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 zszz3/AgentRecall --skill verification-before-completiongit clone --depth 1 https://github.com/zszz3/AgentRecallWrote 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/zszz3/agentrecall/verification-before-completion)<a href="https://agentmods.dev/skills/zszz3/agentrecall/verification-before-completion"><img src="https://agentmods.dev/badge/skills/zszz3/agentrecall/verification-before-completion/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/zszz3/agentrecall/verification-before-completion"><img src="https://agentmods.dev/badge/skills/zszz3/agentrecall/verification-before-completion.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 | $0.00031 | $0.00402 |
| Opus 5.5 | $0.00012 | $0.00161 |
| Sonnet 5.5 | $0.00006 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
verification-before-completion 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
Verification Before Completion
Core rule
NO COMPLETION CLAIMS WITHOUT FRESH VERIFICATION EVIDENCE
Confidence, old results, a clean-looking diff, or another agent's report are not evidence.
Verification gate
Before saying that anything works:
- Identify the command or observation that proves the exact claim.
- Run the complete verification now, not from memory or an earlier turn.
- Read the full output, exit code, failure count, and warnings.
- Compare the evidence with the claim and the acceptance criteria.
- If it does not prove the claim, report the actual state and remaining gap.
- Only then state the successful result and cite the evidence.
Evidence by claim
| Claim | Required evidence |
|---|---|
| Tests pass | Fresh test output with zero failures |
| Build succeeds | Fresh build command with exit code 0 |
| Bug is fixed | Original reproduction or regression test now passes |
| UI behavior works | Runtime observation of the requested interaction |
| Ready to merge | Relevant tests, build, diff review, and repository checks |
| Requirement is complete | Each acceptance criterion mapped to evidence |
A narrow test proves only its narrow behavior. Typecheck is not a build; a build is not an interaction test; a screenshot is not proof of persistence or backend behavior.
Regression tests
For a new regression test, witness the red-green cycle. When practical, revert the fix temporarily, confirm the test fails, restore the fix, and confirm it passes.
Reporting
- State exactly which commands ran and their results.
- Separate verified facts from unverified assumptions.
- Mention skipped checks and why they were skipped.
- Do not use “should”, “probably”, or “looks fixed” as substitutes for evidence.
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 First seen · 50 lines · 31 tokens per session scan A 2b3e8b0596f8
verification-before-completion is a skill published in the GitHub repository zszz3/AgentRecall (594 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 402 once invoked, about $0.0001 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
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.