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 tobihagemann/turbo --skill evaluate-findingsgit clone --depth 1 https://github.com/tobihagemann/turboWrote 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/tobihagemann/turbo/evaluate-findings)<a href="https://agentmods.dev/skills/tobihagemann/turbo/evaluate-findings"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/evaluate-findings/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/tobihagemann/turbo/evaluate-findings"><img src="https://agentmods.dev/badge/skills/tobihagemann/turbo/evaluate-findings.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 | $0.00040 | $0.04991 |
| Opus 5.5 | $0.00016 | $0.01996 |
| Sonnet 5.5 | $0.00008 | $0.00998 |
| Haiku 4.5 | $0.00004 | $0.00499 |
Grade A, and why
evaluate-findings 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluate Findings
Assess external feedback (code reviews, AI suggestions, PR comments) with adversarial verification. Triage findings into actionable verdicts. Do not apply fixes.
Step 1: Assess Each Finding
If you already assessed a finding earlier in this session and recorded a verdict of Skip or Escalate — for example when an iterating loop re-runs review and the same finding resurfaces — do not re-adjudicate it from scratch. When a loop ledger path under .turbo/loops/ is in context, read it and treat the Skip and Escalate verdicts it records the same way, whichever session recorded them. When the re-reported finding matches one you already judged (same location and substance) and presents no new evidence beyond what your recorded reason already accounts for, keep that verdict and reason without re-reading the code, re-verifying, or routing it to the Devil's Advocate in Step 2. Assess fresh only when the finding raises materially new evidence, or when neither this session nor the ledger holds a verdict for it.
When several findings rest on a shared premise — for example a source-of-truth choice — verify that premise once before adjudicating them individually. Findings whose premise holds proceed through normal per-finding verification; when it fails, they are all Skip, citing the refuted premise.
When a plan governs the work, re-read the decisions it records before adjudicating. Having read it earlier in the session does not count: once it falls out of context, a recorded decision is indistinguishable from no decision at all.
The repo's backlog is .turbo/backlog.md at the repo root, or at the main checkout's root inside a linked worktree. A legacy .turbo/improvements.md beside it counts as backlog too. When the repo keeps a backlog, search it before adjudicating for the paths and symbols the findings name and for entries on the same subject, leaving out any entry the work in hand sets out to implement. Read every matching entry: run the search uncapped, splitting it into one search per finding when the combined output is too large to read, since output cut off or capped before its end settles nothing about the entries it left out. An entry that parks all or part of the change a finding asks for records a decision to defer it. When that finding holds and acting on it would make the change now, treat it as one that would reverse a decision the user made earlier, naming the entry as the original decision and quoting any condition it records for taking the change up. An entry that only shares the finding's area leaves the verdict to the finding's merits. In both cases, weigh what the entry records about that code when verifying the claim and assessing severity.
What ships with it
1 file 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 Changed · +2 lines eaddb08f1db8
- 3d ago Changed · -23 tokens per session 91713f0c83d4
- 20d ago Changed · +1 lines b12c2cb9b6ac
- 21d ago Changed f55b0942a2de
- 24d ago Changed · +2 lines b8744dc68a90
- 25d ago Changed 44ca1b764777
- 28d ago Changed 1692a24db4e3
- 1mo ago Changed · +1 lines 06273f5ec748
- 1mo ago Changed · +2 lines a662ea5853f8
- 1mo ago First seen · 149 lines · 63 tokens per session scan A f662ef51deab
evaluate-findings is a skill published in the GitHub repository tobihagemann/turbo (420 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 4,991 once invoked, about $0.0002 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.
evaluate-findings compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| evaluate-findingstobihagemann/turbo | Skill | 420 | today | MIT | A | 40 | Claude Code |
| code-review-checklistmohitagw15856/pm-claude-skills | Skill | 1.5k | today | MIT | A | 88 | Claude Code, Codex |
| pr-description-writermohitagw15856/pm-claude-skills | Skill | 1.5k | today | MIT | A | 80 | Claude Code, Codex |
| best-practicesaddyosmani/web-quality-skills | Skill | 3.1k | 2mo ago | MIT | A | 49 | Claude Code |
| security-specialistfabricioctelles/skills | Skill | 134 | 11d ago | Apache-2.0 | A | 72 | Claude Code, Codex |
| agent-plugin-evalfabricioctelles/skills | Skill | 134 | 11d ago | Apache-2.0 | A | 75 | Claude Code, Codex |
| fold-findingsgtrabanco/agentic-workflow | Skill | 23 | today | MIT | A | 65 | Claude Code |