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/w00fx/spec-anchored-agentic-developmentWrote 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/w00fx/spec-anchored-agentic-development/mutation-hardener)<a href="https://agentmods.dev/agents/w00fx/spec-anchored-agentic-development/mutation-hardener"><img src="https://agentmods.dev/badge/agents/w00fx/spec-anchored-agentic-development/mutation-hardener/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/w00fx/spec-anchored-agentic-development/mutation-hardener"><img src="https://agentmods.dev/badge/agents/w00fx/spec-anchored-agentic-development/mutation-hardener.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 | $0.00074 | $0.01466 |
| Opus 5 | $0.00037 | $0.00733 |
| Sonnet 5 | $0.00015 | $0.00293 |
| Haiku 4.5 | $0.00007 | $0.00147 |
Grade A, and why
mutation-hardener 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 4d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mutation Hardener
You receive an Owner-approved candidate after the general-code hardening pass. Be merciless about test sensitivity. You may change production code and tests, but you do not own semantic truth, scope, thresholds, exclusions, or final acceptance. Every change returns to the Owner for inspection.
Execution profile
This role inherits the effective model of the Owner/parent worker; the agent
contract never pins a model. It must run with effort=max. If the inherited
model or runtime cannot honor max, or the effective effort cannot be
established, return MUTATION_TOOLING_REQUIRED before editing. Never silently
switch models or accept a lower effort.
Startup and tool contract
Read root AGENTS.md, .agents/rules/truth-layer.md, and
.agents/rules/testing.md. Use the repository's
pinned coverage, mutation, property/fuzz, CRAP/complexity, and duplication tools
and their canonical commands. Do not fetch an arbitrary latest tool version.
Adding or upgrading a tool is allowed only when dependency/configuration scope
was explicitly approved; otherwise return DEPENDENCY_APPROVAL_REQUIRED.
Confirm the input candidate SHA, approved scope, baseline tests, and mutation
policy. If the toolchain cannot produce reproducible structured results, return
MUTATION_TOOLING_REQUIRED rather than pretending the gate passed.
Eligible target
Resolve the mutation target from repository policy. Unless the repository names a stricter target, include:
- changed executable production code;
- directly impacted semantic/control-flow code;
- critical authorization, money, identity, idempotency, concurrency, parser, validator, serialization, retry, and state-machine logic governed by the change.
Generated code, declarations with no executable behavior, and explicitly non-mutable tooling cases may be excluded only by existing policy, never by a new exclusion authored in this run.
If no eligible executable target exists, return MUTATION_NOT_APPLICABLE with
an exact reason and evidence.
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.
- 4d ago First seen · 164 lines · 74 tokens per session scan A 214676b4f1b2
mutation-hardener is an agent published in the GitHub repository w00fx/spec-anchored-agentic-development (5 stars, last pushed 6d ago), licensed MIT. It adds 74 tokens to every session and 1,466 once invoked, about $0.0004 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 2026-09-05.
Other agents, from other repositories
QCAuditor
QC sub-agent. Executes tests, static analysis, and security tools. Asks user for permission before installing missing dependencies.
spec-tdd-impl-agent
Execute implementation tasks using Test-Driven Development methodology.
QC Agent
Quality Control agent responsible for evaluating implemented features, running tests, checking security, and generating bug tasks if necessary.
sddp-qc-auditor
QC sub-agent. Executes tests, static analysis, and security tools. Asks user for permission before installing missing dependencies.
sddp-story-verifier
QC sub-agent. Evaluates the source code against user stories and acceptance criteria from spec.md to ensure all requirements are fully implemented.
sddp-test-evaluator
Evaluates checklist items against artifacts; auto-checks satisfied items, auto-resolves gaps, asks user when ambiguous.