Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add pitimon/8-habit-ai-dev/plugin install 8-habit-ai-devWrote 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/pitimon/8-habit-ai-dev/reflect)<a href="https://agentmods.dev/skills/pitimon/8-habit-ai-dev/reflect"><img src="https://agentmods.dev/badge/skills/pitimon/8-habit-ai-dev/reflect.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 | $0.00063 | $0.02127 |
| Opus 5 | $0.00032 | $0.01064 |
| Sonnet 5 | $0.00013 | $0.00425 |
| Haiku 4.5 | $0.00006 | $0.00213 |
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 5d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reflect (ทบทวน)
Habit: H7 — Sharpen the Saw | Anti-pattern: All output, no capability improvement — repeating the same mistakes
Why This Exists
DORA research shows per-task micro-retros (3-5 questions, 5 minutes) are more effective than monthly hour-long retrospectives. The key differentiator: teams that assign action items with owners improve; teams that just "discuss" don't. Q6 below extends the DORA pattern with a skill-effectiveness signal specific to this plugin (H7 applied to the plugin itself, per Issue #92).
Process
Ask these 6 questions. Keep answers brief — this should take no more than 5 minutes.
1. What went well?
Reinforce good practices. What should we keep doing?
2. What surprised me?
Surface hidden complexity. What did we not expect? Sub-prompt: did a prior diagnosis or shipped fix prove wrong this session — what evidence reversed it? A root cause that was confident-but-wrong is a high-value surprise to capture; this signal feeds SKILL-EFFECTIVENESS.md. See independent-source-verification.md.
3. What would I do differently?
Improve next iteration. If starting over, what would change?
4. What reusable pattern did I discover?
Extract for the team. Is there a script, template, or approach worth sharing?
Sub-prompt: did the AI workflow itself reveal a reusable cost/health pattern — repeated loops, retries without new evidence, context compaction that needed verification, missing audit trail, or next-session recovery gaps?
5. Action item
One specific, assigned action with a deadline. Not "we should improve testing" but "create a test template for API endpoints by Friday." If AI-work cost was unhealthy, make the action concrete: add a handoff note, add a validator, shorten a prompt, or preserve a recovery artifact. Do not add runtime budget enforcement in this plugin.
6. Skill effectiveness signal
Capture three fields:
most_useful: Which 8-habit skill was most useful this session?least_or_confusing: Which skill was least useful or confusing?missed_skill: Which skill should have been invoked but was missed until later?
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.
- 5d ago First seen · 144 lines · 63 tokens per session scan A 3b5d6baad6ed
reflect is a skill published in the GitHub repository pitimon/8-habit-ai-dev (3 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 2,127 once invoked, about $0.0003 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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