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/eveld/claude/spawn-implementation-agentsnpx skills add eveld/claude --skill spawn-implementation-agentsgit clone --depth 1 https://github.com/eveld/claudeWhat 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 | $0.00019 | $0.01085 |
| Opus 5 | $0.00010 | $0.00543 |
| Sonnet 5 | $0.00004 | $0.00217 |
| Haiku 4.5 | $0.00002 | $0.00109 |
Grade A, and why
spawn-implementation-agents 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spawn Implementation Agents
Orchestrate specialized agents during implementation to keep main agent context under 40k tokens per phase.
The Problem
Without agents, implementing a phase uses ~92k tokens in main agent:
- Read plan & changelog: 15k
- Read existing code files: 30k
- Find usage patterns: 15k
- Write implementation: 10k
- Write tests: 10k
- Run verification: 10k
- Update changelog: 2k
This approaches the 200k context limit and risks compaction.
The Solution
Use agents to isolate heavy operations:
- Main agent: 38k tokens (plan + changelog + summaries + code writing)
- Sub-agents: 60k tokens total (in isolated contexts)
- Total system: 98k tokens (50% safety margin)
5-Phase Orchestration Pattern
Phase 1: Analysis (Parallel)
Spawn simultaneously to gather context:
Task(subagent_type="workflows:codebase-analyzer",
prompt="Analyze existing auth system architecture.
Focus on handler pattern, middleware usage, error handling.
Return 2-3k summary with key patterns and file:line references.")
Task(subagent_type="workflows:codebase-pattern-finder",
prompt="Find similar implementations of authentication handlers.
Return 3k of concrete examples showing handler pattern, validation, errors.")
Task(subagent_type="workflows:thoughts-analyzer",
prompt="Extract insights from changelog.md about previous phase learnings.
Return 2k of key deviations and discoveries that affect this phase.")
Wait for all three. Main agent receives ~8k of summaries.
Phase 2: Implementation (Main Agent)
Main agent writes code using summaries:
- Has patterns from codebase-pattern-finder
- Understands architecture from codebase-analyzer
- Knows previous deviations from thoughts-analyzer
- Writes implementation: 10k tokens
- Total so far: 15k (plan/changelog) + 8k (summaries) + 10k (code) = 33k
Phase 3: Testing (Sequential)
Spawn test writer:
Task(subagent_type="workflows:test-writer",
prompt="Generate tests for AuthHandler following patterns in testing.md.
Test functions: Login(), Logout(), ValidateToken().
Return test code only, ~3k tokens.")
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 · 138 lines · 19 tokens per session scan A c3667602fa5e
spawn-implementation-agents is a skill published in the GitHub repository eveld/claude (10 stars, last pushed 6mo ago), licensed MIT. It adds 19 tokens to every session and 1,085 once invoked, about $0.0001 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.
Other skills, from other repositories
code-indexing-pipeline
How Infigraph turns source into a graph — adding a language (tree-sitter vs ANTLR grammar-plugin), cross-file call resolution, SCIP compiler-grade enrichment, and file-watch/reindex triage. Use when adding language support, debugging unresolved calls or SCIP import, or triaging stale index/watcher issues.
review-pr-against-issue
Review one or more PRs against the GitHub issue(s) they claim to fix, including fetching PR branches directly when gh can't reach github.com (e.g. gh is authenticated to an enterprise host instead). Use whenever asked "does this PR fix issue.
analysis-subsystems
How Infigraph's multi-repo/group mode and taint analysis work internally — HTTP contract extraction heuristics, cross-service edge linking, combined-graph merge, remote mode, plus taint's line-based tracking and sanitizer heuristic. Use when working on crates/infigraph-core/src/multi/ or src/taint/, or investigating…
opencli-sitemap-author
Use when creating or maintaining OpenCLI site sitemaps: agent-facing navigation, page-state, action, workflow, API-reference, pitfall, and fallback knowledge for a website. Use after browser exploration discovers durable site context, when a sitemap is stale, or when promoting local site knowledge into the repo.
golden-rss
Use when testing the rss golden build.
omh-code-review
This is a Hermes-native code-review workflow skill.