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 jstoup111/ai-conductor --skill remediategit clone --depth 1 https://github.com/jstoup111/ai-conductorWrote 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/jstoup111/ai-conductor/remediate)<a href="https://agentmods.dev/skills/jstoup111/ai-conductor/remediate"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/remediate/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/jstoup111/ai-conductor/remediate"><img src="https://agentmods.dev/badge/skills/jstoup111/ai-conductor/remediate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00049 | $0.04318 |
| Opus 5.5 | $0.00020 | $0.01727 |
| Sonnet 5.5 | $0.00010 | $0.00864 |
| Haiku 4.5 | $0.00005 | $0.00432 |
Grade A, and why
remediate 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.
How it starts
The opening of the file, as written. The whole thing — 274 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Turns a blocking gate into action. When build_review fails, or when prd-audit,
architecture-review --as-built, or the finish verification reports SHIP gaps the daemon would
otherwise HALT on, this skill reasons over each blocking gap and decides how the daemon should
proceed — autonomously where it can, human-in-the-loop only where it must.
Correctness gate: a gap's disposition and its routing target rest on a claim about the gap's
nature. Per the /verify-claims protocol, ground that classification in the audit evidence with a
confidence %, and do not auto-route on an unverified assumption about what the gap is — when the
nature is genuinely uncertain (not just the fix), that low confidence is itself a signal to HALT
for a human rather than to guess a route.
The daemon should be autonomous. So the default is to remediate: translate each gap into concrete, file-scoped work and route it back to the right SDLC step. A HALT is reserved for the three cases a machine genuinely cannot close:
- architectural-clarity — an architectural gap that needs a human decision (ambiguous trade-off, missing ADR, conflicting constraints), not just a code change.
- product-scope — functionality the initial design never accounted for (a real product gap), which needs a human DECIDE amendment.
- unanswerable — a stall-question that cannot be answered from committed artifacts alone and needs more evidence.
If a gap can be turned into concrete work, it is not a HALT. This skill guides planning only: it does not edit code, write tests, or amend the PRD; the owning step does that.
Run when build_review fails, or at SHIP when a prior audit BLOCKED — dispatched by the
conductor on the blocking path.
Engine-selected PRD widening reconciliation mode
Use this branch only when the engine-stamped context declares
mode: "prd-widening-reconciliation". It is one bounded equivalence judgment through the
existing remediate dispatch, not a lifecycle step, a new skill, or a BUILD remediation plan.
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.
- yesterday Changed · +11 lines b7202b750ce9
- 2d ago Changed · +6 lines 7b7704ab34c4
- 5d ago Changed · +4 lines 3c6143175a15
- 6d ago Changed · +9 lines b1a2ec6d95c3
- 13d ago Changed · +1 lines 779c4d6cc1aa
- 16d ago Changed 6809e75991db
- 20d ago Changed · -243 lines · -8 tokens per session 00f46d11331c
- 22d ago Changed · +36 lines e1829f1ae338
- 26d ago Changed · +6 lines 3ce4ae5ecf30
- 1mo ago First seen · 444 lines · 57 tokens per session scan A 281054d3514c
remediate is a skill published in the GitHub repository jstoup111/ai-conductor (7 stars, last pushed yesterday), licensed Apache-2.0. It adds 49 tokens to every session and 4,318 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 2026-08-31.
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