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 drivestream-lab/prayog-skills --skill learning-extractgit clone --depth 1 https://github.com/drivestream-lab/prayog-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/drivestream-lab/prayog-skills/learning-extract)<a href="https://agentmods.dev/skills/drivestream-lab/prayog-skills/learning-extract"><img src="https://agentmods.dev/badge/skills/drivestream-lab/prayog-skills/learning-extract/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/drivestream-lab/prayog-skills/learning-extract"><img src="https://agentmods.dev/badge/skills/drivestream-lab/prayog-skills/learning-extract.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 | $0.00080 | $0.01304 |
| Opus 5 | $0.00040 | $0.00652 |
| Sonnet 5 | $0.00016 | $0.00261 |
| Haiku 4.5 | $0.00008 | $0.00130 |
Grade A, and why
learning-extract 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 10d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning extract
Producer of structured learning after Pass-1 (implement) and human
wave-acceptance / tip fixes. Gateflow may persist records in a global DB; this skill
only writes a workspace artifact + handoff. /ground-spec still owns the
wave Ground Report and §Contracts produced.
Do not require humans to write learning essays. Infer from repo + bind context. Ask taxonomy chips only when classification is ambiguous.
NON-NEGOTIABLE
- Resolve layout from
.harness/profile.yamlor references/layout-defaults.md. - Inspect the wave under test: bound ticket/initiative/wave/PR/run context when
present; Pass-1 tip vs human-fix window (
gitlog/diff); spec/plan/TASK rows; verify scripts /tests_readme; prior Ground Reports as needed. - Emit learning items with closed taxonomy:
SPEC,SKILL,HARNESS, optionalENV. One primary class per item. Prefer SPEC over SKILL when both fit. - Assign stable ids
L-01,L-02, … (seeprayog-skills/references/id-conventions.md). - Each item includes: summary, evidence (paths/commits), codify hint
(suggested skill / spec area / harness home), status
open|codified. Do not open auto-merge codify PRs. - Write durable artifact
{reports_dir}/Learning-Extract-{initiative}-W{N}.mdwith a human table and a fencedlearning_extract:YAML block (machine payload). This file is PURGE at initiative closure (see artifact-write-contract). - Empty
items: []only when there is no human-fix signal and the tip matches intent — state that rationale explicitly. If human fixes clearly exist and zero items → do notpass(usefindings/ fail-closed). - Do not author the full Ground Report REQ matrix or §Contracts produced —
that remains
/ground-spec. - Do not call Gateflow HTTP / DB as skill success. Worker ingest is the
consumer (H6). Follow
prayog-skills/references/forge-side-effects.md#content-producersfor optional workspace publish. - Ids / paths:
prayog-skills/references/id-conventions.md,prayog-skills/references/artifact-write-contract.md.
What ships with it
7 files 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.
- 10d ago First seen · 111 lines · 80 tokens per session scan A e569a456a255
learning-extract is a skill published in the GitHub repository drivestream-lab/prayog-skills (2 stars, last pushed 2d ago), licensed MIT. It adds 80 tokens to every session and 1,304 once invoked, about $0.0004 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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