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/insideout-ai/make-it-work/close-the-gapsnpx skills add insideout-ai/make-it-work --skill close-the-gapsgit clone --depth 1 https://github.com/insideout-ai/make-it-workWrote 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/insideout-ai/make-it-work/close-the-gaps)<a href="https://agentmods.dev/skills/insideout-ai/make-it-work/close-the-gaps"><img src="https://agentmods.dev/badge/skills/insideout-ai/make-it-work/close-the-gaps.svg" alt="Measured on agentmods" 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 | $0.00043 | $0.01879 |
| Opus 5 | $0.00022 | $0.00940 |
| Sonnet 5 | $0.00009 | $0.00376 |
| Haiku 4.5 | $0.00004 | $0.00188 |
Grade A, and why
close-the-gaps 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 6d 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 — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ticket Refinement Session
Role: Act as a Product Analyst facilitating a live ticket refinement session.
Goal: Read a ticket (from a project management tool or pasted), detect all gaps in the requirements, explore relevant code areas to find conflicts, then ask the user clarifying questions one by one. Combine everything into a final Gherkin-format ticket ready for development.
Usage
/make-it-work:close-the-gaps [TICKET-ID]
Or paste the ticket content directly into the chat after invoking.
Phase 1 — Ticket Ingestion
- If a ticket ID is provided, fetch it via the available MCP integration (e.g., Atlassian, Linear, GitHub Issues).
- If content is pasted, parse it as-is — work with whatever is there, even if vague or incomplete.
- Extract: ticket ID, summary, description, acceptance criteria, linked tickets, and any attachments or comments.
- Acknowledge to the user: "Loaded ticket [ID]: [summary]. Starting analysis..."
Phase 2 — Skills Loading
Look at the list of available skills for this project (shown in the session context). Skills are organized in two layers:
- Domain skills — cover specific functional domains. Load the ones whose scope overlaps with the ticket.
- Use-case skills — cover specific end-to-end flows. Load the ones that are related to the functional domains already loaded, or directly referenced by the ticket.
How to detect which skills to load:
- Read the ticket content and extract key nouns, verbs, and domain terms.
- Match them against skill names and descriptions visible in the session.
- Load relevant domain skills first, then any use-case skills whose scope overlaps with the ticket.
- When a use-case skill references a domain skill (or vice versa), load both — cross-referenced skills often contain the constraints that matter most.
- When in doubt, load the skill — a false positive is cheaper than a missed constraint.
Do not mention which skills were loaded unless the user asks. Just use them.
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.
- 6d ago First seen · 176 lines · 43 tokens per session scan A 93b05d64e42c
close-the-gaps is a skill published in the GitHub repository insideout-ai/make-it-work (11 stars, last pushed 24d ago), licensed MIT. It adds 43 tokens to every session and 1,879 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
check-docs
Kit-maintainer diagnostic (LOCAL-ONLY, not distributed) that audits the README and docs/ for staleness against the repo's real command/skill/CLI roster, stamped tree, and cross-doc consistency. Read-only; produces a severity-ranked findings report.
review-agent-config
Diagnostic gate that checks CLAUDE.md and AGENTS.md for completeness, consistency, and stale artifacts — run right after /cold-start and before /add-feature. Read-only; produces a findings report.
deep-test
Perform a deep test of the codebase, validating functional smoke tests, claim verification, drift checks, license headers, and placeholder checks. Trigger whenever the user asks to run deep tests, check code health, or perform strict verification.
cold-start
Bootstrap the ai/ knowledge layer for a repo that has none — draft the MODULEMAP, diagrams, and guide docs as [inferred] for a human to audit, touching no source code. Use when the maps are still placeholders or the user asks to cold-start, bootstrap, or onboard a repository.
adversarial-audit
Deep, judgement-based adversarial code audit — hunts for stale cross-references, unescaped interpolation, platform gaps, generated-file ownership conflicts, and cross-module consistency rot that no mechanical check can catch. Read-only; writes a dated findings report.
implement-spec
Implement a spec from ai/lab/specs/ exactly as written — zero design decisions, stop-and-report on any spec-vs-reality conflict, tests are the definition of done. Built for lighter implementation models.