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 agentmods add skills/plainlang/plain-forge/add-featurenpx skills add plainlang/plain-forge --skill add-featuregit clone --depth 1 https://github.com/plainlang/plain-forgeWhat 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 | $0.00128 | $0.02443 |
| Opus 5 | $0.00064 | $0.01222 |
| Sonnet 5 | $0.00026 | $0.00489 |
| Haiku 4.5 | $0.00013 | $0.00244 |
Grade A, and why
add-feature 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 2d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Add Feature
Always use the skill load-plain-reference to retrieve the ***plain syntax rules — but only if you haven't done so yet.
add-feature is the continuous-loop counterpart of forge-plain. Where forge-plain bootstraps an entire project from scratch, add-feature adds a single feature to an existing set of .plain specs. It begins with a short intent phase, then runs the authoring loop scoped to one feature.
Core loop: one question → one answer → write to disk
This loop starts after Phase 0. Each iteration is a single question followed by an immediate write:
- Ask one focused question via
AskUserQuestion— never bundle two. Shape it so any plausible answer maps directly to one writable snippet: a single behavior, concept, attribute, edge case, or constraint — not an open-ended design question. Bad shape: "How should the feature behave?" Good shape: "When the user submits an empty title, should the request be rejected with HTTP 400, accepted with a default title, or something else?" Offer concrete options plus a free-form catch-all whenever the answer space is predictable. - Author immediately — the moment the user answers, write the snippet to disk (see 2b for which skill to route to). Do not wait for "enough" context; eager writes are the point. A snippet that is wrong on the first try is expected — the next question corrects it, and the user can read exactly where things stand after every step.
- Refine on the next question, which often extends or corrects what was just written.
One question per call, but drill as deep as the topic needs. "One question" governs the AskUserQuestion call, not the topic. If an answer is vague or leaves real choices open, the next question drills into the same topic — another iteration of the loop — until it is concrete enough to write. Stopping early and writing on top of a vague answer is worse than one more focused follow-up.
Fix contradictions in place. If a later answer refines or contradicts a snippet already on disk, edit that snippet right now. Never leave stale intent on disk; surface a non-trivial change in the next question.
What ships with it
2 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.
- 2d ago First seen · 141 lines · 128 tokens per session scan A 453c525bfcdf
add-feature is a skill published in the GitHub repository plainlang/plain-forge (55 stars, last pushed 4d ago), licensed MIT. It adds 128 tokens to every session and 2,443 once invoked, about $0.0006 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…