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 mondaycom/mcp --skill forecastgit clone --depth 1 https://github.com/mondaycom/mcpWrote 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/mondaycom/mcp/forecast)<a href="https://agentmods.dev/skills/mondaycom/mcp/forecast"><img src="https://agentmods.dev/badge/skills/mondaycom/mcp/forecast/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/mondaycom/mcp/forecast"><img src="https://agentmods.dev/badge/skills/mondaycom/mcp/forecast.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 118 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 185 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 203 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 203 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- high Prompt Injection · line 239 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00102 | $0.03545 |
| Opus 5.5 | $0.00041 | $0.01418 |
| Sonnet 5.5 | $0.00020 | $0.00709 |
| Haiku 4.5 | $0.00010 | $0.00354 |
Grade A, and why
forecast 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 1mo 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 — 278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Forecast Dashboard
Builds a native monday dashboard that renders the pipeline as a forecast — committed / best-case / pipeline / closed-won, grouped by stage × close month. Replaces the "export to CSV and pivot it" pattern that sales ops leaders do manually every Monday.
Flow: Trigger → Gather → Synthesize → Publish (α, default) → Share (β, opt-in) → Proactive extension (opt-in, hygiene fix).
Input
- Optional: board name / ID + forecast period (e.g., "Q2 2026", "next 90 days").
- Optional: pre-declared mode (Default / Silent / Proactive).
Output
- α (default): A native monday
create_dashboardwith widgets per bucket + URL. - β (Share): Viewer-permission grant to sales leader via
all_monday_api+create_notification. - Proactive extension: Hygiene fix — if a non-closed deal is missing close date,
change_item_column_values(with batched user confirm) to set a placeholder OR post a prompt update asking the owner to fill it. Missing-amount items are flagged in the dashboard description and a hygiene update is posted on the item, but the amount column itself is never written (forecast integrity rail).
Knowledge
- Bucket definitions: closed-won / committed / best-case / pipeline (§ Step 5).
- Shared artifact conventions (see Shared patterns section below).
Tools (MCP)
get_user_context,list_workspaces,search— board resolution.get_board_info,get_column_type_info— schema + column types (stage/amount/close-date/forecast-category).get_board_items_page— paginated pull.board_insights— aggregate fallback.create_dashboard,create_widget— α publish.create_doc— fallback for non-admins (see Error handling).create_notification,list_users_and_teams— β share.create_column— add a forecast-category column only if required to render the dashboard.change_item_column_values— proactive hygiene fix.all_monday_api— escape hatch for dashboard permissions, idempotency GraphQL query, and doc updates.
What ships with it
2 files 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.
- 1mo ago First seen · 278 lines · 102 tokens per session scan A 6b3531b18408
forecast is a skill published in the GitHub repository mondaycom/mcp (433 stars, last pushed today), licensed MIT. It adds 102 tokens to every session and 3,545 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-30.
Other skills, from other repositories
init-workspace-flow
Workflow for initializing or upgrading a workspace: context, discovery, documentation, etc.
arrange-workspace-flow
Workflow for arranging a workspace: layout, reference source code, business/technical context, ecosystem setup.
github-triage
Triage GitHub issues through a label-based state machine with interactive grilling sessions. Use when user wants to triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.
paseo
Paseo reference for managing projects, workspaces, workspace scripts, agents, schedules, and heartbeats.
testgen-flow
Workflow for generating test cases from requirements (Issue Tracker / Wiki sources), exporting to a Test Management System, etc.
backlog
To validate story readiness for a sprint or to break work into a human WBS.