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/fixnpx skills add bjornjee/agent-dashboard --skill fixgit 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 | $0.00021 | $0.01877 |
| Opus 5 | $0.00010 | $0.00938 |
| Sonnet 5 | $0.00004 | $0.00375 |
| Haiku 4.5 | $0.00002 | $0.00188 |
Grade A, and why
fix 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 2d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Diagnose and fix a bug.
Bug description: $ARGUMENTS
Instructions
Follow these phases in order. Each phase has a gate — do not proceed until the gate is satisfied. Apply all project rules and conventions that are in your context.
If the bug involves browser UI, Playwright, dev-server ports, screenshots, or interactive Browser/Chrome inspection, apply ../_shared/ui-automation.md at evidence gathering, environment setup, reproduction, verification, delegation, and cleanup points.
Phase 1: Setup
Follow ../_shared/worktree-setup.md with branch prefix fix.
Gate: Working directory is the new worktree on the correct branch, based on latest main. If .env* files existed in the source repo, they are all present in the worktree.
Phase 2: Gather Evidence
Start two tracks in parallel:
Background — Environment setup: First check for a reusable environment: if .env-setup-done exists in the worktree root AND every dependency manifest/lockfile present (package-lock.json, pnpm-lock.yaml, yarn.lock, requirements.txt, pyproject.toml, uv.lock, go.mod, go.sum) is older than the sentinel ([ "$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 — Evidence gathering:
Before touching code, collect grounded evidence from observable sources. Do not guess from reading code alone.
- Take the bug description — this may be an error message, stack trace, issue URL, or user description.
- Collect evidence from these sources (check all that are available):
- Logs: application logs, server logs, error tracking (Sentry, Datadog, etc.). Ask the user where logs live if not obvious.
- Metrics: dashboards, monitoring, performance counters. Ask for links or screenshots.
- Stack traces: the full trace, not just the top frame. Include line numbers and timestamps.
- Steps to reproduce: exact inputs, environment, and sequence that triggers the bug.
- Git history:
git log --oneline --since="2 weeks ago" -- <affected files>— what changed recently in the area? - Issue tracker: if an issue URL was provided, read it fully including comments for additional context.
- Summarize the evidence. State what is known (from logs/metrics/traces) vs what is hypothesized.
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.
- 2d ago First seen · 131 lines · 21 tokens per session scan A 3434efd5b0d1
fix is a skill published in the GitHub repository bjornjee/agent-dashboard (21 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 1,877 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.
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