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 run-regressiongit 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/run-regression)<a href="https://agentmods.dev/skills/insideout-ai/make-it-work/run-regression"><img src="https://agentmods.dev/badge/skills/insideout-ai/make-it-work/run-regression/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/run-regression"><img src="https://agentmods.dev/badge/skills/insideout-ai/make-it-work/run-regression.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 | $0.00075 | $0.11060 |
| Opus 5.5 | $0.00030 | $0.04424 |
| Sonnet 5.5 | $0.00015 | $0.02212 |
| Haiku 4.5 | $0.00007 | $0.01106 |
Grade B, and why
run-regression scanned grade B with 1 finding 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.
Enumerates other installed skillsmediumAgent snooping
Other skills' SKILL.md files reveal prompts, capabilities and secrets that should be invisible to peers.
For token runs, group tokens by prefix. For each prefix, find the repo as above (`repo not found` is a validation reason for that prefix's tokens), read its `.claude/skills/test-strategy/SKILL.md`, and use every location 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 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 · -4 lines 086b350a5e33
- 5d ago Changed · +12 lines 0413531d0ce9
- 8d ago Changed d7eb1758be72
- 9d ago Changed · +87 lines · +14 tokens per session scan A → B d847eeadfb69
- 16d ago Changed b8f893b25013
- 18d ago Changed · +2 lines e7c57da7325a
- 22d ago Changed · -7 lines 0642c2e1db4a
- 23d ago Changed · +1 lines · +4 tokens per session bc461b7e7319
- 24d ago Changed · +32 lines 2e7765b76a5c
- 25d ago First seen · 275 lines · 57 tokens per session scan A 4e2fa73c700f
run-regression is a skill published in the GitHub repository insideout-ai/make-it-work (12 stars, last pushed today), with no licence file. It adds 75 tokens to every session and 11,060 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (enumerates other installed skills). 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.