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 YSAA1/harness-workflow --skill reviewgit clone --depth 1 https://github.com/YSAA1/harness-workflowWrote 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/ysaa1/harness-workflow/review)<a href="https://agentmods.dev/skills/ysaa1/harness-workflow/review"><img src="https://agentmods.dev/badge/skills/ysaa1/harness-workflow/review/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/ysaa1/harness-workflow/review"><img src="https://agentmods.dev/badge/skills/ysaa1/harness-workflow/review.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 | $0.00048 | $0.01322 |
| Opus 5.5 | $0.00019 | $0.00529 |
| Sonnet 5.5 | $0.00010 | $0.00264 |
| Haiku 4.5 | $0.00005 | $0.00132 |
Grade A, and why
review 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 15d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review and verification
Combine problem discovery and the evidence checking of completion claims into a single review. The review scope comes from the user's request; WIP can be reported as findings — implementation does not have to be finished before review.
Process
- Clarify the diff/version under review, the target behavior, and the required acceptance. Examine the relevant source code, tests, documentation, and existing evidence; do not substitute the implementer's explanation for inspection. Also search the repo for every file that documents how code should be written and list them as the standards sources:
CODING_STANDARDS.mdandCONTRIBUTING.mdare mandatory whenever they exist, plus lint/format convention notes, ADRs for the area touched, andCONTEXT.mdterms; skip rules that tooling already enforces. - Check correctness, contracts, boundaries, and regressions along concrete failure paths; check conventions against the standards sources, code changes always layer on the smell baseline in
references/smell-baseline.md(it applies even when the repo documents nothing; docs-only changes skip it). A documented repo standard wins and suppresses any smell it endorses; a smell is always a judgement call ("possible Feature Envy"), never a hard violation, listed as Minor by default and never blocking. Important risks warrant deep investigation; there is no need to pad findings or run a fixed number of review rounds. - When independent inspection adds value and the runtime allows it, use a read-only subagent with a clear scope; when dispatching several review axes (e.g. a standards axis and a contract/correctness axis), issue them together in one message, in the foreground, and aggregate their reports side by side without merging or reranking; the absence of a subagent is not itself a failure. If the user or the project explicitly requires independent review, honestly flag it when that condition is unmet. When the environment has external delegation skills such as cli-delegate, a read-only independent re-check across model CLIs is allowed; its result is a claim, and the evidence still returns to the main tree for re-verification.
- Map the required criteria to pass / fail / unknown. fresh evidence means the evidence still applies to the current relevant code, configuration, and inputs; actual outputs unaffected by subsequent changes can be reused, and do not automatically expire because the skill changed or a commit already exists. A completion claim counts as evidence only via the actual output of runnable checks; implementer paraphrase or verbal assertion does not count.
- Only fill in missing or invalidated checks. Use cold re-verification according to evidence ambiguity and risk; do not require delegating another agent to re-run the same tests.
- Judge the specific completion claim: ready is possible only when there are no unresolved blocking issues and all required criteria pass. Critical/Important correctness, data, permission, or contract issues must all be resolved; list non-blocking suggestions separately. Do not delete or alter tests or acceptance scripts in order to pass; if the acceptance contract genuinely must change, get the user's consent first.
- Persist findings (conditionally): all findings handled on the spot → do not persist to disk. If there are findings pending across sessions and the project has a recovery surface → write to this track's state surface: state.md Evidence records the findings list (number/location/severity) and the ready verdict, and Next records the pending fixes; when findings are numerous or need independent tracking, persist to
docs/reviews/YYYY-MM-DD--<topic>-review.md, linked from this track's row or from state. Projects without a recovery surface keep in-session handoff and create no new state files.
What ships with it
9 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.
- agents/openai.yaml 155 B
- references/adversarial-reviewer-prompt.md 1.1 KB
- references/attack-taxonomy.md 3.3 KB
- references/capability-recommendations.md 554 B
- references/cold-verifier-prompt.md 863 B
- references/cross-cutting-anti-patterns.md 1.0 KB
- references/evidence-ladder.md 2.0 KB
- references/premature-completion-patterns.md 638 B
- references/smell-baseline.md 2.3 KB
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.
- 15d ago Changed · +1 lines a1f294859f68
- 1mo ago Changed · +1 lines · +1 tokens per session d39fa0984afd
- 1mo ago Changed 48ae35e9e219
- 1mo ago Changed · -52 lines · -8 tokens per session 79e3ba13f4d2
- 1mo ago First seen · 92 lines · 55 tokens per session scan A 3ec45de7c930
review is a skill published in the GitHub repository YSAA1/harness-workflow (3 stars, last pushed 16d ago), licensed MIT. It adds 48 tokens to every session and 1,322 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 2026-08-31.
Other skills, from other repositories
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intent-correlation-analysis
Analyze a bounded IntentCorrelationPacketV1 and propose reviewable links among user inputs, execution slices, change units, commits, artifacts, and validation outcomes. Use when reconstructing why an observed coding-agent change exists or when Studio needs evidence-backed Intent correlation. Do not use for raw…
generate-harness-dsl
Generate, revise, or review complete Harness as Code .harness files when a coding-agent workflow, agent role, skill, tool contract, MCP connection, runtime, or deployment must be compiler-valid and resolvable with @qoder-ai/harness.
review-work
Post-implementation gate review: run manual QA on the real surface yourself, then launch ONE gate reviewer (never a panel) to audit goal, constraints, code quality, security, missed context, and QA evidence. Use before a PR handoff or when the user explicitly asks to review completed work.
pr-blocker-summarizer
Summarizes open pull requests into a blockers-first standup digest. Activates when the user asks to summarize open PRs, find blocked pull requests, generate a PR standup, or triage review backlog from a PR export.