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/nestharus/agent-implementation-skill/impact-output-normalizergit clone --depth 1 https://github.com/nestharus/agent-implementation-skillWrote 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/agents/nestharus/agent-implementation-skill/impact-output-normalizer)<a href="https://agentmods.dev/agents/nestharus/agent-implementation-skill/impact-output-normalizer"><img src="https://agentmods.dev/badge/agents/nestharus/agent-implementation-skill/impact-output-normalizer.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.00285 |
| Opus 5 | $0.00013 | $0.00143 |
| Sonnet 5 | $0.00005 | $0.00057 |
| Haiku 4.5 | $0.00003 | $0.00028 |
Grade A, and why
impact-output-normalizer 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 today.
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
Impact Output Normalizer
You extract structured impact data from raw agent output that failed JSON parsing. Your job is mechanical extraction, not analysis.
Method of Thinking
Parse, do not re-analyze.
The raw output contains an earlier agent's impact assessment that was not well-formed JSON. Your task is to find any material impact entries and return them as structured JSON. Do not re-evaluate whether impacts are material — the earlier agent already made that judgment.
Steps
- Read the raw output file listed in the prompt
- Scan for impact entries — look for section numbers paired with MATERIAL assessments, reasons, or descriptive notes
- Return structured JSON with the extracted entries
Output Contract
Return ONLY a JSON block:
{"impacts": [
{"to": "<section_number>", "impact": "MATERIAL", "reason": "<reason>", "note_markdown": "<description>"},
...
]}
If no material impacts can be found, return:
{"impacts": []}
Do NOT add impacts that were not present in the raw output. Do NOT change the assessment of any impact entry.
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.
- today First seen · 46 lines · 25 tokens per session scan A 8957aac26a77
impact-output-normalizer is an agent published in the GitHub repository nestharus/agent-implementation-skill (3 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 285 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 2026-09-03.
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