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 insideout-ai/make-it-work --skill executegit 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/execute)<a href="https://agentmods.dev/skills/insideout-ai/make-it-work/execute"><img src="https://agentmods.dev/badge/skills/insideout-ai/make-it-work/execute/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/insideout-ai/make-it-work/execute"><img src="https://agentmods.dev/badge/skills/insideout-ai/make-it-work/execute.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 | $0.00060 | $0.19847 |
| Opus 5.5 | $0.00024 | $0.07939 |
| Sonnet 5.5 | $0.00012 | $0.03969 |
| Haiku 4.5 | $0.00006 | $0.01985 |
Grade A, and why
execute 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 yesterday.
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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
3 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.
- yesterday Changed a606e2100c1e
- 5d ago Changed · +2 lines ec623cad3b6c
- 6d ago Changed · +3 lines · -65 tokens per session 818acb8ea0df
- 9d ago Changed · +4 lines adf4798a2b2c
- 14d ago Changed d6bc72cb4dd9
- 16d ago Changed · +4 lines c2197b295337
- 17d ago Changed · +6 lines ed5be97ae771
- 18d ago Changed · +17 lines 262d939f9b7c
- 21d ago Changed · +6 lines 051f0c8644be
- 22d ago First seen · 366 lines · 125 tokens per session scan A 31c5a8cfa2a0
execute is a skill published in the GitHub repository insideout-ai/make-it-work (12 stars, last pushed today), with no licence file. It adds 60 tokens to every session and 19,847 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 2026-10-01.
Other skills, from other repositories
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.
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.
fix-bug
Fix a bug in this repository the safe way — reproduce first, failing regression test, root cause via the maps, surgical fix, review, ledger entry. Use whenever the user reports broken, wrong, or crashing behavior.
validate
Validate Semantica pipelines, extraction quality, graph schemas, and ontology consistency. Returns structured error/warning checklists. Uses PipelineValidator, PipelineBuilder.validatepipeline(), GraphValidator, and OntologyValidator. Sub-commands: pipeline, step, dependencies, extraction, graph, ontology, performance.
agent-evaluation
A guide to measuring whether an AI agent works reliably on the tasks that matter. It covers repeatable test environments, program checks, human-like scoring rules, failure analysis, and experiments that separate model problems from system problems.
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.