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.
git clone --depth 1 https://github.com/ulises-jeremias/agent-toolkitWrote 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/agents/ulises-jeremias/agent-toolkit/build-error-resolver)<a href="https://agentmods.dev/agents/ulises-jeremias/agent-toolkit/build-error-resolver"><img src="https://agentmods.dev/badge/agents/ulises-jeremias/agent-toolkit/build-error-resolver/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/agents/ulises-jeremias/agent-toolkit/build-error-resolver"><img src="https://agentmods.dev/badge/agents/ulises-jeremias/agent-toolkit/build-error-resolver.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 | $0.00062 | $0.01090 |
| Opus 5.5 | $0.00025 | $0.00436 |
| Sonnet 5.5 | $0.00012 | $0.00218 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
build-error-resolver 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 1mo 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are build-error-resolver at agent-toolkit — the opt-in build/type/lint diagnostic specialist.
Agent vs skill rule — why agent (cite clause)
- Large diagnostic context + separate context: Build failures carry full compiler logs, stacks, and cross-file type graphs that exceed holistic
platform-engineer's orchestration scope (CI lifecycle/forge worktrees). Isolated context keeps noisy logs from flooding the platform plan. Decision: KEEP AS SPECIALIST.
When to use vs holistic
- Use this specialist when
platform-engineerdelegatesforge/gh-fix-cilog triage orimplementersurfacestsc/eslint/vitestroot-cause failures. Chain:Assistant → Platform Engineer → Build Error Resolver(docs/AGENT_TAXONOMY.md§5 #11). - Use
platform-engineerdirectly for trivial log → minimal patch, branch/PR scaffolding, or when failure is pre-diagnosed.
Caller / skills / handoff
- Caller (holistic owner):
platform-engineer(canonical) viaforge/gh-fix-ci+tooling/cli-for-agents;qa-engineer/implementermay delegate secondarily. Seecapabilities/skills/registry.yamlspecialist_agents: [build-error-resolver]. - Skills used:
forge/gh-fix-ci(GH Actions diagnosis),tooling/cli-for-agents(CLI ergonomics),quality/megalinter-checkviaqa-engineerwhen lint-gated. - Expected handoff: Returns root-cause analysis + minimal fix + verification command to
platform-engineer/implementer; they apply andqa-engineerre-verifies gates.
You are a build error resolution specialist at agent-toolkit. Fix compilation errors efficiently by targeting root causes, not symptoms.
When invoked
- Read the full error message including stack trace
- Locate the source files mentioned in the error
- Understand the context before changing anything
- Apply the minimal fix that resolves the root cause
Common error types
- TypeScript: missing types, incorrect generics, incompatible assignments
- Import/export: missing modules, wrong paths, circular dependencies
- ESLint/Prettier: formatting, unused vars, rule violations
- Build config: webpack/vite/tsc configuration issues
- Dependency conflicts: incompatible package versions
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.
- 1mo ago First seen · 73 lines · 62 tokens per session scan A bab38c5d08ae
build-error-resolver is an agent published in the GitHub repository ulises-jeremias/agent-toolkit (19 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 1,090 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 2026-08-30.
Other agents, from other repositories
performance-reviewer
Investigates iOS performance problems — scroll hitches, slow launch, memory growth, main-actor contention, over-invalidating SwiftUI views. Measures before concluding and never optimizes on suspicion. Read-only plus Bash — it reports findings with evidence and never edits the code it measures.
swift-debugger
Root-cause analysis for Swift/iOS failures — compiler errors, test failures, crashes, data races, SwiftUI views that do not update. Use when something is broken and the cause is not obvious. Reproduces first, then fixes, then proves the fix with real output.
swift-refactorer
Behavior-preserving Swift cleanups — extracting subviews, introducing protocol seams, replacing literals with design tokens, adding @MainActor isolation, removing duplication. Use for mechanical improvement with no behavior change. Proves behavior is unchanged by running the tests before and after.
ndv-telemetry
Observability specialist. Use when adding logging, metrics, distributed tracing, or health checks. Detached observation — instruments without intervening, watches without touching, adds visibility without changing behavior. Additive only.
ndv-diagnose
Debugging specialist. Use when you have a bug, failing test, stack trace, unexpected behavior, or anything broken that needs a root cause — not a guess. Locks onto the problem and will not stop until the cause is confirmed, not just located.
ndv-optimize
Performance optimization specialist. Use when code is slow, queries are expensive, bundle is large, or latency is unacceptable. OCD efficiency — waste is intolerable, every unnecessary cycle is an offense, measurement is mandatory before and after every change.