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/dragoon0x/usemindit/mindit-correctabilitynpx skills add Dragoon0x/usemindit --skill mindit-correctabilitygit clone --depth 1 https://github.com/Dragoon0x/useminditWhat 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 | $0.00115 | $0.01147 |
| Opus 5 | $0.00057 | $0.00574 |
| Sonnet 5 | $0.00023 | $0.00229 |
| Haiku 4.5 | $0.00012 | $0.00115 |
Grade A, and why
mindit-correctability 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 2d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
mindit-correctability
The eighth of the eight forces. Run this when the question is "if this is wrong, how fast can we find out and how cleanly can we fix it."
The force
Most design decisions are bets. Some bets are right; some are wrong. The cost of being wrong depends on three things: how quickly can the team tell, how broadly does it apply when it's wrong, and how cleanly can they reverse course.
A change behind a 5 percent rollout with a kill switch and rich telemetry is cheap to be wrong about. A change shipped to 100 percent of users with no instrumentation and a destructive data migration is catastrophic to be wrong about.
Correctability asks: given that this decision might be wrong, what is the cost of being wrong, and what mechanisms are in place to recover?
When to run this
- The user is planning a launch, rollout, or migration.
- The user mentions A/B tests, feature flags, percentage rollouts, kill switches, or telemetry.
- The user is about to make a broad-impact change: default change, pricing change, schema migration, brand refresh, design system overhaul.
- The user uses words like "rollback," "revert," "kill switch," "monitoring," "blast radius."
How to analyze
-
Identify the decision. What specifically is being shipped? Who is affected? What state changes?
-
Map the recovery dimensions. For each criterion, ask both "can we" and "have we planned to."
-
Distinguish "reversible in theory" from "reversible in practice." A change is only practically reversible if the rollback is planned, rehearsed, and instrumented.
-
Surface the blast radius explicitly. Even good correctability is wasted if the blast is too big. A bad change behind a 100 percent rollout with a working rollback still affected every user for the duration of the rollback.
-
Check
anti-patterns.yaml. -
Write the artifact.
Rubric
| Criterion | Weight | What you are scoring |
|---|---|---|
| User-side reversibility | 0.20 | Can users undo what they did inside this design? Cancel, revert, restore, change-of-mind paths. |
| System-side reversibility | 0.20 | Can the team roll back the change? Feature flag, deploy revert, data migration reversibility. |
| Detection latency | 0.20 | How quickly will the team know if this change is wrong? Telemetry, error tracking, support tickets, business metrics. |
| Blast radius | 0.20 | If wrong, how many users does it affect before someone can intervene? 5 percent rollout is small; 100 percent is large. |
| Pre-commit checks | 0.20 | What gates does the change pass through before broad release? Internal test, beta cohort, percentage rollout, holdback. |
What ships with it
1 file 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.
- 2d ago First seen · 79 lines · 115 tokens per session scan A 1bdd5cdc4d38
mindit-correctability is a skill published in the GitHub repository Dragoon0x/usemindit (2 stars, last pushed 3mo ago), licensed MIT. It adds 115 tokens to every session and 1,147 once invoked, about $0.0006 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 2026-08-31.
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