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/archive228/loopkit/self-eval-biasnpx skills add Archive228/loopkit --skill self-eval-biasgit clone --depth 1 https://github.com/Archive228/loopkitWhat 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 | $0.00037 | $0.00900 |
| Opus 5 | $0.00018 | $0.00450 |
| Sonnet 5 | $0.00007 | $0.00180 |
| Haiku 4.5 | $0.00004 | $0.00090 |
Grade A, and why
self-eval-bias 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Eval Bias
An agent that just produced a plan, a diff, or a report cannot fairly grade it in the same context. The reasoning that justified writing it is still loaded — every doubt was already resolved in favor of shipping. Asked to review, the same context reliably returns "looks good, ship it." This is not review. It is rationalization wearing a review's uniform.
The pattern shows up hardest in planner/generator/evaluator architectures where the evaluator drifts toward leniency over long runs — the prompts it reads fill up with the generator's reasoning, and skepticism erodes. (See Prithvi's March 2026 post on the three-agent harness: https://blog.anthropic.com/three-agent-harness-march-2026.)
When to apply
- You just wrote code, a plan, or a claim, and the next step is "confirm it's correct".
- A reviewer verdict comes back positive with no cited line numbers, no failing case explored, no counter-example attempted.
- You're about to mark a feature
passes: true, close an issue, or hand off to the next session. - The evaluator persona in a multi-agent loop has agreed with the last N generator outputs in a row.
Procedure
- Notice the same-context tell. If the review verdict lands in under three sentences and contains "looks correct", "this should work", or "no issues found" without a cited artifact — treat the verdict as unwritten.
- Force a fresh persona. Drop the generation context. Open a new subagent, or at minimum re-prompt with only the artifact (diff, plan, output) and the acceptance criteria — no reasoning trail, no self-justification.
- Demand concrete evidence, not verdicts. The reviewer must cite: the file:line it inspected, the input it ran, the observed output, and the criterion it matched against. "LGTM" without these is a null review — discard it.
- Adversarially probe. Ask the reviewer for the strongest case where the artifact fails. If it can't produce one, the review didn't happen — the reviewer just agreed.
- Run the artifact. For code, exercise it end-to-end (see [[broken-window-check]]). For a plan, walk the first two steps concretely. Same-context confidence collapses fast against a runtime.
- Rotate the reviewer periodically. In long multi-agent loops, re-prompt the evaluator from scratch every ~5 sprints — leniency drift compounds silently.
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 · 46 lines · 37 tokens per session scan A 7d03635ea2f3
self-eval-bias is a skill published in the GitHub repository Archive228/loopkit (753 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 900 once invoked, about $0.0002 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.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…