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 agentmods add skills/bjornjee/agent-dashboard/featurenpx skills add bjornjee/agent-dashboard --skill featuregit clone --depth 1 https://github.com/bjornjee/agent-dashboardWhat 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 | $0.00015 | $0.03575 |
| Opus 5 | $0.00008 | $0.01788 |
| Sonnet 5 | $0.00003 | $0.00715 |
| Haiku 4.5 | $0.00002 | $0.00358 |
Grade A, and why
feature 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 3d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Start a new feature in an isolated git worktree.
Feature description: $ARGUMENTS
Instructions
Follow these phases in order. Each phase has a gate — do not proceed until the gate is satisfied.
If the feature touches browser UI, Playwright, dev-server ports, screenshots, or interactive Browser/Chrome inspection, apply ../_shared/ui-automation.md at planning, environment setup, verification, delegation, and cleanup points.
For Verification profiles, apply ../_shared/verification-profiles.md. Active AGENTS.md/core rules may add doctrine, but this shared glossary is the standalone agent-dashboard fallback.
Phase 1: Setup
Follow ../_shared/worktree-setup.md with branch prefix feat.
Gate: Working directory is the new worktree on the correct branch, based on latest main. If .env* files existed in the source repo, they are all present in the worktree.
Phase 2: Plan
Start two tracks in parallel:
Background — Environment setup: First check for a reusable environment: if .env-setup-done exists in the worktree root AND every dependency manifest/lockfile present (package-lock.json, pnpm-lock.yaml, yarn.lock, requirements.txt, pyproject.toml, uv.lock, go.mod, go.sum) is older than the sentinel ([ "$f" -ot .env-setup-done ]), skip the launch and note the reuse — the setup from a prior run in this worktree is current. Otherwise, launch a background agent (run_in_background: true) to set up the dev environment per ../_shared/env-setup.md.
Foreground — Planning:
Phase order: research first, interview second, plan mode third, submit fourth. Plan mode is the last gate before approval, not a pre-research speed-bump. Each step has a HARD-GATE you cannot rationalize past.
- Research with
Explore. Use the built-inExploresubagent for any non-trivial codebase question or library lookup. Do not callAgentwithsubagent_type=Plan— composing the plan is your job, not a delegated subagent's. Synthesize what you found inline as your own assistant text.
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.
- 3d ago First seen · 225 lines · 15 tokens per session scan A 7458ac929faf
feature is a skill published in the GitHub repository bjornjee/agent-dashboard (21 stars, last pushed 1mo ago), licensed MIT. It adds 15 tokens to every session and 3,575 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
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gh-assign-issues
Use to assign GitHub issues to a milestone and/or owners in bulk, verifying each.
mcp-builder
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feishu
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interview
Ask one useful structured question at a time only when material product/implementation choices are genuinely missing; remember answers and produce a brief/spec. Discoverable facts should be investigated instead of asked.
security-review
Review trust boundaries, auth/authz, injection, secrets, filesystem/network exposure, dependencies, and exploitability without pretending a shallow lint is an audit.