exact template structure
  • a short gap list for fields that still need manual补数 or确认

Do not add extra chapters unless the user explicitly asks for them.

安全使用建议
Before installing or enabling this skill, ask the publisher to explain how it authenticates to your BP system and where it sends fetched data. Specific questions to ask: (1) which credentials or tokens are required and how should they be supplied (environment variables, agent-scoped secret, OAuth flow)? (2) will any of the included scripts be executed by the agent, and if so what Python runtime and dependencies are required? (3) do the scripts call any external endpoints beyond your BP/work-report APIs (provide full URLs) and do they persist any data locally or to third parties? If you cannot get clear answers, review the Python scripts line-by-line in a safe environment (offline or air-gapped) or avoid installing — especially avoid providing any high-privilege credentials until you confirm the code’s network targets and data flows. If you decide to test, do so with non-production credentials and in an isolated environment.
功能分析
Type: OpenClaw Skill Name: bp-monthly-report Version: 0.3.1 The bundle is a specialized automation tool designed to generate Business Plan (BP) monthly reports by interfacing with a specific enterprise API (mediportal.com.cn). It uses Python scripts (e.g., collect_bp_month_evidence.py and generate_scheme_v2_monthly_reports.py) to fetch task data, collect evidence, and format markdown reports based on provided templates. While the code includes some idiosyncratic practices—such as hardcoded local file paths (/Users/hou/...) and invoking a Ruby subprocess for YAML parsing in build_dual_report_artifacts.py—these appear to be artifacts of an internal development environment rather than indicators of malice. There is no evidence of data exfiltration, unauthorized remote control, or malicious prompt injection intended to subvert the agent's safety.
能力评估
Purpose & Capability
The skill's stated purpose is to fetch BP system data and generate evidence-backed reports. The bundle contains scripts that almost certainly implement API fetches against BP endpoints (references name concrete /bp/... and /work-report/... APIs). However, the skill declares no required environment variables, no primary credential, and no required config paths. A capability that performs authenticated fetches from an internal BP system normally needs credentials or a clear explanation of how it will obtain auth; the lack of declared credentials or explanation is an incoherence.
Instruction Scope
SKILL.md explicitly instructs the agent to fetch BP periods, resolve nodes, read BP anchors, and pull related work-report details (it references concrete API fetch order). The instructions otherwise limit reading to bundled templates and referenced docs; they do not ask to read unrelated system files. The scope is appropriate for the stated reporting task, but it implicitly requires network/API access and authenticated calls — the instructions do not state how credentials will be provided or whether network access to the BP system is available, which expands the implicit scope.
Install Mechanism
The registry lists this as an instruction-only skill (no install spec), yet the package contains multiple non-trivial scripts (several large Python scripts). Including executable scripts without an install/runtime plan is inconsistent: either these scripts are expected to run (in which case an install/runtime and dependency declaration is needed) or they are simply documentation. The absence of an install spec and dependency list makes it unclear how/where these scripts will execute and what runtime privileges they require.
Credentials
No environment variables, credentials, or config paths are declared despite the skill's explicit need to call internal BP and work-report APIs. Skills that fetch from protected APIs typically require at least one token/credential or an explanation that they will rely on existing agent-hosted auth. The lack of declared secrets (API token, cookie, base URL) is disproportionate to the task and leaves unanswered how sensitive corporate data will be accessed.
Persistence & Privilege
The skill does not set always:true and is user-invocable only; disable-model-invocation is false (normal). There is no evidence it modifies other skills or system-wide settings. Persistence/privilege configuration appears reasonable.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install bp-monthly-report
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /bp-monthly-report 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.3.1
Replace misleading static business titles in bundled template/spec with neutral placeholders and hard rules so subsection titles must come from the current node's real BP goals.
v0.3.0
Switch BP report evidence chain to reportId-based detail fetch; use work-report detail APIs for content, replies, and node opinions; add direct report links; fix month attribution to report_time; finalize fine-grained KR/action cards and review queue.
v0.2.5
Add black-light manual-review rule: AI can judge black light only, while subtype selection and required follow-up answers must be confirmed by the user.
v0.2.4
Fix report pagination, add raw-hit vs adopted counts, and collapse bulk notification evidence.
v0.2.3
Add adopted work-report count summary to section 1 and source inventory.
v0.2.2
Bundle default monthly template and rename fill specification to a generic organization example.
v0.2.1
Add Chinese business description and Chinese design-solution documents, and wire both references into SKILL.md for business-facing and architecture-facing guidance.
v0.2.0
Update evidence model to favor direct report references over local JSON snapshots, add action-layer evidence handling, improve first-turn behavior, unify traffic-light judgment, and refine user-facing report rendering with BP names instead of raw IDs.
v0.1.0
Initial alpha release: BP-based monthly report drafting with evidence prioritization, traffic lights, rolling baseline, and artifact preservation.
元数据
Slug bp-monthly-report
版本 0.3.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 9
常见问题

BP Monthly Report Writer 是什么?

Draft monthly BP reports by normalizing templates, mapping BP anchors, collecting evidence, and writing sections in order with data-backed progress evaluation. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 154 次。

如何安装 BP Monthly Report Writer?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install bp-monthly-report」即可一键安装,无需额外配置。

BP Monthly Report Writer 是免费的吗?

是的,BP Monthly Report Writer 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

BP Monthly Report Writer 支持哪些平台?

BP Monthly Report Writer 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 BP Monthly Report Writer?

由 houtonghoutong(@houtonghoutong)开发并维护,当前版本 v0.3.1。

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