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 olgaiv39/claude-oss-skills --skill implementation-integritygit clone --depth 1 https://github.com/olgaiv39/claude-oss-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/olgaiv39/claude-oss-skills/implementation-integrity)<a href="https://agentmods.dev/skills/olgaiv39/claude-oss-skills/implementation-integrity"><img src="https://agentmods.dev/badge/skills/olgaiv39/claude-oss-skills/implementation-integrity/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/olgaiv39/claude-oss-skills/implementation-integrity"><img src="https://agentmods.dev/badge/skills/olgaiv39/claude-oss-skills/implementation-integrity.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 | $0.00030 | $0.01437 |
| Opus 5 | $0.00015 | $0.00718 |
| Sonnet 5 | $0.00006 | $0.00287 |
| Haiku 4.5 | $0.00003 | $0.00144 |
Grade A, and why
implementation-integrity 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 9d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
implementation-integrity
Audit whether an implementation honestly satisfies the requested task through the real product path, with only necessary artifacts and evidence that supports its completion claims. This is a read-only audit; do not repair, delete, regenerate, commit, or deploy while using this skill.
Central question:
Did the agent solve the requested task honestly through the real implementation path, with only necessary artifacts and evidence that supports its completion claims?
This skill does not replace test-and-debug, public-code-review, or
release-deploy. Intended sequence:
implement-minimal -> test-and-debug -> implementation-integrity
-> public-code-review -> release-deploy
When to use
- After implementation and targeted debugging are complete
- Before public review, release, deployment, benchmark submission, or hackathon submission
- For AI-generated code
- For benchmark and verifier-based tasks
- For MCP integrations
- For generated applications
- For test assignments and open-source contributions
When not to use
- Before any implementation exists -> use
oss-planorimplement-minimal - As a general style review -> use
public-code-review - As a substitute for debugging a known failure -> use
test-and-debug - As permission to inspect unrelated private files
- As a guarantee that no hidden cheating exists; it audits the tested scope only
Required inputs
Use, in order of preference:
- Explicit task text and acceptance criteria supplied by the user
- An approved execution plan
- A supplied issue, specification, or benchmark prompt
- A clearly identified commit or diff range
If acceptance criteria are unavailable, do not invent them. Provisional criteria may be derived from explicit repository evidence, but must be labelled as inferred. If there is no usable task definition or baseline, stop and request the missing input.
Baseline selection
Define the comparison baseline before reviewing. Support:
What ships with it
4 files 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.
- 9d ago First seen · 201 lines · 30 tokens per session scan A fc64d0b3eb74
implementation-integrity is a skill published in the GitHub repository olgaiv39/claude-oss-skills (12 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,437 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
agentplane-task-closure-recovery
Use when Agentplane task completion, direct finish, branchpr integration, hosted-close, close-tail PRs, PR metadata, dirty task artifacts, or remote branch divergence need diagnosis or recovery.
reviewing-code-quality
Reviews a diff or module for slipping standards, favoring deletion over rearranging, and ends in one honest verdict. Use when a change risks oversized files, needless layers, feature logic leaking into shared code, or clever indirection. Do not use for a trivial obvious edit, or for a check that is only about whether…
review-deep
Drive the deep-review phase of an automated PR review. Consumes the walkthrough, runs the deterministic deep-review workflow (parallel lenses → adversarial validation → code-enforced threshold/caps), drafts the surviving findings, and completes the review run.
review-orchestrator
Drive an automated PR review. Produces a structured walkthrough plus inline draft comments via the octomux review CLI. NEVER posts to GitHub directly — publishing is human-gated.
audit
Use for PACEflow internal full audit: run five independent review agents, verify evidence from code/tests/logs, de-duplicate findings, and produce a severity-ranked report for release gates or comprehensive code review.
review-pr
Use when reviewing a pull request, posting PR review comments, or when user says /review-pr. Reviews code with parallel agents and posts a pending GitHub review with inline comments.