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 avizmarlon/agent-skills --skill large-payload-completenessgit clone --depth 1 https://github.com/avizmarlon/agent-skillsWrote 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/avizmarlon/agent-skills/large-payload-completeness)<a href="https://agentmods.dev/skills/avizmarlon/agent-skills/large-payload-completeness"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/large-payload-completeness/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/avizmarlon/agent-skills/large-payload-completeness"><img src="https://agentmods.dev/badge/skills/avizmarlon/agent-skills/large-payload-completeness.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 | $0.00060 | $0.01264 |
| Opus 5.5 | $0.00024 | $0.00506 |
| Sonnet 5.5 | $0.00012 | $0.00253 |
| Haiku 4.5 | $0.00006 | $0.00126 |
Grade A, and why
large-payload-completeness 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 2mo 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify Large Payload Fetches Are COMPLETE — Anti Silent-Truncation
When fetching a large payload (database queries, API responses, log dumps, HAR files, transcript extracts, or any source exceeding ~50k characters), you must explicitly verify completeness before claiming you have read everything.
Why This Matters
Silent truncation is a particularly dangerous failure mode because coherent output masks incomplete data. An AI can produce a plausible, well-reasoned response based on the first chunk of a paginated fetch without realizing 60–70% of the source is missing. The response sounds complete and internally consistent, which fools both the AI and the human reader into believing the analysis is based on the full dataset.
Real-world example: An agent was asked to build a comprehensive report from a large database across multiple rounds of data collection. The agent fetched in chunks and implicitly assumed completeness. The response was coherent. Only later, when the human questioned a specific detail, did re-inspection reveal that a substantial middle section (containing critical data) and the final portion were silently skipped. The first chunk had been sufficient to construct a seemingly complete narrative, masking the gap.
The cost: rework, missed details in the deliverable, and erosion of trust.
Mandatory Gate — Before Operating on Fetched Content
Apply this gate every time you fetch a large payload:
-
Cross-check size returned vs. expected.
- If the source is known to contain 337k characters and the fetch returned 90k, the gap of 247k must be explained. An unexplained gap means the fetch is incomplete.
- Do not assume "the important bits came through." Do not assume "rest is probably duplicate or low-value." Declare the gap explicitly.
-
Follow all pagination cursors to completion.
- For paginated APIs: follow
next_cursor,next_page,has_more: false, or equivalent end-of-list signal provided by the source. - Confirm explicitly that the source has signaled "end of data" — do not stop because you got enough results to construct an answer.
- Common sources: Notion databases and pages, GitHub API, Stripe API, any REST API with
pageorcursorparameters, HAR fileentriesarray, JSON Lines log files.
- For paginated APIs: follow
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.
- 2mo ago First seen · 74 lines · 60 tokens per session scan A 10ef9a35aec8
large-payload-completeness is a skill published in the GitHub repository avizmarlon/agent-skills (2 stars, last pushed 4mo ago), licensed MIT. It adds 60 tokens to every session and 1,264 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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