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 shashankreddy509/claude-tdd-kit --skill merge-feedbackgit clone --depth 1 https://github.com/shashankreddy509/claude-tdd-kitWrote 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/shashankreddy509/claude-tdd-kit/merge-feedback)<a href="https://agentmods.dev/skills/shashankreddy509/claude-tdd-kit/merge-feedback"><img src="https://agentmods.dev/badge/skills/shashankreddy509/claude-tdd-kit/merge-feedback/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/shashankreddy509/claude-tdd-kit/merge-feedback"><img src="https://agentmods.dev/badge/skills/shashankreddy509/claude-tdd-kit/merge-feedback.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 | $0.01347 |
| Opus 5 | $0.00037 | $0.00674 |
| Sonnet 5 | $0.00015 | $0.00269 |
| Haiku 4.5 | $0.00007 | $0.00135 |
Grade A, and why
merge-feedback 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 10d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Merge Feedback
One job: take what this session revealed about how the user wants to work and merge it into
tasks/feedback.md as a new dated section, preserving everything already there. This is a Utility —
the feedback artifact, every time. It does NOT write session-notes or the memory vault (end-session
owns those and calls this only for the feedback piece).
Steps
-
Read the existing
tasks/feedback.mdif it exists. You must see the current content before writing — the merge rule depends on it. -
Synthesize this session's observations into the five sections below. Scan the FULL conversation, not just recent turns. Be specific — paraphrase the actual correction/preference, not vague generalities. Keep each bullet ≤ 20 words. No code snippets or implementation details — behavioral/communication patterns only. (A genuinely technical, non-behavioral gotcha belongs in the memory vault, which end-session handles — don't force it into feedback.md.)
-
Write a new dated section at the TOP of the file (most recent first), using today's date:
# Session Feedback — {YYYY-MM-DD}
## Preferences
- [concise bullet per preference observed]
## Corrections I Made
- [exact correction → what to do instead]
## What Worked Well
- [approaches the user accepted without pushback or praised]
## What to Avoid
- [behaviors the user pushed back on or corrected]
## System Instructions for Future Sessions
- [imperative rule a future session can follow immediately]
- ...
-
Patch the skill that was running. A correction recorded only as prose in
feedback.mddoes not reach the skill that produced the mistake — the next invocation repeats it. So for each correction captured in step 2, ask: was a named skill driving when this happened? If yes, also append the lesson as one line to a## Gotchassection in THAT skill's ownSKILL.md(create the section at the end of the file if absent).Write the gotcha as an imperative rule with its trigger, not a story: "Before X, check Y — Z fails silently otherwise." One line each. Skip this step entirely when no skill was driving; a correction from ordinary conversation belongs in
feedback.mdonly.
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.
- 10d ago First seen · 102 lines · 0 tokens per session scan A cf3abfa5d049
merge-feedback is a skill published in the GitHub repository shashankreddy509/claude-tdd-kit (2 stars, last pushed 18d ago), licensed MIT. It adds 74 tokens to every session and 1,347 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 2026-08-31.
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