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 pe-menezes/vibeflow --skill teachgit clone --depth 1 https://github.com/pe-menezes/vibeflowWrote 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/pe-menezes/vibeflow/teach)<a href="https://agentmods.dev/skills/pe-menezes/vibeflow/teach"><img src="https://agentmods.dev/badge/skills/pe-menezes/vibeflow/teach.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 | $0.00072 | $0.02139 |
| Opus 5 | $0.00036 | $0.01069 |
| Sonnet 5 | $0.00014 | $0.00428 |
| Haiku 4.5 | $0.00007 | $0.00214 |
Grade C, and why
teach scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
A clone gets removed with `rm -rf "$REPO_PATH"` — never `rm -rf` without an Copies of this mod
1 near-identical copy found in the catalogue:
- teach — 91% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Description and examples
What it does: Updates .vibeflow/ from natural language: corrects a pattern doc, adds a convention, records a decision, or documents a new pattern. Also imports patterns and conventions from an external reference repo via --from. Prefer corrections outside the auto-generated markers so they survive the next analyze.
Examples:
teach sempre usar camelCase para variáveis de estado— Adds or updates a convention.teach o padrão de API mudou, agora validamos com zod— Updates the relevant pattern or conventions.teach decidimos usar Redis para cache, não in-memory— Logs the decision (e.g. in decisions.md or conventions).teach --from https://github.com/org/platform-patterns— Imports patterns from an external repo.teach --from ./my-patterns --name platform— Imports from a local path with a custom alias.
Language
Detect the language of the user's current request or conversation. Write ALL output in that same language. Technical terms in English are acceptable regardless of the detected language.
Process the user's current feedback and update project knowledge.
The one rule that governs every edit
When editing an existing generated region, everything you write goes
outside the <!-- vibeflow:auto:start/end --> markers — that is what makes
a correction survive the next incremental analyze. Read the target file before
editing it, and never rewrite what is inside the markers; analyze owns that
region. One explicit exception: a new pattern doc created in category (d) is
born with its own markers and its initial content inside them, so analyze owns
that region from the start.
Before starting
.vibeflow/ has to exist: read .vibeflow/index.md directly by path for
orientation. If it isn't there, stop with ".vibeflow/ does not exist. Run
analyze first to create the knowledge base." — don't create it by hand.
Then branch on the input: --from in the user's current request goes to Import from external
repo; anything else goes to Classify the feedback.
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.
- yesterday Changed · -1 lines · -2 tokens per session f7ca27728c19
- 7d ago First seen · 253 lines · 74 tokens per session scan C 326bb694a471
teach is a skill published in the GitHub repository pe-menezes/vibeflow (29 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 2,139 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
instruction-maintainer
Detect reusable rules in user corrections or manual rewrites of AI output, ask whether they should be persisted into copilot-instructions or scoped instructions, and maintain them with minimal changes.
init-workspace-documentation
Skill "init-workspace-documentation" from griddynamics/rosetta, covering agent memory.md, agent memory, preventive rules, what worked and what failed.
ijfw-memory-audit
Audit and clean project memory files. Trigger: 'memory audit', 'clean memory', 'memory health', /memory-audit.
ijfw-handoff
Session handoff generation and loading. Trigger: session end, context full, /handoff.
ijfw-summarize
Generate optimized project context from codebase scan. Trigger: new project, no CLAUDE.md, /ijfw-summarize.
repo-context-ledger
Maintain durable, evidence-based repository context whenever an agent initializes a repository, changes behavior, checkpoints or resumes work, switches AI tools or windows, collaborates through Git, prepares a pull request, or completes a coding task. Use the deterministic runtime to route bounded context, isolate…