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    Agent Skill Fine-Tuner

    1

    Audits and adapts any agent skill for compatibility with your agent's actual tools, permissions, and environment, and documents every change.

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    Agent Skill Fine-Tuner

    Agent Skill Fine-Tuner

    Example session with this skill installed

    I bought this skill for my team's market digest. My agent can read and write files, search the web through a connector, and send email through a connector. It has no scheduler, it cannot open a browser, and it has no Slack connection. Fine-tune it:


    name: daily-market-digest
    description: Every morning, build and distribute a short market digest for the team.

    Daily Market Digest

    At 8:00 AM each day:

    1. Use web_fetch to open https://markets.example-source.com/headlines and read the top 10 headlines.
    2. For each company mentioned, call stock_price(company) and record the current price.
    3. Summarize each headline in two sentences.
    4. Write the digest to /var/digest/today.md.
    5. Use send_email(to="team@company.com") to send the full digest.
    6. Use slack_post(channel="#markets") to post a three-line summary.
    7. Log completion to the console.

    Requires: cron scheduling, web fetching, stock price API, email, Slack.

    • Read your context and instructions
    • Compiled the agent fine-tuner

    Fine-tune complete. The daily-market-digest skill carries 7 requirements: 1 is compatible as written, 4 adapt through substitution, and 2 are blocking and reported below. The revised skill is delivered as daily-market-digest-tuned.md, with the full audit in fine-tune-report.md.

    1. Original composition

    • Tools: web_fetch, stock_price, send_email, slack_post, console logging
    • Permissions: network access, file write
    • Connections: stock price API, email service, Slack
    • Knowledge: one headline source URL
    • Runtime: cron schedule at 8:00 AM daily; fixed output path /var/digest/today.md
    • Model demands: standard two-sentence summarization
    • Workflow: autonomous unattended run, delivery to team email and a Slack channel

    2. Agent profile (stated by you, checked against my runtime)

    • Tools: file read, file write, web search connector, email connector
    • Execution model: runs when invoked; no scheduler
    • Absent: browser control, terminal, Slack connection

    3. Compatibility matrix

    #RequirementSkill expectsAgent hasVerdict
    1Headline fetchweb_fetch opens a fixed URLsearch connector with page textAdaptable
    2Stock pricesstock_price APIsearch lookup, dated resultsAdaptable
    3Digest file write/var/digest/today.mdfile write; fixed path unverifiedAdaptable
    4Email deliverysend_email to teamemail connectorCompatible
    5Slack postingslack_post to #marketsno Slack connectorBlocking
    68:00 AM schedulingcron dailyno scheduler; on requestBlocking
    7Console logterminal outputno terminalAdaptable

    4. Revisions made

    • Tool substitution: the headline fetch becomes a search connector query plus opening the source page when reachable; when the page cannot be opened, the digest builds from search snippets and says so in its header.
    • Tool substitution with dating: stock_price becomes a search lookup; each price is labeled with its retrieval date, since search results can lag live quotes.
    • Environment normalization: the digest writes to ./daily-market-digest/today.md, created on first run; the original /var/digest/ path is unverified on your setup.
    • Output generalization: console logging becomes a completion note in the chat reply, including which retrieval path ran.
    • Blocking (Slack): the three-line summary now rides in the email body so the team still gets the short version. The requirement stays in the skill notes; any messaging connector or webhook would unblock it fully.
    • Blocking (schedule): the skill runs on request. A platform-level scheduled task invoking the agent at 8:00 AM restores the original cadence.

    5. Blocking findings

    • Slack posting: unblocked by a Slack connector, webhook, or any messaging integration.
    • 8:00 AM scheduling: unblocked by a platform scheduler that invokes the agent daily.

    Revised skill (daily-market-digest-tuned.md):

    ---
    name: daily-market-digest
    description: Build and distribute a short market digest for the team. Runs on request; a platform scheduler can invoke the agent daily at 8:00 AM to keep the original cadence.
    ---
    
    # Daily Market Digest
    
    1. Retrieve the day's top market headlines: query the search connector for the source's latest market headlines and open the source page when reachable. When the page cannot be opened, build the digest from the search snippets and note the substitution in the digest header.
    2. For each company mentioned, look up the current share price through the search connector. Label each price with its retrieval date, since search results can lag live quotes.
    3. Summarize each headline in two sentences.
    4. Write the digest to ./daily-market-digest/today.md, creating the folder on first run.
    5. Send the full digest with the email connector to team@company.com, appending the three-line summary to the email body so the team receives the short version in the same message.
    6. Report completion in the chat reply, including which retrieval path ran.
    
    Notes: the original skill posted the three-line summary to #markets on Slack; this agent has no Slack connector, so the summary rides in the email until one is added. The original 8:00 AM cron cadence needs a platform-level scheduled task that invokes this agent daily.
    

    Next steps: install the tuned skill and run it once on request to check the output shape; set a platform scheduled task at 8:00 AM if you want the daily cadence back; a Slack connector would fully unblock the channel posting.

    One question before we finish: would you like this skill tuned further to your specific needs?

    The revised skill is delivered as daily-market-digest-tuned.md

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Every skill written for one agent carries that agent's assumptions: the tools it calls, the permissions it needs, the connections it expects, the environment it was designed for. Installed on a different agent, those assumptions break quietly, and the skill fails at run time on a requirement nobody wrote down.

    Agent Skill Fine-Tuner closes that gap. Point your agent at any skill and it will:

    • Inventory the skill's full requirements: tools, permissions, connections, knowledge, runtime assumptions, and model capability demands, including the ones the skill never states outright
    • Profile the running agent: actual tools, granted permissions, connections, and execution model, verified against your setup
    • Grade every requirement: Compatible, Adaptable, or Blocking, in a one-row-per-requirement matrix
    • Revise where needed: rewrite the skill so it runs on your agent while preserving the author's intent, using documented substitution and fallback patterns
    • Document everything: a fine-tune report covering the original composition, the gap analysis, and every revision made, plus the revised skill as a ready-to-use file

    After the audit, the skill asks whether you want the tuning taken further to your own needs, and if so, whether it should derive those needs from your previous conversations and stored knowledge or take the context from you directly.

    What buyers use it for

    • Porting skills between agents, harnesses, and environments
    • Diagnosing an installed skill that fails or misbehaves
    • Vetting third-party skills before installing them
    • Narrowing a skill's permission footprint to what its work actually needs

    The package ships with three reference files that make the analysis repeatable: a requirements taxonomy (seven categories, explicit and assumed), a compatibility matrix template with verdict rules, and a catalog of revision patterns for tool substitution, step decomposition, fallbacks, environment normalization, output generalization, and permission narrowing.

    Runs entirely inside your agent. It reads the skill, reasons, and writes the report and revised files: no network, no terminal, no external services.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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