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 fzfclee/consulting-skills --skill signal-vs-noise-filtergit clone --depth 1 https://github.com/fzfclee/consulting-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/fzfclee/consulting-skills/signal-vs-noise-filter)<a href="https://agentmods.dev/skills/fzfclee/consulting-skills/signal-vs-noise-filter"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/signal-vs-noise-filter/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/fzfclee/consulting-skills/signal-vs-noise-filter"><img src="https://agentmods.dev/badge/skills/fzfclee/consulting-skills/signal-vs-noise-filter.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 | $0.00057 | $0.00764 |
| Opus 5 | $0.00028 | $0.00382 |
| Sonnet 5 | $0.00011 | $0.00153 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
signal-vs-noise-filter 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 12d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Signal vs Noise Filter
Use this skill to identify which observations should change the diagnosis or action plan.
Method Notes
- A signal is information that changes the likely explanation, priority, risk, timing, or next action.
- Noise may be true but not decision-relevant.
- The method works best after an initial evidence map or fact clarification.
Required Inputs
Collect or infer these inputs before execution:
- decision question or action choice
- list of observations, events, facts, cues, and concerns
- baseline expectation before the event
- timing of key changes
- available evidence strength
If an input is missing, mark it as missing, state the assumption used, and add a validation action.
When Not To Use
Do not use to repair unreliable evidence or fill basic situation gaps. Use evidence-map when facts, claims, sources, and confidence are mixed; use 5w1h-analysis when who, what, when, where, why, or how is missing. Use this method only after the input is good enough to judge decision relevance.
Step-by-Step Execution
| Step | Required input | How to execute | Output |
|---|---|---|---|
| State the baseline | Prior expectation, normal pattern, status quo. | Clarify what would have happened if nothing material changed. | Baseline expectation. |
| List candidate signals | Observations, events, user concerns, timing. | Convert each item into a candidate signal. Remove duplicates. | Candidate signal list. |
| Rate decision relevance | Decision question, action options. | Ask whether this item changes explanation, priority, risk, timing, stakeholder stance, or validation need. | Relevance rating. |
| Rate evidence strength | Source, directness, recency, corroboration. | Mark each candidate as strong, medium, weak, or missing. | Evidence-strength rating. |
| Separate noise | Low-relevance or low-evidence items. | Keep true-but-not-actionable items in a noise list so they do not dominate the plan. | Noise list. |
| Name the core signal | High-relevance and sufficiently evidenced items. | Select the 1-3 signals most likely to change action. | Core signal diagnosis. |
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.
- 12d ago First seen · 79 lines · 57 tokens per session scan A 34c9bc3d1625
signal-vs-noise-filter is a skill published in the GitHub repository fzfclee/consulting-skills (4 stars, last pushed 24d ago), licensed Apache-2.0. It adds 57 tokens to every session and 764 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 2026-08-31.
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