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 fernando-bertholdo/4-successful-AI-life --skill boardgit clone --depth 1 https://github.com/fernando-bertholdo/4-successful-AI-lifeWrote 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/fernando-bertholdo/4-successful-ai-life/board)<a href="https://agentmods.dev/skills/fernando-bertholdo/4-successful-ai-life/board"><img src="https://agentmods.dev/badge/skills/fernando-bertholdo/4-successful-ai-life/board/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/fernando-bertholdo/4-successful-ai-life/board"><img src="https://agentmods.dev/badge/skills/fernando-bertholdo/4-successful-ai-life/board.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 | $0.00088 | $0.06905 |
| Opus 5.5 | $0.00035 | $0.02762 |
| Sonnet 5.5 | $0.00018 | $0.01381 |
| Haiku 4.5 | $0.00009 | $0.00690 |
Grade A, and why
board 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 16d 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 — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multica: the parts around the command reference
0. The command reference is not this skill
Multica publishes the canonical CLI skill at
multica-ai/multica-cli. It is well maintained —
a linter runs its documented commands against the real binary daily, on both the minimum and
latest CLI — and it is the authority on the command surface:
/plugin marketplace add multica-ai/multica-cli
/plugin install multica-cli@multica-cli
Go there for: issue search semantics, the comment read modes (--roots-only --summary,
--thread --tail, --recent capping threads not comments, --compact, --full), the
working-directory restriction on --content-file / --description-file / --attachment, when
--parent is mandatory, the mention:// side effects, metadata versus custom properties,
subscribers, run inspection and cancellation, sub-issue stages, and PR linking.
Do not restate any of that here. Two skills that describe the same commands will disagree eventually, and the session obeys whichever it read first. This skill covers what that reference does not, and the moment it starts to overlap, the overlap is the bug.
If the reference is not installed, say so before operating a board — the safe defaults for mentions, status side effects and comment writes live there.
1. Finding the binary in a desktop-app install
The reference assumes multica is on PATH (a package-manager install). With the desktop app
it is not, and command -v multica returning nothing is expected — the reference's very first
step, multica version, fails there.
M="${MULTICA_BIN:-/Applications/Multica.app/Contents/Resources/app.asar.unpacked/resources/bin/multica}"
"$M" --version
ls ~/.multica/profiles # the profile names on this machine
P="${MULTICA_PROFILE:-<one of those>}"
"$M" --profile "$P" workspace list --output json # -> [{id, name, slug}, …]
Record the version next to any claim you make about the surface. The CLI ships changes almost
daily; the output shapes in §4 were first measured on v0.4.42, and everything added since —
the control-character note, the parent_issue_id note, the reply-routing rule in §5 — on
v0.4.44, between 19 and 20/09/2026.
What ships with it
1 file 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.
- 16d ago Changed · +9 lines 6a464bd4f5f6
- 25d ago Changed · +21 lines 0db640de7edc
- 27d ago Changed · +29 lines b25cc9589479
- 29d ago Changed · +62 lines 0006cc2b2238
- 1mo ago Changed · +34 lines 1c423c46bd25
- 1mo ago First seen · 219 lines · 88 tokens per session scan A a963bbb2edff
board is a skill published in the GitHub repository fernando-bertholdo/4-successful-AI-life (2 stars, last pushed yesterday), licensed MIT. It adds 88 tokens to every session and 6,905 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 2026-09-11.
Other skills, from other repositories
nav-pilot
Dispatch a Navigator task doc to Pilot as a labeled GitHub issue. Use when user says "dispatch TASK-XX to Pilot", "hand off to Pilot", "send to Pilot", or "queue for Pilot". One-way handoff (Navigator authors the spec, Pilot executes autonomously).
customer-success
Support workflows, ticketing systems (Zendesk, Intercom), knowledge base design, chatbot design, and metrics (CSAT, NPS). Use when building support infrastructure, designing help centers, or optimizing customer experience.
event-planner
Event planning with timelines, budgets, vendor coordination, logistics checklists, and post-event evaluation. Use when organizing conferences, workshops, galas, or corporate events.
hr-talent
HR and talent management expertise for talent acquisition, performance management, compensation strategy, organizational design, culture building, succession planning, and D&I programs. Use when hiring, managing performance, designing organizations, or building culture.
operations
Operations excellence expertise for supply chain optimization, process improvement (Lean, Six Sigma), capacity planning, vendor management, quality assurance, and operational efficiency. Use when optimizing processes, managing supply chains, or improving operational performance.
innovation
Innovation management expertise for innovation frameworks (Design Thinking, Stage-Gate), ideation processes, innovation portfolio management, venture capital, open innovation, and IP strategy. Use when driving innovation, managing R&D portfolios, or building innovation programs.