Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/mishahanin/heading-osnpx agentmods add skills/mishahanin/heading-os/dashboardWrote 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/mishahanin/heading-os/dashboard)<a href="https://agentmods.dev/skills/mishahanin/heading-os/dashboard"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os/dashboard/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/mishahanin/heading-os/dashboard"><img src="https://agentmods.dev/badge/skills/mishahanin/heading-os/dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 11 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00051 | $0.01083 |
| Opus 5.5 | $0.00020 | $0.00433 |
| Sonnet 5.5 | $0.00010 | $0.00217 |
| Haiku 4.5 | $0.00005 | $0.00108 |
Grade A, and why
dashboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CEO Morning Dashboard
Generate the daily CEO Morning Dashboard -- a single-page operational briefing aggregating CRM health, pipeline, calendar, email, strategy, and data freshness.
Trigger Phrases
"dashboard", "morning dashboard", "morning brief", "daily brief", "bridge view", "daily dashboard"
Execution
Step 1: Generate the dashboard
Run the generation script:
python scripts/generate-dashboard.py --pdf
This reads all workspace data sources and produces:
trace/scratch/outputs/operations/dashboard/YYYY-MM-DD/morning-dashboard.htmltrace/scratch/outputs/operations/dashboard/YYYY-MM-DD/morning-dashboard.pdf
Step 2: Validate hidden characters
Bash gets no path redirect, so resolve the outputs root first.
OUTPUTS_DIR="$(python3 -c "import sys; sys.path.insert(0,'.'); from scripts.utils.workspace import get_outputs_dir; print(get_outputs_dir())")"
python scripts/sanitize-text.py "$OUTPUTS_DIR/operations/dashboard/YYYY-MM-DD/morning-dashboard.html" --scan
Step 3: Report to Misha
Summarize key findings from the dashboard:
- Number of urgent items (RED contacts, overdue commitments)
- Today's meeting count
- Pipeline snapshot (active deals, won, investors)
- Any stale data files needing refresh
Format: concise 3-5 bullet summary with file paths.
After the hidden-characters confirmation line, append a one-line branding confirmation:
Branding: 31C corporate (dark cover, GT Standard, orange corner, blue accents).
The GT Standard fonts or the canonical brand logos can fail to embed, when a
file is missing from asset/brand/datastore/brand/. Surface that explicitly instead of a
silent fall back to Inter.
Optional: Live Sea State Enrichment
If Misha requests a live sea state update, run a quick WebSearch for:
- "DPI deep packet inspection" latest news
- "telecom cybersecurity" latest developments
- Key regions ([priority regions]) telecom news
Add a brief 2-3 sentence sea state note to the summary.
Data Sources
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 Changed e7bbae2850b8
- 1mo ago Changed 532b16746126
- 1mo ago Changed · +1 lines 591af9271fb3
- 1mo ago Changed · +3 lines 88f684ac1873
- 1mo ago First seen · 115 lines · 51 tokens per session scan A faad545743b6
dashboard is a skill published in the GitHub repository mishahanin/heading-os (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 51 tokens to every session and 1,083 once invoked, about $0.0002 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
dcf-model
Build discounted cash flow valuation workbooks in Excel.
ascii-video
ASCII video: convert video/audio to colored ASCII MP4/GIF.
grounded-citations
Ground answers and documents in cited, verifiable sources.
hermes-agent-skill-authoring
Author in-repo SKILL.md files: frontmatter and structure.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
baoyu-article-illustrator
Article illustrations: type × style × palette consistency.