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 mpecan/tokf --skill tokf-discovergit clone --depth 1 https://github.com/mpecan/tokfWrote 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/mpecan/tokf/tokf-discover)<a href="https://agentmods.dev/skills/mpecan/tokf/tokf-discover"><img src="https://agentmods.dev/badge/skills/mpecan/tokf/tokf-discover/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/mpecan/tokf/tokf-discover"><img src="https://agentmods.dev/badge/skills/mpecan/tokf/tokf-discover.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00020 | $0.00559 |
| Opus 5 | $0.00010 | $0.00280 |
| Sonnet 5 | $0.00004 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
Grade A, and why
tokf-discover 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 9d 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 — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tokf discover — Find Missed Token Savings
Use this skill to analyze Claude Code sessions and find commands that are running without tokf filtering, wasting tokens on verbose output.
Quick Start
Run tokf discover in the project directory to scan recent sessions:
tokf discover
Options
--all— scan all projects, not just the current one--since 7d— only scan sessions from the last 7 days (also24h,30m)--limit 0— show all results (default: top 20)--json— output as JSON for programmatic use--session <path>— scan a specific session file--project <path>— scan sessions for a specific project path
Interpreting Results
The output shows:
- COMMAND — the shell command pattern being run without filtering
- FILTER — the tokf filter that would handle it
- RUNS — how many times it appeared in sessions
- TOKENS — estimated token count of unfiltered output
- SAVINGS — estimated tokens that filtering would save
Workflow
- Run
tokf discoverto identify top savings opportunities - For commands with existing filters: run
tokf hook installto set up automatic filtering - For commands without filters: use
/tokf-filterskill to create a custom filter - Re-run
tokf discoverafter changes to verify improvement
Creating Filters for Unfiltered Commands
If tokf discover shows commands with no matching filter, create one:
# See what a filter would look like
tokf which "the-command --args"
# Use the tokf-filter skill to create a proper filter
# /tokf-filter
JSON Output
Use --json for integration with other tools:
tokf discover --json | jq '.results[] | select(.estimated_savings > 1000)'
The JSON schema includes:
sessions_scanned— number of JSONL files processedtotal_commands— all Bash commands foundalready_filtered— commands already using tokffilterable_commands— commands with available filtersno_filter_commands— commands with no matching filterestimated_total_savings— total estimated token savingsresults[]— per-command breakdown sorted by savings
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.
- 9d ago First seen · 72 lines · 20 tokens per session scan A 3a195529f0c0
tokf-discover is a skill published in the GitHub repository mpecan/tokf (197 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 559 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 2026-08-30.
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