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 iwe-org/dev-workspace --skill verifygit clone --depth 1 https://github.com/iwe-org/dev-workspaceWrote 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/iwe-org/dev-workspace/verify)<a href="https://agentmods.dev/skills/iwe-org/dev-workspace/verify"><img src="https://agentmods.dev/badge/skills/iwe-org/dev-workspace/verify/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/iwe-org/dev-workspace/verify"><img src="https://agentmods.dev/badge/skills/iwe-org/dev-workspace/verify.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 | $0.00067 | $0.01035 |
| Opus 5.5 | $0.00027 | $0.00414 |
| Sonnet 5.5 | $0.00013 | $0.00207 |
| Haiku 4.5 | $0.00007 | $0.00103 |
Grade A, and why
verify 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.
How it starts
The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify implementation against the graph
The graph makes claims — tasks checked, requirements SHALL-ed, features marked implemented. This skill tests those claims against the code and reports what doesn't hold. It never fixes anything: the report is the deliverable, and the fixes belong to the skills and sessions it points at.
Steps
- Pick the mode. A plan named or inferable from conversation → plan mode. No plan ("audit the docs", "check for drift") → audit mode (step 5).
- Select the plan. If ambiguous, list the plans under
## Activeindata/plans.md(a just-finished plan may also sit under## Done) and ask. Announce: "Verifying: ". Read the full plan, every spec named in its## Spec changes, and the linked feature/bug docs (iwe retrieve -k data/plans/<key>). - Check three dimensions, collecting issues as CRITICAL / WARNING /
SUGGESTION, each with a concrete recommendation:
- Completeness — every unchecked
- [ ]task is a CRITICAL ("complete it, or tick it if already done"). Every spec in## Spec changesexists and reflects the change (a back-ticked not-yet-created spec still pending at verify time is a CRITICAL). Every### Requirement:in the touched specs has implementation evidence in the codebase — search for it; none found is a CRITICAL. - Correctness — map each requirement to
path:lineevidence and judge whether the implementation matches its SHALL statement (divergence is a WARNING with the file and lines to review). For each#### Scenario:, check the condition is handled in code and covered by a test (uncovered scenario is a WARNING). Run the plan's## Verificationcommands and report their actual output — a failing command is a CRITICAL. - Coherence — the implementation follows the plan's
## Approach(a different approach that works is a WARNING: either the code or the plan should change);## Out of scopeitems stayed out; the## Authoring rulesindata/product.mdwere honored.
- Completeness — every unchecked
- Report and stop. Issues ranked most severe first, then the verdict: ready to ship (zero CRITICAL — hand off to the ship skill) or the blocker list. Do not fix, tick, or edit anything.
- Audit mode — the same discipline over the whole graph, against the
codebase:
- Specs whose requirements the code now contradicts (sample the
highest-traffic specs first:
iwe find --references <key>counts). - Features marked
implementedwith no trace in the code; shipped behavior with no feature doc. - Open bugs (no
stage) — still reproducible? Point at ones whose## Key referencesno longer exist. - Stale map docs: for each
data/codebase/doc, commits touching itssourceafter itscommit(git log --oneline <commit>..HEAD -- <source>) — flag for the map skill's refresh mode. - Consistency: plans with
stage: donestill linked under## Active(iwe find --filter '{stage: done}' --included-by data/plans -f keyscross-checked against the hub sections);iwe schema validateviolations;iwe statsdangling links anddata/orphans other thandata/index. - Report in the same CRITICAL/WARNING/SUGGESTION format, grouped by fix owner: "run the ship skill on X", "update spec Y", "close bug Z".
- Specs whose requirements the code now contradicts (sample the
highest-traffic specs first:
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 · 74 lines · 67 tokens per session scan A 7056eb5cac74
verify is a skill published in the GitHub repository iwe-org/dev-workspace (10 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 1,035 once invoked, about $0.0003 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
agent-state-bar
A design guide for an agent status bar: a short, automatically updated summary of progress, remaining tasks, tool counts, errors, and system state placed near the latest model input.
reward-design
A guide to defining the scores that an AI agent receives during trial-and-error training. It covers checks based on rules, human preferences, or another model, and distinguishes rewarding the final result from rewarding the steps taken.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
agent-evolution
A guide to making an AI agent improve from its past runs. It covers how to judge results, extract reusable lessons, and decide whether to store them as knowledge, instructions, code, or model training.
bad-case-to-dpo
A guide to turning failed production agent runs into training and evaluation data. It explains how to find the first mistake, create preference examples for DPO (Direct Preference Optimization), and check that a fix does not damage other tasks.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.