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 alyssa-dahlberg/agent-skills --skill tooling-feedbackgit clone --depth 1 https://github.com/alyssa-dahlberg/agent-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/alyssa-dahlberg/agent-skills/tooling-feedback)<a href="https://agentmods.dev/skills/alyssa-dahlberg/agent-skills/tooling-feedback"><img src="https://agentmods.dev/badge/skills/alyssa-dahlberg/agent-skills/tooling-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/alyssa-dahlberg/agent-skills/tooling-feedback"><img src="https://agentmods.dev/badge/skills/alyssa-dahlberg/agent-skills/tooling-feedback.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 | $0.00152 | $0.01397 |
| Opus 5.5 | $0.00061 | $0.00559 |
| Sonnet 5.5 | $0.00030 | $0.00279 |
| Haiku 4.5 | $0.00015 | $0.00140 |
Grade A, and why
tooling-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 2mo 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tooling feedback
Field reports about where an agent, skill, or tool could be better — captured in the moment, during real work, when one falls short. The point is to turn a fleeting "that was awkward" into a durable, reviewable signal without derailing the task.
"Tooling" here is deliberately broad. It is not only about tool calls — it covers all three kinds of thing you work through:
- agents — a subagent you delegated to that did the wrong thing or missed something
- skills — a skill that triggered badly, gave awkward steps, or fell short
- tools — a tool or command that behaved unexpectedly or got used wrongly
A report can also be about the agent itself — your own behaviour in the session.
Unlike a scratch backlog, each report is checked in with the PR it came from. That makes it portable (no machine-local session links that die when the transcript is gone) and puts it in front of whoever reviews the change.
When to capture
Log a report the moment an agent, skill, or tool underperforms:
- it missed something it should have caught
- it gave awkward or wrong steps
- it triggered when it shouldn't have, or failed to trigger when it should
- it left you wishing it did more
Also capture whenever the user explicitly asks — e.g. "tooling feedback", "agent feedback", "skill feedback", "log feedback", "file a field report". Treat that as a direct request to write a report now.
If you're unsure it's worth it, log it anyway — these files are cheap and lost signal isn't.
Logging should not derail the user, but the report itself should be thorough. You have the full session in context right now; the person triaging this later will not — they'll have only this file and the PR. Spend that context generously: write down what you were doing, how you got here, and what actually went wrong while you still remember it. A rich report is cheap for you to write now and expensive to reconstruct later.
How to capture
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.
- 2mo ago First seen · 103 lines · 152 tokens per session scan A 1d81f64b4204
tooling-feedback is a skill published in the GitHub repository alyssa-dahlberg/agent-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 152 tokens to every session and 1,397 once invoked, about $0.0006 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 2026-08-31.
tooling-feedback compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| tooling-feedbackalyssa-dahlberg/agent-skills | Skill | 2 | 3mo ago | MIT | A | 152 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |