Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add shalintripathi/organic-os/plugin install organic-osWrote 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/shalintripathi/organic-os/hoo-monday-report)<a href="https://agentmods.dev/skills/shalintripathi/organic-os/hoo-monday-report"><img src="https://agentmods.dev/badge/skills/shalintripathi/organic-os/hoo-monday-report/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/shalintripathi/organic-os/hoo-monday-report"><img src="https://agentmods.dev/badge/skills/shalintripathi/organic-os/hoo-monday-report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00053 | $0.01330 |
| Opus 5 | $0.00026 | $0.00665 |
| Sonnet 5 | $0.00011 | $0.00266 |
| Haiku 4.5 | $0.00005 | $0.00133 |
Grade A, and why
hoo-monday-report 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 12d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monday report
Resolve the brain: use registry.get_active() when running interactively; scheduled runs receive the brain path from the routine configuration.
-
Run
core.contracts.check_schema(brain_path)first. If not compatible, relay the action string and stop before any of the steps below. -
Read the last 7 days only:
signals/YYYY-MM-DD.mdfiles in the window,outcomes/records touched in the window,approvals/queue.mdas it stands today,runs/report folders created in the window, and anyskillbook.mdentries whoselast-confirmeddate falls in the window (cross-referencereflections/for the week to see which entries were added vs. just touched). -
Write
runs/YYYYMMDD-monday-report/REPORT.mdwith exactly these five sections, in this order, and no others:- What moved - the top 3 metric changes this week. Each line names
the number and the file it came from (a signal line or an outcome
record). When the week's signals carry
ai_referralslines, add one line for AI referrals - the week's total and the direction against the prior week (the AI-surface list is maintained in hoo-daily's SKILL.md); noai_referralslines in the window means no AI line, never an invented one. When the window's weekly run appended keyword-portfolio rows tokeywords/history.tsv, add one line for the biggest mover - the keyword, position now vs the prior recorded week, labeled honestly as GSC average position, not scraped SERP rank (the rule lives in hoo-weekly's Keyword portfolio section); no new history rows in the window means no keyword line, never an invented one. When~/.config/organic-os/cost-ledger-YYYYMM.tsvexists, close the section with one cost line - runs this week, total duration, tokens where the ledger has them; no ledger means no cost line, never an invented number. - What shipped - proposals that reached
appliedand briefs that reachedpublishedthis week, each with the item id and a link to its file in the brain. - What needs you - every item currently
proposedinapprovals/queue.md, one line each: what it is and what deciding it takes (a yes/no, not "please review"). - What we learned - skillbook entries appended or edited this week, restated in plain language, no evidence-tier jargon, each with its source item.
- Next week - the 2-3 highest-leverage
proposedorapproveditems still queued, ranked by the expected impact stated in their own source signal or finding.
- What moved - the top 3 metric changes this week. Each line names
the number and the file it came from (a signal line or an outcome
record). When the week's signals carry
-
Every number and claim must trace to one specific file in the brain. Never invent a metric, a trend, or a "likely cause" the brain does not already state. If a section has nothing to report, say so in one line instead of omitting the section or padding it.
-
Write for a reader with no SEO background: plain nouns and verbs, no unexplained acronyms, no jargon. Keep the whole report under 400 words.
-
Sparse week (few or no signals, outcomes, or shipped items): the report gets shorter, not padded - state plainly what did not happen ("no proposals shipped this week; two are still waiting on your review").
-
Deliver the report as a document through the configured approval channel. Document delivery is a channel capability, not a Telegram feature (ADR-0009 in the repo): each adapter declares whether it can carry a file, and a channel that cannot reports the file path instead. The markdown REPORT.md stays in
runs/as the canonical record either way - the document is a delivery format, never the source of truth. a. Render HTML next to the markdown:PYTHONPATH="$CLAUDE_PLUGIN_ROOT/lib" python3 -csnippet callingcore.report_render.render_html(markdown_text, title, site_name)(title "Monday report YYYY-MM-DD", site_name from the profile) and writingruns/YYYYMMDD-monday-report/REPORT.html. b. Probe for a PDF converter withcore.report_render.find_pdf_converter()(the probe list is canonical inreport_render.py). If one is found,core.report_render.to_pdf(html_path, converter); a None return means fall back to the HTML file, no error. c. Compose a two-line caption from the report itself: line 1 the week ("Monday report, week of YYYY-MM-DD - "), line 2 the single strongest What-moved line plus the count of items waiting ("3 waiting on you" / "nothing waiting"). No invented numbers here either - both lines quote the report. d. Send per channel: telegram -core.telegram.send_document(token, chat_id, path, caption=...)with the PDF if produced, else the HTML. in-session - save the file where step 2 wrote it and tell the user the exact path (attach it if the session surface can). slack/email - the adapter sends the file where the connector supports attachments; where it does not, deliver the caption plus the file path. pr-merge - the report is already in the brain repo; the caption plus the file path is the message. e. A failed send never fails the run: note it in one line at the end of REPORT.md and continue. -
Commit "monday-report: YYYY-MM-DD" if git.
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.
- 12d ago First seen · 91 lines · 53 tokens per session scan A da16153f255b
hoo-monday-report is a skill published in the GitHub repository shalintripathi/organic-os (5 stars, last pushed 5d ago), licensed MIT. It adds 53 tokens to every session and 1,330 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
Other skills, from other repositories
seo-vertical-detect
Classify a website as ecommerce, local-business, blog-publisher, saas, docs, or generic (multiple may apply), and report detected locales, from the persisted PageSnapshot plus the platform detector's vertical hints. Used by seo-orchestrator to decide which conditional modules to run and how to reweight the scores.
seo-fix-apply
Writer protocol preloaded into the seo-fixer-writer agent — how confirmed SEO/AI-search fixes are applied (git pre-flight, backup, Edit/Write for local diffs, ticketed adapter CLIs for remote targets, re-verify, publish only on a second ticket, rollback) and the findings-array output contract. Used only by the fix…
audit
Audit a website or web codebase for SEO and AI-search (GEO/AEO) — produces two independent 0-100 scores (Search SEO + AI Visibility) plus a prioritized, evidence-backed report persisted on disk. Read-only; never writes to the project. Use when the user asks to audit, analyze, check, or score a site's SEO, structured…
ai-visibility
Fast-path AI visibility — get a brand's 0–100 Akii Visibility Score (computed by an open-source LLM judge against the brand's public footprint) with four-dimension breakdown AND a per-engine proxy map for ChatGPT, Claude, Gemini, Perplexity, Copilot, and Google AI Overviews based on FirstPageSage signal weights.…
seo-audit
Single audit entry point — surface-level scorecard or deep infrastructure dive depending on the requested mode. Default full mode produces a multi-layer scorecard across all 9 areas (crawlability, indexation, meta tags, headings, images, Core Web Vitals, JS rendering, mobile + security, structured data, internal…
optimize-page
Comprehensive single-page optimization across all three layers — traditional SEO (title / meta / H1 / internal links), AEO (chunk quality, direct-answer leads, FAQ extraction), and GEO rewrites using the tactics published by the Princeton/IIT Delhi GEO study (citation integration, expert quotes, statistics enrichment…