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 t0ddharris/claude-code-skills --skill reflectgit clone --depth 1 https://github.com/t0ddharris/claude-code-skillsWrote 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/t0ddharris/claude-code-skills/reflect)<a href="https://agentmods.dev/skills/t0ddharris/claude-code-skills/reflect"><img src="https://agentmods.dev/badge/skills/t0ddharris/claude-code-skills/reflect/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/t0ddharris/claude-code-skills/reflect"><img src="https://agentmods.dev/badge/skills/t0ddharris/claude-code-skills/reflect.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 | $0.00063 | $0.01622 |
| Opus 5.5 | $0.00025 | $0.00649 |
| Sonnet 5.5 | $0.00013 | $0.00324 |
| Haiku 4.5 | $0.00006 | $0.00162 |
Grade A, and why
reflect 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect: Self-Improving Skills
Scan the current conversation for signals (corrections, approvals, patterns) and propose updates to the skill files that were invoked during the session. Every correction should only need to happen once.
Step 1: Identify Skills Used
Scan the conversation for skill invocations. Look for:
- Explicit
/skill-namecalls - Skill tool invocations (the Skill tool being called)
- Agent delegations that loaded a skill
If no skills were used this session, check whether the corrections apply to:
- General behavior → suggest a CLAUDE.md or memory update instead
- A specific skill that should have been used → note the routing gap
- Neither → report "no learnings to extract" and stop
Step 2: Extract Signals
Scan the full conversation for three signal types:
HIGH confidence (explicit corrections):
- User said "no," "don't," "never," "stop," "wrong," "not that"
- User rejected output and asked for a different approach
- User provided a specific rule ("always do X," "use Y not Z")
- User flagged an error, bad pattern, or bad output
MEDIUM confidence (validated patterns):
- User approved output without pushback ("yes," "perfect," "looks good," accepted without changes)
- A non-obvious approach worked and the user confirmed it
- User's positive reaction to a specific technique or format
LOW confidence (observations):
- Patterns that seemed to work but weren't explicitly validated
- Ambiguous signals worth reviewing later
Not a signal, skip these:
- One-time contextual decisions (e.g., "make this one shorter" doesn't mean all future outputs should be shorter)
- Task-specific details that don't generalize beyond this session
- Things already documented in the skill file, CLAUDE.md, or memory
- Corrections to factual or biographical content (those belong in a dedicated facts/context file, not skills)
- Preferences already captured in the session brief's carried-forward sections
Step 3: Read the Target Skill Files
For each skill identified in Step 1, read its SKILL.md. Check:
- Does a
## Learningssection exist at the bottom? If not, it will be created. - Is this learning already captured? Don't duplicate.
- Does this learning contradict an existing learning or rule? Flag the conflict for the user to resolve.
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 · 162 lines · 63 tokens per session scan A 187b03333664
reflect is a skill published in the GitHub repository t0ddharris/claude-code-skills (2 stars, last pushed 3mo ago), licensed MIT. It adds 63 tokens to every session and 1,622 once invoked, about $0.0003 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 2026-08-31.
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