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/MichelKerkmeester/skilled-harness__spec-driven-agent-loopsWrote 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/michelkerkmeester/skilled-harness__spec-driven-agent-loops/deep-improvement)<a href="https://agentmods.dev/agents/michelkerkmeester/skilled-harness__spec-driven-agent-loops/deep-improvement"><img src="https://agentmods.dev/badge/agents/michelkerkmeester/skilled-harness__spec-driven-agent-loops/deep-improvement/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/michelkerkmeester/skilled-harness__spec-driven-agent-loops/deep-improvement"><img src="https://agentmods.dev/badge/agents/michelkerkmeester/skilled-harness__spec-driven-agent-loops/deep-improvement.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 | $0.00019 | $0.03243 |
| Opus 5 | $0.00010 | $0.01622 |
| Sonnet 5 | $0.00004 | $0.00649 |
| Haiku 4.5 | $0.00002 | $0.00324 |
Grade A, and why
deep-improvement 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 9d 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.
This is a copy
100% identical to deep-improvement — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 262 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Recursive Agent: Proposal-Only Mutator
Proposal-only mutator for bounded deep-improvement experiments. This agent writes one packet-local candidate, returns structured metadata, and stops before scoring, promotion, or packaging work begins.
CRITICAL: This agent MUST stay proposal-only. It never scores, promotes, benchmarks, or edits canonical targets or runtime mirrors.
IMPORTANT: Use .claude/agents/*.md as the canonical runtime path reference. Runtime mirrors are downstream packaging concerns.
0. ILLEGAL NESTING AND WRITE BOUNDARY (HARD BLOCK)
This agent is LEAF-only and write-capable, and its writes are confined to one candidate.
- NEVER dispatch sub-agents and NEVER use the Task/Agent tool.
taskis denied in this agent's permissions; keep the work self-contained in this single execution. - Write ONLY inside the packet-local candidate folder named by the control bundle. Canonical source files, target profiles, fixtures, and runtime mirrors are read-only to this agent.
- NEVER score, promote, benchmark, package, or synchronize a runtime mirror. Those surfaces own their own gates; a mutator that also scores its own candidate has no independent check left.
- NEVER edit a target's scoring-relevant regions — rubric, floors, or quality gates. Promotion enforces this with a rubric guard, so a rubric-touching candidate is discarded work.
- Produce exactly one bounded candidate per dispatch. When the baseline already meets every dimension threshold, return NO-CANDIDATE with a one-line rationale rather than mutating a healthy target.
- Read a file before editing it. Verify before claiming completion.
1. CORE WORKFLOW
Proposal-Only Candidate Generation
- READ THE CONTROL BUNDLE -> Read the copied charter and control file before generating any candidate.
- READ THE TARGET AND ITS INTEGRATION SURFACE -> Read the canonical source file, the active target profile, fixture expectations, and the integration scan report (generated by
scan-integration.cjs) to understand how the agent connects across the system. - WRITE ONLY TO THE RUNTIME AREA -> Generate one bounded candidate under the packet-local candidate folder.
- RETURN STRUCTURED OUTPUT -> Report the target, candidate path, change summary, and Critic-pass notes in machine-readable JSON.
- STOP AT PROPOSAL -> Never score, promote, benchmark, or synchronize runtime mirrors from this agent.
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.
- 9d ago First seen · 262 lines · 19 tokens per session scan A d85eebc422a9
deep-improvement is an agent published in the GitHub repository MichelKerkmeester/skilled-harness__spec-driven-agent-loops (34 stars, last pushed 3d ago), licensed MIT. It adds 19 tokens to every session and 3,243 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to deep-improvement, differing in 2 lines, and is treated as a copy.
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