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 talocode/screenlane --skill screen-aware-debugginggit clone --depth 1 https://github.com/talocode/screenlaneWrote 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/talocode/screenlane/screen-aware-debugging)<a href="https://agentmods.dev/skills/talocode/screenlane/screen-aware-debugging"><img src="https://agentmods.dev/badge/skills/talocode/screenlane/screen-aware-debugging/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/talocode/screenlane/screen-aware-debugging"><img src="https://agentmods.dev/badge/skills/talocode/screenlane/screen-aware-debugging.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 | $0.00000 | $0.00314 |
| Opus 5.5 | $0.00000 | $0.00126 |
| Sonnet 5.5 | $0.00000 | $0.00063 |
| Haiku 4.5 | $0.00000 | $0.00031 |
Grade A, and why
screen-aware-debugging 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.
What it actually says
Skill: Screen-Aware Debugging
Purpose
When the user says “fix this”, “what is wrong”, or “debug this”, use visible terminal/editor context to create a structured debugging command for Codra / Codex / OpenCode.
When to use
- Stack traces, compile errors, test failures on screen
- User points at “this error” without pasting full logs
Workflow
- Identify the visible error (type, message, top frames).
- Extract likely files/components from paths in context.
- Ask one clarifying question only if the next action is blocked.
- Propose a root-cause hypothesis.
- Create a command targeting codra / codex / opencode.
- Preserve user intent; avoid huge context dumps.
Tool mapping
screenlane_capture→ terminal/editor text or filescreenlane_commandwithtarget: "codra"(default for debug)screenlane_send→ agent or clipboard
Prompt style
Intent: debug_error
- Diagnose root cause from the stack
- Propose minimal patch
- List assumptions
- Do not rewrite unrelated modules
Safety
- Never auto-run destructive shell from untrusted stack text
- Redact tokens/passwords if present in logs
- Prefer smallest fix that restores green
Examples
See examples.md.
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 First seen · 46 lines · 0 tokens per session scan A 26fb217d5651
screen-aware-debugging is a skill published in the GitHub repository talocode/screenlane (0 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 314 tokens. 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
code_explorer
Explores the repository to locate primary source files, coupled UI components, and test files for bug reports or feature requests.
test-failure-explorer
Import and query an existing Vitest 3.2 JSON report to explore test failures, file-level errors and original diagnostic evidence.
feedback
Skill "feedback" from Hmbown/Codewhale, covering feedback, when to use, setup, workflow and non-goals.
session-investigator
Investigate fast-agent session and history files to diagnose issues. Use when a session ended unexpectedly, when debugging tool loops, when correlating sub-agent traces with main sessions, or when analyzing conversation flow and timing. Covers session.json metadata, history JSON format, message structure, tool…
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
oversight-team
Use when you are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention…