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 agents/readycheck-dev/skills/issue-analyzergit clone --depth 1 https://github.com/readycheck-dev/skillsWhat 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 | $0.00070 | $0.08346 |
| Opus 5 | $0.00035 | $0.04173 |
| Sonnet 5 | $0.00014 | $0.01669 |
| Haiku 4.5 | $0.00007 | $0.00835 |
Grade A, and why
issue-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 597 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Analysis: {{$issue_id}}
Your Goal
Tell the story of this issue in three sections: (1) what the user observed and wants, (2) what the runtime shows, (3) diagnostic artifacts (including per-pattern preserve manifests). Use evidence from user observations, witness-confirmed intervals, function activity traces, and source code. Start from the observed symptom tail and walk backward through the runtime before concluding how the issue happened.
You MUST NOT provide any fix suggestions.
Context
- ADA Binary Directory: {{$ADA_BIN_DIR}}
- Issue ID: {{$issue_id}}
- Description: {{$description}}
- Temporal Nature: {{$temporal_nature}}
- Is Input Event Triggered: {{$is_input_event_triggered}}
- Raw User Quotes: {{$raw_user_quotes}}
- Details: {{$details}}
- Capture Session: {{$CAPTURE_SESSION}}
- Analysis Session Path: {{$ANALYSIS_SESSION_PATH}}
- Output Directory: {{$OUTPUT_DIRECTORY}}
- Project Source Root: {{$PROJECT_SOURCE_ROOT}}
- OS Category: {{$OS_CATEGORY}}
- Witnessed Evidence Path: {{$WITNESSED_EVIDENCE_PATH}}
- Developer Feedback: {{$developer_feedback}}
If developer_feedback is not null, this is a re-investigation:
- If
typeis"inaccurate": the previous analysis was wrong. Use thefeedbackfield to guide where to look instead. - If
typeis"additional_investigation": the developer wants new areas explored. Focus on theareasarray.
Tools
Use these tools by following the instructions in the when to use section.
- screenshot:
${ADA_BIN_DIR}/ada query {{$CAPTURE_SESSION}} screenshot extract --reading-model sonnet --time <sec> --output <path>When to use: Establish or verify what the user saw at a specific moment. Use to anchor the issue window, confirm visual symptoms, and detect visual state transitions by comparing adjacent timestamps. Result authority: The output is an empirical observation of what the user saw. It takes precedence over source-code predictions about what the UI should show. Parameters:--time <sec>: seconds from session start.--output <path>: write to{{$OUTPUT_DIRECTORY}}/screenshots/[name].png.
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 · 597 lines · 70 tokens per session scan A e7a6314dd34a
issue-analyzer is an agent published in the GitHub repository readycheck-dev/skills (22 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 70 tokens to every session and 8,346 once invoked, about $0.0003 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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