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 jellydn/my-ai-tools --skill implementation-loggergit clone --depth 1 https://github.com/jellydn/my-ai-toolsWrote 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/jellydn/my-ai-tools/implementation-logger)<a href="https://agentmods.dev/skills/jellydn/my-ai-tools/implementation-logger"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/implementation-logger/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/jellydn/my-ai-tools/implementation-logger"><img src="https://agentmods.dev/badge/skills/jellydn/my-ai-tools/implementation-logger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 174 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium Rogue Agent · line 389 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00015 | $0.02788 |
| Opus 5.5 | $0.00006 | $0.01115 |
| Sonnet 5.5 | $0.00003 | $0.00558 |
| Haiku 4.5 | $0.00002 | $0.00279 |
Grade A, and why
implementation-logger 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 21d 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 — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementation Logger
When to Use
Use this skill during implementation when:
- Working on complex or uncertain changes
- The implementation approach isn't fully defined
- You want to track decisions for documentation
- Building knowledge about unknowns for future work
- Need to explain reasoning in PR descriptions
What It Does
Tracks deviations from the original plan and decision rationale during implementation. Helps identify where your mental model (map) differed from reality (territory).
How to Execute
Step 1: Set Up Logging
At the start of implementation, create a log file:
# Create implementation log
echo "# Implementation Log: [Feature Name]" > .implementation-log.md
echo "" >> .implementation-log.md
echo "Started: $(date)" >> .implementation-log.md
echo "" >> .implementation-log.md
echo "## Original Plan" >> .implementation-log.md
echo "[Brief summary of approach]" >> .implementation-log.md
echo "" >> .implementation-log.md
echo "## Deviations & Decisions" >> .implementation-log.md
Step 2: Log During Implementation
Whenever reality differs from plan, log it:
### [Timestamp] - [Decision Point Title]
**Context**: What I encountered that wasn't in the plan
**Original Assumption**: What I thought would work
**Reality**: What I actually found
**Decision**: What I decided to do instead
**Rationale**: Why this approach is better/necessary
**Impact**: What else this might affect
Step 3: Log Categories
Track different types of deviations:
Architectural Discoveries:
- Found existing abstraction that changes approach
- Realized need for new pattern
- Dependency constraints
Unknown Unknowns:
- Edge cases not in spec
- Integration points discovered
- Performance considerations
Technical Constraints:
- Library limitations
- Type system issues
- Test infrastructure gaps
Spec Gaps:
- Ambiguous requirements
- Missing error handling specs
- Unclear business logic
Step 4: Review & Extract
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.
- 21d ago Changed · +2 lines 743175533608
- 1mo ago First seen · 403 lines · 15 tokens per session scan A d8351690fcab
implementation-logger is a skill published in the GitHub repository jellydn/my-ai-tools (124 stars, last pushed today), licensed MIT. It adds 15 tokens to every session and 2,788 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
ask-first
A decision guide for handling unclear requests before taking action. It uses one focused question or probe when the requested goal, context, or boundaries are not clear.
insight-miner
A manually triggered writing guide that turns notes, debugging records, design summaries, code snippets, or rough drafts into Chinese technical blog posts. It first identifies and evaluates possible article topics before writing.
book-recommender
A Chinese-language book recommendation and analysis skill that evaluates what books are about, whether they are worth reading, and how to read them.
notes-to-blog
A manually triggered guide for turning technical notes and rough material into a Chinese programming blog post. It first extracts and evaluates the main transferable idea, then writes the article after the user confirms its direction.
wisdom-decoder
A guided explanation skill for ideas from Buddhism, philosophy, and psychology. It presents a chosen topic in everyday language and connects it to practical actions, sources, and perspectives from other fields.
teach-me
A five-step teaching workflow for introducing an unfamiliar topic and guiding the learner toward applying it. It moves from an example and explanation to supported practice and an independent task.