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.
git clone --depth 1 https://github.com/StamKavid/last-ds-mileWrote 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/commands/stamkavid/last-ds-mile/ds-learn)<a href="https://agentmods.dev/commands/stamkavid/last-ds-mile/ds-learn"><img src="https://agentmods.dev/badge/commands/stamkavid/last-ds-mile/ds-learn.svg" alt="Measured on agentmods" 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 | $0.00011 | $0.00473 |
| Opus 5 | $0.00005 | $0.00236 |
| Sonnet 5 | $0.00002 | $0.00095 |
| Haiku 4.5 | $0.00001 | $0.00047 |
Grade A, and why
ds-learn 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 8d 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.
What it actually says
Invoke the capturing-learnings skill now via the Skill tool to determine
whether what just happened clears the bar for a real lesson, and to get the
capture format. (This command's steps below are the mechanical write path;
capturing-learnings's own Core Process covers the judgment call and also
fires as a standalone proactive nudge — the two lists overlap by design, not
by drift; keep them in sync if either changes.) Then:
- If the user provided details in $ARGUMENTS, use them. Otherwise ask what broke and what fixed it.
- Determine tags: always include the current or next pipeline stage
(
ds-framethroughds-handoff, from.last-ds-mile/stages/— same stage/dswould route to next) so the lesson can resurface viaSessionStart's stage-based matching, plus any domain skill(s) (target-leakage-detection,imbalanced-data, etc.) genuinely relevant. A skill-only tag with no stage tag will never resurface automatically — nothing in this plugin re-attaches a captured entry to a skill file, so always include a stage tag if you want this lesson to resurface. - Append one line to
.last-ds-mile/learnings.jsonl(create the file and its parent.last-ds-mile/directory if either doesn't exist yet) with this exact shape:
{"type": "lesson", "recorded_at": "<current UTC timestamp in the same format datetime.now(timezone.utc).isoformat() produces, e.g. 2026-07-10T14:23:45.123456+00:00 — matches what hooks/stop_persist_learnings.py already writes to this same file>", "session_id": "unknown", "title": "<short title>", "what_broke": "<the specific failure>", "what_fixed_it": "<the specific fix>", "tags": ["<stage-or-skill-name>", "..."]}
- Confirm back to the user in one line: what was captured and which tags it was filed under.
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.
- 8d ago First seen · 32 lines · 11 tokens per session scan A ce3a04f51d05
ds-learn is a command published in the GitHub repository StamKavid/last-ds-mile (3 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 473 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 2026-08-31.
Other commands, from other repositories
design-tutorial
Interactive guided tour of Naksha — learn commands through real exercises, discover workflows, and get oriented in under 10 minutes.
alt
Import an Exam Radar (OPTIMETA Alt plugin) export and fold its lecture-emphasis exam signal into the course index — radar.md, a lecture-emphasis column on coverage.md, and a gold-zone weakmap.
weakmap
Priority-ranked weakness report. No arg → fresh report from latest errors per pattern. With concept arg → patch latest report by adding the user-declared weakness, save as new timestamped file.
pattern
Show solution pattern cards from course-index/patterns.md, filtered by topic or keyword.
hwmap
Show HW/example coverage of course sections from course-index/coverage.md. HW density = exam probability; surface the exam-hot zones.
recap
Get a summary of everything you learned this session — concepts, quizzes, and progress.