Better Harness is an open-source platform for studying and improving how coding agents work on tasks, using written harnesses, controlled tests, and evidence from projects and sessions. Coding teams use it to identify workflow problems and turn them into prioritized improvements and verifiable next steps. Its catalogue entries extend coding-agent workflows for supported hosts such as Claude Code, Codex, Cursor, and others.
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 skills add QoderAI/better-harness --skill memory-recapgit clone --depth 1 https://github.com/QoderAI/better-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/skills/qoderai/better-harness/memory-recap)<a href="https://agentmods.dev/skills/qoderai/better-harness/memory-recap"><img src="https://agentmods.dev/badge/skills/qoderai/better-harness/memory-recap/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/skills/qoderai/better-harness/memory-recap"><img src="https://agentmods.dev/badge/skills/qoderai/better-harness/memory-recap.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 | $0.00047 | $0.01971 |
| Opus 5.5 | $0.00019 | $0.00788 |
| Sonnet 5.5 | $0.00009 | $0.00394 |
| Haiku 4.5 | $0.00005 | $0.00197 |
Grade A, and why
memory-recap 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Memory Recap
Turn authorized memories into an actionable retrospective: briefly describe how the user works, then focus on which collaboration problems are worth addressing, what to change next time, and how to evaluate the result. Go beyond profiles or praise, and do not reuse a previously generated profile as the answer to a new analysis.
Scope and inputs
- Preserve the current request's sources, projects, time window, model, and budget. Do not ask again for authorization already given; clarify only when missing information would materially change what is read or sent externally.
- For a small recap, prefer summaries and relevant independent task records. When the user requests “all memories,” enumerate and copy all readable original content within the authorized scope before analyzing it in batches. Clearly distinguish sampling, summary analysis, and full-corpus analysis.
- A global library may contain project knowledge. Preserve native source identity, project binding, content scope, and material role. Do not merge projects by basename or interpret a global path as evidence of personal preferences.
- Reuse existing native Memory interfaces or user-provided exports. Missing sources do not justify automatically expanding into raw sessions, databases, or caches. When using Better Harness or Qoder, read the execution reference as needed.
- Before large model runs, report file count, content size, and expected batch count, and respect the existing budget. Revising a recommendation or creating this skill does not require rescanning or rerunning the entire corpus.
For each document actually read, retain a stable label, source ID, host, scope, material role, content SHA-256, capture time, and original line numbers. Record failures, size limits, and partial coverage. Modification time is not event time; “not read” does not mean “no memory exists.”
When the user requests consolidation, preserve the complete original text and a mapping from original line numbers to the combined file. Keep inputs, analysis outputs, and private paths in a local directory outside native Memory libraries so future analyses do not treat their own conclusions as new evidence. Fold only byte-identical model inputs by content hash, retaining aliases. An index, summary, and working compilation of the same event do not count as separate behaviors.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 90 lines · 47 tokens per session scan A bd58f321d95a
memory-recap is a skill published in the GitHub repository QoderAI/better-harness (2,408 stars, last pushed 5d ago), licensed MIT. It adds 47 tokens to every session and 1,971 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 2026-09-09.
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