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 tomzx/agents --skill slack-kb-individualgit clone --depth 1 https://github.com/tomzx/agentsWrote 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/tomzx/agents/slack-kb-individual)<a href="https://agentmods.dev/skills/tomzx/agents/slack-kb-individual"><img src="https://agentmods.dev/badge/skills/tomzx/agents/slack-kb-individual/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/tomzx/agents/slack-kb-individual"><img src="https://agentmods.dev/badge/skills/tomzx/agents/slack-kb-individual.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 | $0.00048 | $0.00915 |
| Opus 5 | $0.00024 | $0.00458 |
| Sonnet 5 | $0.00010 | $0.00183 |
| Haiku 4.5 | $0.00005 | $0.00092 |
Grade A, and why
slack-kb-individual 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 7d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Individual Slack activity collector
Use when collecting all Slack conversations a specific person participated in during a date range — useful for performance reviews, 1:1 prep, or building a picture of someone's contributions.
What it does
collect_individual_threads.py searches Slack for messages from a given user, deduplicates by thread, then fetches each thread root for reply count and preview. It outputs JSONL (one object per thread: thread_ts, channel, channelName, replies, preview, permalink).
Uses the Slack API directly (search.messages + conversations.replies) — no CLI dependencies. Thread fetching is concurrent (8 workers by default). Run via uv run for automatic dependency management (requests, python-dotenv).
Credentials: reads SLACK_TOKEN and SLACK_COOKIE from .env (searches up from cwd).
Two modes
Full scan
Searches the entire --after/--before date range newest-first and fetches every discovered thread. Pagination uses sort=timestamp&sort_dir=desc with sliding date windows to work around Slack's 100-page cap. Stops automatically at the retention boundary (older messages return empty).
uv run collect_individual_threads.py \
--user tom.rochette --after 2024-10-06 --before 2026-04-08 \
-o tom-threads.jsonl
Incremental update
Loads the existing output JSONL as a cache, then searches only a recent window (default: last 7 days). Threads found in the window are fetched (or re-fetched if they were already cached — they had recent activity). Cached threads outside the window are kept as-is.
# Daily — searches last 7 days, merges with existing cache
uv run collect_individual_threads.py \
--user tom.rochette --incremental -o tom-threads.jsonl
# Custom window — last 14 days
uv run collect_individual_threads.py \
--user tom.rochette --incremental --recent-days 14 -o tom-threads.jsonl
All options
| Flag | Description |
|---|---|
--user (required) |
Slack username (e.g. tom.rochette) |
--after / --before |
Full scan date range (YYYY-MM-DD). Slack's after: is exclusive. |
--incremental |
Incremental mode: search recent window, merge with cache. Requires -o. |
--recent-days N |
Days to search in incremental mode (default 7). |
--channel |
Restrict search to a specific channel name. |
--skip-threads |
List unique threads without fetching them (search only). |
--workers N |
Concurrent thread fetch workers (default 8). |
-o / --output |
Output path, or - for stdout. Default: <user>-threads.jsonl. |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 81 lines · 48 tokens per session scan A 9ccd73dd3888
slack-kb-individual is a skill published in the GitHub repository tomzx/agents (6 stars, last pushed yesterday), licensed MIT. It adds 48 tokens to every session and 915 once invoked, about $0.0002 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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