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 commands/seongwoongcho/adaptive-harness/evalgit clone --depth 1 https://github.com/SeongwoongCho/adaptive-harnessWrote 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/commands/seongwoongcho/adaptive-harness/eval)<a href="https://agentmods.dev/commands/seongwoongcho/adaptive-harness/eval"><img src="https://agentmods.dev/badge/commands/seongwoongcho/adaptive-harness/eval.svg" alt="Measured on agentmods" 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 | $0.00011 | $0.00649 |
| Opus 5 | $0.00005 | $0.00324 |
| Sonnet 5 | $0.00002 | $0.00130 |
| Haiku 4.5 | $0.00001 | $0.00065 |
Grade A, and why
eval 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 6d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
adaptive-harness-eval
Manually trigger evaluation of a task result. Use this to re-evaluate the last completed task, evaluate a specific change, or run evaluation when auto-evaluation did not fire.
Parsing Arguments
--last— (default) Evaluate the most recent task result in this session--file=path— Evaluate a specific file or diff (e.g.,--file=src/auth.py)- No argument — same as
--last
Execution Steps
Step 1: Identify What to Evaluate
If --last or no argument:
Find the most recent evaluation context in this session:
- Check
.adaptive-harness/sessions/{session_id}/for the latest evidence files - Read
.adaptive-harness/sessions/{session_id}/evidence/— sort by timestamp, take most recent - If no evidence files exist, check if the last conversation turn produced code changes
If no evaluation context found:
No recent task result found to evaluate.
To evaluate a specific file: /adaptive-harness:eval --file=path/to/file
To run a task first: /adaptive-harness:run <task description>
If --file=path:
Read the specified file(s) to use as evaluation input. Accept glob patterns (e.g., --file=src/**/*.py).
Step 2: Collect Evidence
Read all evidence files from .adaptive-harness/sessions/{session_id}/evidence/ sorted by timestamp:
Read(".adaptive-harness/sessions/{session_id}/evidence/")
Also collect any git diff if available:
git diff --stat HEAD 2>/dev/null || echo "no git"
git diff HEAD 2>/dev/null | head -200
Step 3: Spawn Evaluator Agent
Task(
subagent_type="adaptive-harness:evaluator",
prompt="Manually evaluate this task result.\n\nEvaluation target: {description of what's being evaluated}\n\nEvidence:\n{evidence_summary}\n\nGit diff (if available):\n{diff_output}"
)
Step 4: Display Results
Show evaluation scores and write to state:
Manual evaluation complete.
Overall score: {score} ({PASS|FAIL})
Dimension scores:
correctness: {score} — {brief_reasoning}
completeness: {score} — {brief_reasoning}
quality: {score} — {brief_reasoning}
robustness: {score} — {brief_reasoning}
clarity: {score} — {brief_reasoning}
verifiability: {score} — {brief_reasoning}
Quality gate: {PASSED|FAILED — reason}
Suggestions:
{improvement_suggestions}
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.
- 6d ago First seen · 86 lines · 11 tokens per session scan A 209ce7bdfb06
eval is a command published in the GitHub repository SeongwoongCho/adaptive-harness (8 stars, last pushed 5mo ago), licensed MIT. It adds 11 tokens to every session and 649 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 commands, from other repositories
svsi-review
/svsi-review {slug} # modo interactivo en sesión Claude Code /svsi-review --batch {file} # delega a predrivereview.py (tmux, no en sesión) /svsi-review --stats # métricas acumuladas desde SQLite.
handover
Works for you. Go outside and live. — AI orchestrator that auto-routes tasks to the cheapest model that solves them. 70% run free on local models. Self-auditing, self-improving, zero prompting skill needed. Built with vibe coding by a finance student. Your models, your data.
instinct-status
Shows learned instincts from dqiii8.db, grouped by project and confidence. Internal diagnostic tool — not for user invocation.
quality-gate
Works for you. Go outside and live. — AI orchestrator that auto-routes tasks to the cheapest model that solves them. 70% run free on local models. Self-auditing, self-improving, zero prompting skill needed. Built with vibe coding by a finance student. Your models, your data.
test-team
Works for you. Go outside and live. — AI orchestrator that auto-routes tasks to the cheapest model that solves them. 70% run free on local models. Self-auditing, self-improving, zero prompting skill needed. Built with vibe coding by a finance student. Your models, your data.
checkpoint
Works for you. Go outside and live. — AI orchestrator that auto-routes tasks to the cheapest model that solves them. 70% run free on local models. Self-auditing, self-improving, zero prompting skill needed. Built with vibe coding by a finance student. Your models, your data.