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 PromptPartner/agentsmith --skill new-feedbackgit clone --depth 1 https://github.com/PromptPartner/agentsmithWrote 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/promptpartner/agentsmith/new-feedback)<a href="https://agentmods.dev/skills/promptpartner/agentsmith/new-feedback"><img src="https://agentmods.dev/badge/skills/promptpartner/agentsmith/new-feedback/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/promptpartner/agentsmith/new-feedback"><img src="https://agentmods.dev/badge/skills/promptpartner/agentsmith/new-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00082 | $0.00579 |
| Opus 5.5 | $0.00033 | $0.00232 |
| Sonnet 5.5 | $0.00016 | $0.00116 |
| Haiku 4.5 | $0.00008 | $0.00058 |
Grade A, and why
new-feedback 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 1mo 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
New feedback note (harness post-incident)
The point is not to fix THIS bug — it's to change the SYSTEM so this whole CLASS is less likely (core/60). Small, specific, traceable to the incident.
When this fires
You were corrected on something a rule could have prevented; you iterated more than you should have; a human stepped in; you re-derived a decision a past session already made. After fixing the immediate thing, capture the lesson.
Absence gate
Before filing or implementing a missing-item defect, confirm absence with an exact, count-based, or untruncated result. Truncated, paginated, filtered, or summarized search output is incomplete evidence.
Fast path — if the Agentsmith CLI is available
Run agentsmith new-feedback "short symptom" — use .agentsmith/agentsmith on macOS/Linux or
.agentsmith\\agentsmith.cmd on Windows when the command is not on PATH. It computes the next number and writes
docs/feedback/NNNN-<slug>.md with the five-stage template, Status open.
Fallback — no CLI
- Next number = (highest existing
docs/feedback/NNNN-*.md+ 1), zero-padded to 4 digits. Numbers are never reused (R9), even if an old entry was archived. - Create
docs/feedback/NNNN-<slug>.md(Status: open) with the five stages:- Evidence / symptom — what was observed, quoted.
- Failure mechanism — WHY the system allowed it (missing rule / vague instruction / absent guardrail / unreached tool / noisy context — almost never "the model was dumb").
- Bounded edit — the smallest change that prevents the whole class.
- Named surface — exactly where it lands (a core rule, a hook, a verify phase, a template); prefer a deterministic surface over prose the model can skip.
- Non-regression — the check that stays red on recurrence. Until it's real, Status stays open.
Report
Name the file, then land the bounded edit on its named surface and flip Status to applied.
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.
- 1mo ago Changed · +4 lines 37a654dbd5b3
- 1mo ago First seen · 36 lines · 82 tokens per session scan A 8de3a3e631fa
new-feedback is a skill published in the GitHub repository PromptPartner/agentsmith (178 stars, last pushed 7d ago), licensed MIT. It adds 82 tokens to every session and 579 once invoked, about $0.0003 per session on Opus 5.5. 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
web-animation-css-animations
CSS animation patterns - transitions, keyframes, scroll-driven timelines, @property, compositor-friendly properties, prefers-reduced-motion.
meta-config-stack-detect
Proposes an agents-inc configuration — a SeedPayload plus a report — from a repository, a stack the user describes, or a description of what they want to build. Load when seeding a configuration for a project that has none, or re-running one after its dependencies changed.
ai-infrastructure-ollama
Local LLM inference with the Ollama JavaScript client -- chat, streaming, tool calling, vision, embeddings, structured output, model management, and OpenAI-compatible endpoint.
ai-infrastructure-replicate
Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training.
ai-infrastructure-together-ai
Together AI SDK patterns for TypeScript — client setup, chat completions, streaming, structured output, function calling, embeddings, image generation, fine-tuning, and OpenAI-compatible endpoints.
ai-observability-langfuse
LLM observability with Langfuse — OpenTelemetry-based tracing, evaluations, prompt management, datasets, and production best practices.