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 randommonicle/claude-skills --skill no-silent-data-dropgit clone --depth 1 https://github.com/randommonicle/claude-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/randommonicle/claude-skills/no-silent-data-drop)<a href="https://agentmods.dev/skills/randommonicle/claude-skills/no-silent-data-drop"><img src="https://agentmods.dev/badge/skills/randommonicle/claude-skills/no-silent-data-drop/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/randommonicle/claude-skills/no-silent-data-drop"><img src="https://agentmods.dev/badge/skills/randommonicle/claude-skills/no-silent-data-drop.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 | $0.00095 | $0.00618 |
| Opus 5 | $0.00048 | $0.00309 |
| Sonnet 5 | $0.00019 | $0.00124 |
| Haiku 4.5 | $0.00010 | $0.00062 |
Grade A, and why
no-silent-data-drop 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 11d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
No silent data drop
The ASH 2026-06-04 cluster: user and model content silently vanishing from delivered reports through render gates, model-assigned routing keys, and JSON parse failures — discovered by the recipient, never by the pipeline. Layer: norm + playbook — the one-line norm in the global CLAUDE.md is the everyday trigger; this file is the depth.
The rule
Wherever code can discard data — a filter, a gate, a continue, a catch, a ?? fallback
— ask: what happens to the non-matching data? A gate may hide an empty section; it must
never drop content. Anything holding content still renders somewhere, and a safe default
bucket always beats a drop.
How to apply
- Gates hide empty things only. A property-flag render gate once dropped mis-classified photos from delivered reports for weeks; the fix routes non-matching content to a visible default section instead.
- A parse failure never discards content. A truncated model JSON threw in
JSON.parseand five report sections vanished silently. Salvage what can be salvaged, log the stop reason, surface the failure. - Model-emitted enums, keys, and ids used for routing are untrusted input. Clamp to the canonical vocabulary where the value enters AND again at render (stored rows may hold unclamped values), defaulting unknowns to a visible bucket.
- Two renderers share the grouping function. Hand-synced twins drift, and the divergence masks the loss — one representation kept all photos while the other dropped them, so the audit trail looked complete.
- Verify the delivered artifact (count sections, count photos), and be suspicious of any test that asserts content IS dropped — a passing test once encoded the bug.
What this skill does not do
It does not govern how failures are messaged to the user — that is honest-failure-surfacing. It does not apply to filters over data the user never receives (internal telemetry, debug output).
Why
Dropped content is the worst failure class in a document pipeline because the artifact still ships: everything looks successful, and only the recipient can notice the hole. Evidence: ASH LESSONS_LEARNED 2026-06-04 (all entries) and the CLAUDE.md max_tokens truncation lesson; ICC L-008.
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.
- 11d ago First seen · 49 lines · 95 tokens per session scan A f3da8735e1eb
no-silent-data-drop is a skill published in the GitHub repository randommonicle/claude-skills (23 stars, last pushed 4d ago), licensed Apache-2.0. It adds 95 tokens to every session and 618 once invoked, about $0.0005 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 2026-08-31.
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